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2025-05-15 00:00:00
|
|---|---|---|---|---|---|---|---|---|
Addis S. Fuhr, Panchapakesan Ganesh, Rama K. Vasudevan, Bobby G.
Sumpter
|
Bridging Theory with Experiment: Digital Twins and Deep Learning
Segmentation of Defects in Monolayer MX2 Phases
| null | null | null |
cond-mat.mtrl-sci
|
Developing methods to understand and control defect formation in
nanomaterials offers a promising route for materials discovery. Monolayer MX2
phases represent a particularly compelling case for defect engineering of
nanomaterials due to the large variability in their physical properties as
different defects are introduced into their structure. However, effective
identification and quantification of defects remains a challenge even as
high-throughput scanning tunneling electron microscopy (STEM) methods improve.
This study highlights the benefits of employing first principles calculations
to produce digital twins for training deep learning segmentation models for
defect identification in monolayer MX2 phases. Around 600 defect structures
were obtained using density functional theory calculations, with each monolayer
MX2 structure being subjected to multislice simulations for the purpose of
generating the digital twins. Several deep learning segmentation architectures
were trained on this dataset, and their performances evaluated under a variety
of conditions such as recognizing defects in the presence of unidentified
impurities, beam damage, grain boundaries, and with reduced image quality from
low electron doses. This digital twin approach allows benchmarking different
deep learning architectures on a theory dataset, which enables the study of
defect classification under a broad array of finely controlled conditions. It
thus opens the door to resolving the underpinning physical reasons for model
shortcomings, and potentially chart paths forward for automated discovery of
materials defect phases in experiments.
|
[{'version': 'v1', 'created': 'Thu, 4 May 2023 15:20:59 GMT'}]
|
2023-05-05
|
Miguel Herranz, Clara Pedrosa, Daniel Martinez-Fernandez, Katerina
Foteinopoulou, Nikos Ch. Karayiannis and Manuel Laso
|
Fine-Tuning of Colloidal Polymer Crystals by Molecular Simulation
|
Physical Review E 107, 064605 (2023)
|
10.1103/PhysRevE.107.064605
| null |
cond-mat.soft cond-mat.mtrl-sci physics.chem-ph
|
Through extensive molecular simulations we determine a phase diagram of
attractive, flexible polymer chains in two and three dimensions. A surprisingly
rich collection of distinct crystal morphologies appear, which can be finely
tuned through the range of attraction. In three dimensions these include the
face centered cubic, hexagonal close packed, simple hexagonal and body centered
cubic crystals and the Frank-Kasper phase. A simple geometric model is
proposed, based on the concept of cumulative neighbours of ideal crystals,
which can accurately predict most of the observed structures and the
corresponding transitions. The attraction range can thus be considered as an
adjustable parameter for the design of colloidal polymer crystals with tailored
morphologies.
|
[{'version': 'v1', 'created': 'Mon, 8 May 2023 17:40:57 GMT'}]
|
2023-06-23
|
N.M. Chtchelkatchev, R.E. Ryltsev, M.V. Magnitskaya, S.M. Gorbunov,
K.A. Cherednichenko, V.L. Solozhenko, and V.V. Brazhkin
|
Local structure, thermodynamics, and melting curve of boron phosphide at
high pressures by deep learning-driven ab initio simulations
|
Journal of Chemical Physics, 159 [6] 064507 (2023)
|
10.1063/5.0165948
| null |
cond-mat.mtrl-sci physics.chem-ph physics.comp-ph
|
Boron phosphide (BP) is a (super)hard semiconductor constituted of light
elements, which is promising for high demand applications at extreme
conditions. The behavior of BP at high temperatures and pressures is of special
interest but is also poorly understood because both experimental and
conventional ab initio methods are restricted to studying refractory covalent
materials. The use of machine learning interatomic potentials is a
revolutionary trend that gives a unique opportunity for high-temperature study
of materials with ab initio accuracy. We develop a deep machine learning
potential (DP) for accurate atomistic simulations of solid and liquid phases of
BP as well as their transformations near the melting line. Our DP provides
quantitative agreement with experimental and ab initio molecular dynamics data
for structural and dynamic properties. DP-based simulations reveal that at
ambient pressure tetrahedrally bonded cubic BP crystal melts into an open
structure consisting of two interpenetrating sub-networks of boron and
phosphorous with different structures. Structure transformations of BP melts
under compressing are reflected by the evolution of low-pressure tetrahedral
coordination to high-pressure octahedral coordination. The main contributions
to structural changes at low pressures are made by the evolution of
medium-range order in B-subnetwork and at high pressures by the change of
short-range order in P-sub-network. Such transformations exhibit an anomalous
behavior of structural characteristics in the range of 12--15 GPa. Analysis of
the results obtained raise open issues in developing machine learning
potentials for covalent materials and stimulate further experimental and
theoretical studies of melting behavior in BP.
|
[{'version': 'v1', 'created': 'Thu, 11 May 2023 17:08:50 GMT'}]
|
2023-08-15
|
Shoieb Ahmed Chowdhury, M.F.N. Taufique, Jing Wang, Marissa Masden,
Madison Wenzlick, Ram Devanathan, Alan L Schemer-Kohrn, Keerti S Kappagantula
|
Automated Grain Boundary (GB) Segmentation and Microstructural Analysis
in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy
| null | null | null |
cond-mat.mtrl-sci cs.CV eess.IV
|
Austenitic 347H stainless steel offers superior mechanical properties and
corrosion resistance required for extreme operating conditions such as high
temperature. The change in microstructure due to composition and process
variations is expected to impact material properties. Identifying
microstructural features such as grain boundaries thus becomes an important
task in the process-microstructure-properties loop. Applying convolutional
neural network (CNN) based deep-learning models is a powerful technique to
detect features from material micrographs in an automated manner. Manual
labeling of the images for the segmentation task poses a major bottleneck for
generating training data and labels in a reliable and reproducible way within a
reasonable timeframe. In this study, we attempt to overcome such limitations by
utilizing multi-modal microscopy to generate labels directly instead of manual
labeling. We combine scanning electron microscopy (SEM) images of 347H
stainless steel as training data and electron backscatter diffraction (EBSD)
micrographs as pixel-wise labels for grain boundary detection as a semantic
segmentation task. We demonstrate that despite producing instrumentation drift
during data collection between two modes of microscopy, this method performs
comparably to similar segmentation tasks that used manual labeling.
Additionally, we find that na\"ive pixel-wise segmentation results in small
gaps and missing boundaries in the predicted grain boundary map. By
incorporating topological information during model training, the connectivity
of the grain boundary network and segmentation performance is improved.
Finally, our approach is validated by accurate computation on downstream tasks
of predicting the underlying grain morphology distributions which are the
ultimate quantities of interest for microstructural characterization.
|
[{'version': 'v1', 'created': 'Fri, 12 May 2023 22:49:36 GMT'}]
|
2023-05-16
|
Yu Song, Santiago Miret, Bang Liu
|
MatSci-NLP: Evaluating Scientific Language Models on Materials Science
Language Tasks Using Text-to-Schema Modeling
| null | null | null |
cs.CL cond-mat.mtrl-sci cs.AI
|
We present MatSci-NLP, a natural language benchmark for evaluating the
performance of natural language processing (NLP) models on materials science
text. We construct the benchmark from publicly available materials science text
data to encompass seven different NLP tasks, including conventional NLP tasks
like named entity recognition and relation classification, as well as NLP tasks
specific to materials science, such as synthesis action retrieval which relates
to creating synthesis procedures for materials. We study various BERT-based
models pretrained on different scientific text corpora on MatSci-NLP to
understand the impact of pretraining strategies on understanding materials
science text. Given the scarcity of high-quality annotated data in the
materials science domain, we perform our fine-tuning experiments with limited
training data to encourage the generalize across MatSci-NLP tasks. Our
experiments in this low-resource training setting show that language models
pretrained on scientific text outperform BERT trained on general text. MatBERT,
a model pretrained specifically on materials science journals, generally
performs best for most tasks. Moreover, we propose a unified text-to-schema for
multitask learning on \benchmark and compare its performance with traditional
fine-tuning methods. In our analysis of different training methods, we find
that our proposed text-to-schema methods inspired by question-answering
consistently outperform single and multitask NLP fine-tuning methods. The code
and datasets are publicly available at
\url{https://github.com/BangLab-UdeM-Mila/NLP4MatSci-ACL23}.
|
[{'version': 'v1', 'created': 'Sun, 14 May 2023 22:01:24 GMT'}]
|
2023-05-16
|
Franco Pellegrini, Ruggero Lot, Yusuf Shaidu, Emine K\"u\c{c}\"ukbenli
|
PANNA 2.0: Efficient neural network interatomic potentials and new
architectures
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci cs.LG physics.chem-ph
|
We present the latest release of PANNA 2.0 (Properties from Artificial Neural
Network Architectures), a code for the generation of neural network interatomic
potentials based on local atomic descriptors and multilayer perceptrons. Built
on a new back end, this new release of PANNA features improved tools for
customizing and monitoring network training, better GPU support including a
fast descriptor calculator, new plugins for external codes and a new
architecture for the inclusion of long-range electrostatic interactions through
a variational charge equilibration scheme. We present an overview of the main
features of the new code, and several benchmarks comparing the accuracy of
PANNA models to the state of the art, on commonly used benchmarks as well as
richer datasets.
|
[{'version': 'v1', 'created': 'Fri, 19 May 2023 16:41:59 GMT'}]
|
2023-05-22
|
Sergey N. Pozdnyakov and Michele Ceriotti
|
Smooth, exact rotational symmetrization for deep learning on point
clouds
| null | null | null |
cs.CV cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Point clouds are versatile representations of 3D objects and have found
widespread application in science and engineering. Many successful
deep-learning models have been proposed that use them as input. The domain of
chemical and materials modeling is especially challenging because exact
compliance with physical constraints is highly desirable for a model to be
usable in practice. These constraints include smoothness and invariance with
respect to translations, rotations, and permutations of identical atoms. If
these requirements are not rigorously fulfilled, atomistic simulations might
lead to absurd outcomes even if the model has excellent accuracy. Consequently,
dedicated architectures, which achieve invariance by restricting their design
space, have been developed. General-purpose point-cloud models are more varied
but often disregard rotational symmetry. We propose a general symmetrization
method that adds rotational equivariance to any given model while preserving
all the other requirements. Our approach simplifies the development of better
atomic-scale machine-learning schemes by relaxing the constraints on the design
space and making it possible to incorporate ideas that proved effective in
other domains. We demonstrate this idea by introducing the Point Edge
Transformer (PET) architecture, which is not intrinsically equivariant but
achieves state-of-the-art performance on several benchmark datasets of
molecules and solids. A-posteriori application of our general protocol makes
PET exactly equivariant, with minimal changes to its accuracy.
|
[{'version': 'v1', 'created': 'Tue, 30 May 2023 15:26:43 GMT'}, {'version': 'v2', 'created': 'Tue, 12 Dec 2023 00:20:01 GMT'}, {'version': 'v3', 'created': 'Tue, 6 Feb 2024 13:14:35 GMT'}]
|
2024-02-07
|
Yong Liu, Hao Wang, Linxin Guo, Zhanfeng Yan, Jian Zheng, Wei Zhou,
and Jianming Xue
|
Deep learning inter-atomic potential for irradiation damage in 3C-SiC
| null | null | null |
cond-mat.mtrl-sci
|
We developed and validated an accurate inter-atomic potential for molecular
dynamics simulation in cubic silicon carbide (3C-SiC) using a deep learning
framework combined with smooth Ziegler-Biersack-Littmark (ZBL) screened nuclear
repulsion potential interpolation. Comparisons of multiple important properties
were made between the deep-learning potential and existing analytical
potentials which are most commonly used in molecular dynamics simulations of
3C-SiC. Not only for equilibrium properties but also for significant properties
of radiation damage such as defect formation energies and threshold
displacement energies, our deep-learning potential gave closer predictions to
DFT criterion than analytical potentials. The deep-learning potential framework
solved the long-standing dilemma that traditional empirical potentials
currently applied in 3C-SiC radiation damage simulations gave large disparities
with each other and were inconsistent with ab-initio calculations. A more
realistic depiction of the primary irradiation damage process in 3C-SiC can be
given and the accuracy of classical molecular dynamics simulation for cubic
silicon carbide can be expected to the level of quantum mechanics.
|
[{'version': 'v1', 'created': 'Wed, 31 May 2023 02:56:40 GMT'}]
|
2023-06-01
|
Koji Shimizu, Ryuji Otsuka, Masahiro Hara, Emi Minamitani, Satoshi
Watanabe
|
Prediction of Born effective charges using neural network to study ion
migration under electric fields: applications to crystalline and amorphous
Li$_3$PO$_4$
| null | null | null |
cond-mat.mtrl-sci
|
Understanding ionic behaviour under external electric fields is crucial to
develop electronic and energy-related devices using ion transport. In this
study, we propose a neural network (NN) model to predict the Born effective
charges of ions along an axis parallel to an applied electric field from atomic
structures. The proposed NN model is applied to Li$_3$PO$_4$ as a prototype.
The prediction error of the constructed NN model is 0.0376 $e$/atom. In
combination with an NN interatomic potential, molecular dynamics (MD)
simulations are performed under a uniform electric field of 0.1 V/angstrom,
whereby an enhanced mean square displacement of Li along the electric field is
obtained, which seems physically reasonable. In addition, the external forces
along the direction perpendicular to the electric field, originating from the
off-diagonal terms of the Born effective charges, are found to have a
nonnegligible effect on Li migration. Finally, additional MD simulations are
performed to examine the Li motion in an amorphous structure. The results
reveal that Li migration occurs in various areas despite the absence of
explicitly introduced defects, which may be attributed to the susceptibility of
the Li ions in the local minima to the electric field. We expect that the
proposed NN method can be applied to any ionic material, thereby leading to
atomic-scale elucidation of ion behaviour under electric fields.
|
[{'version': 'v1', 'created': 'Wed, 31 May 2023 04:24:01 GMT'}]
|
2023-06-01
|
Ali Riza Durmaz, Sai Teja Potu, Daniel Romich, Johannes M\"oller, Ralf
N\"utzel
|
Microstructure quality control of steels using deep learning
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
In quality control, microstructures are investigated rigorously to ensure
structural integrity, exclude the presence of critical volume defects, and
validate the formation of the target microstructure. For quenched,
hierarchically-structured steels, the morphology of the bainitic and
martensitic microstructures are of major concern to guarantee the reliability
of the material under service conditions. Therefore, industries conduct small
sample-size inspections of materials cross-sections through metallographers to
validate the needle morphology of such microstructures. We demonstrate
round-robin test results revealing that this visual grading is afflicted by
pronounced subjectivity despite the thorough training of personnel. Instead, we
propose a deep learning image classification approach that distinguishes steels
based on their microstructure type and classifies their needle length alluding
to the ISO 643 grain size assessment standard. This classification approach
facilitates the reliable, objective, and automated classification of
hierarchically structured steels. Specifically, an accuracy of 96% and roughly
91% is attained for the distinction of martensite/bainite subtypes and needle
length, respectively. This is achieved on an image dataset that contains
significant variance and labeling noise as it is acquired over more than ten
years from multiple plants, alloys, etchant applications, and light optical
microscopes by many metallographers (raters). Interpretability analysis gives
insights into the decision-making of these models and allows for estimating
their generalization capability.
|
[{'version': 'v1', 'created': 'Thu, 1 Jun 2023 15:25:53 GMT'}]
|
2023-06-02
|
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti
Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan Bringuier, L. Catherine
Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L.
Evans, Nicolas Gastellu, Jerome Genzling, Mar\'ia Victoria Gil, Ankur K.
Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub
L\'ala, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas
Moitessier, Elias Moubarak, Beatriz Mouri\~no, Brenden Pelkie, Michael
Pieler, Mayk Caldas Ramos, Bojana Rankovi\'c, Samuel G. Rodriques, Jacob N.
Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben
E. Smith, Joren Van Herck, Christoph V\"olker, Logan Ward, Sean Warren,
Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana
Scourtas, KJ Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
|
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A
Reflection on a Large Language Model Hackathon
| null |
10.1039/D3DD00113J
| null |
cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Large-language models (LLMs) such as GPT-4 caught the interest of many
scientists. Recent studies suggested that these models could be useful in
chemistry and materials science. To explore these possibilities, we organized a
hackathon.
This article chronicles the projects built as part of this hackathon.
Participants employed LLMs for various applications, including predicting
properties of molecules and materials, designing novel interfaces for tools,
extracting knowledge from unstructured data, and developing new educational
applications.
The diverse topics and the fact that working prototypes could be generated in
less than two days highlight that LLMs will profoundly impact the future of our
fields. The rich collection of ideas and projects also indicates that the
applications of LLMs are not limited to materials science and chemistry but
offer potential benefits to a wide range of scientific disciplines.
|
[{'version': 'v1', 'created': 'Fri, 9 Jun 2023 22:22:02 GMT'}, {'version': 'v2', 'created': 'Tue, 13 Jun 2023 07:44:32 GMT'}, {'version': 'v3', 'created': 'Thu, 13 Jul 2023 07:47:05 GMT'}, {'version': 'v4', 'created': 'Fri, 14 Jul 2023 13:24:43 GMT'}]
|
2023-11-23
|
Akash Singh, Yumeng Li
|
Reliable machine learning potentials based on artificial neural network
for graphene
|
Computational Materials Science Volume 227, August 2023, 112272
|
10.1016/j.commatsci.2023.112272
| null |
physics.comp-ph cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.stat-mech cs.LG
|
Graphene is one of the most researched two dimensional (2D) material due to
its unique combination of mechanical, thermal and electrical properties.
Special 2D structure of graphene enables it to exhibit a wide range of peculiar
material properties like high Young's modulus, high specific strength etc.
which are critical for myriad of applications including light weight structural
materials, multi-functional coating and flexible electronics. It is quite
challenging and costly to experimentally investigate graphene/graphene based
nanocomposites, computational simulations such as molecular dynamics (MD)
simulations are widely adopted for understanding the microscopic origins of
their unique properties. However, disparate results were reported from
computational studies, especially MD simulations using various empirical
inter-atomic potentials. In this work, an artificial neural network based
interatomic potential has been developed for graphene to represent the
potential energy surface based on first principle calculations. The developed
machine learning potential (MLP) facilitates high fidelity MD simulations to
approach the accuracy of ab initio methods but with a fraction of computational
cost, which allows larger simulation size/length, and thereby enables
accelerated discovery/design of graphene-based novel materials. Lattice
parameter, coefficient of thermal expansion (CTE), Young's modulus and yield
strength are estimated using machine learning accelerated MD simulations
(MLMD), which are compared to experimental/first principle calculations from
previous literatures. It is demonstrated that MLMD can capture the dominating
mechanism governing CTE of graphene, including effects from lattice parameter
and out of plane rippling.
|
[{'version': 'v1', 'created': 'Mon, 12 Jun 2023 17:12:08 GMT'}]
|
2023-06-13
|
Or Shafir and Ilya Grinberg
|
Bonding-aware Materials Representation for Deep Learning Atomistic
Models
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Deep potentials for molecular dynamics (MD) achieve first-principles accuracy
at much lower computational cost. However, their use in large length- and
time-scale simulations is limited by their lower speeds compared to analytical
atomistic potentials, primarily due to network complexity and long embedding
time. Here, based on the moments theorem, we develop a chemical-bonding-aware
embedding for neural network potentials that achieve state-of-the-art accuracy
in forces and local electronic density of states prediction with an ultrasmall
16x32 neural network resulting in significantly lower computational cost.
|
[{'version': 'v1', 'created': 'Wed, 14 Jun 2023 06:45:41 GMT'}, {'version': 'v2', 'created': 'Fri, 16 Jun 2023 12:20:18 GMT'}]
|
2023-06-19
|
B.N. Galimzyanov, M.A. Doronina, A.V. Mokshin
|
Neural network as a tool for design of amorphous metal alloys with
desired elastoplastic properties
| null |
10.3390/met13040812
| null |
cond-mat.mtrl-sci
|
The development and implementation of the methods for designing amorphous
metal alloys with desired mechanical properties is one of the most promising
areas of modern materials science. Here, the machine learning methods appear to
be a suitable complement to empirical methods related to the synthesis and
testing of amorphous alloys of various compositions. In the present work, it is
proposed a method to determine amorphous metal alloys with mechanical
properties closest to those required. More than $50\,000$ amorphous alloys of
different compositions have been considered, and the Young's modulus $E$ and
the yield strength $\sigma_{y}$ have been evaluated for them by the machine
learning model trained on the fundamental physical properties of the chemical
elements. Statistical treatment of the obtained results reveals that the
fundamental physical properties of the chemical element with the largest mass
fraction are the most significant factors, whose values correlate with the
values of the mechanical properties of the alloys, in which this element is
involved. It is shown that the values of the Young's modulus $E$ and the yield
strength $\sigma_{y}$ are higher for amorphous alloys based on Cr, Fe, Co, Ni,
Nb, Mo and W formed by the addition of semimetals (e.g. Be, B, Al, Sn),
nonmetals (e.g. Si and P) and lanthanides (e.g. La and Gd) than for alloys of
other compositions. Increasing the number of components in alloy from $2$ to
$7$ and changing the mass fraction of chemical elements has no significantly
impact on the strength characteristics $E$ and $\sigma_{y}$. Amorphous metal
alloys with the most improved mechanical properties have been identified. In
particular, such extremely high-strength alloys include Cr$_{80}$B$_{20}$
(among binary), Mo$_{60}$B$_{20}$W$_{20}$ (among ternary) and
Cr$_{40}$B$_{20}$Nb$_{10}$Pd$_{10}$Ta$_{10}$Si$_{10}$ (among multicomponent).
|
[{'version': 'v1', 'created': 'Wed, 14 Jun 2023 09:18:27 GMT'}]
|
2023-06-16
|
Zhiling Zheng, Oufan Zhang, Christian Borgs, Jennifer T. Chayes, Omar
M. Yaghi
|
ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF
Synthesis
|
J. Am. Chem. Soc. 2023, 145, 32, 18048-18062
|
10.1021/jacs.3c05819
| null |
cs.IR cond-mat.mtrl-sci cs.CL physics.chem-ph
|
We use prompt engineering to guide ChatGPT in the automation of text mining
of metal-organic frameworks (MOFs) synthesis conditions from diverse formats
and styles of the scientific literature. This effectively mitigates ChatGPT's
tendency to hallucinate information -- an issue that previously made the use of
Large Language Models (LLMs) in scientific fields challenging. Our approach
involves the development of a workflow implementing three different processes
for text mining, programmed by ChatGPT itself. All of them enable parsing,
searching, filtering, classification, summarization, and data unification with
different tradeoffs between labor, speed, and accuracy. We deploy this system
to extract 26,257 distinct synthesis parameters pertaining to approximately 800
MOFs sourced from peer-reviewed research articles. This process incorporates
our ChemPrompt Engineering strategy to instruct ChatGPT in text mining,
resulting in impressive precision, recall, and F1 scores of 90-99%.
Furthermore, with the dataset built by text mining, we constructed a
machine-learning model with over 86% accuracy in predicting MOF experimental
crystallization outcomes and preliminarily identifying important factors in MOF
crystallization. We also developed a reliable data-grounded MOF chatbot to
answer questions on chemical reactions and synthesis procedures. Given that the
process of using ChatGPT reliably mines and tabulates diverse MOF synthesis
information in a unified format, while using only narrative language requiring
no coding expertise, we anticipate that our ChatGPT Chemistry Assistant will be
very useful across various other chemistry sub-disciplines.
|
[{'version': 'v1', 'created': 'Tue, 20 Jun 2023 05:20:29 GMT'}, {'version': 'v2', 'created': 'Thu, 20 Jul 2023 02:20:35 GMT'}]
|
2023-10-04
|
Xiang Zhang, Zichun Zhou, Chen Ming, Yi-Yang Sun
|
GPT-assisted learning of structure-property relationships by graph
neural networks: Application to rare-earth doped phosphors
| null |
10.1021/acs.jpclett.3c02848
| null |
cond-mat.mtrl-sci
|
Applications of machine learning techniques in materials science are often
based on two key ingredients, a set of empirical descriptors and a database of
a particular material property of interest. The advent of graph neural
networks, such as the Crystal Graph Convolutional Neural Network (CGCNN),
demonstrates the possibility of directly mapping the relationship between
material structures and properties without employing empirical descriptors.
Another exciting recent advancement is in large language models such as
OpenAI's GPT-4, which demonstrates competency at reading comprehension tasks
and holds great promise for accelerating the acquisition of databases on
material properties. Here, we utilize the combination of GPT-4 and CGCNN to
develop rare-earth doped phosphors for solid-state lighting. GPT-4 is applied
to data-mine chemical formulas and emission wavelengths of 264 Eu(II)-doped
phosphors from 274 papers. A CGCNN model is trained on the acquired dataset,
achieving a test $R^2$ of 0.77. The model is then used to screen over 40,000
inorganic materials to make predictions on the emission wavelengths. We also
demonstrate the possibility of leveraging transfer learning to fine-tune a
bandgap-predicting CGCNN model towards the prediction of phosphor emission
wavelengths. The workflow requires minimal human supervision, little domain
knowledge about phosphors, and is generalizable to other material properties.
|
[{'version': 'v1', 'created': 'Sun, 25 Jun 2023 13:17:44 GMT'}, {'version': 'v2', 'created': 'Tue, 5 Dec 2023 08:00:00 GMT'}]
|
2023-12-18
|
Zhiling Zheng, Zichao Rong, Nakul Rampal, Christian Borgs, Jennifer T.
Chayes, Omar M. Yaghi
|
A GPT-4 Reticular Chemist for Guiding MOF Discovery
|
Angew. Chem. Int. Ed. 2023, e202311983
|
10.1002/anie.202311983
| null |
cs.AI cond-mat.mtrl-sci physics.chem-ph
|
We present a new framework integrating the AI model GPT-4 into the iterative
process of reticular chemistry experimentation, leveraging a cooperative
workflow of interaction between AI and a human researcher. This GPT-4 Reticular
Chemist is an integrated system composed of three phases. Each of these
utilizes GPT-4 in various capacities, wherein GPT-4 provides detailed
instructions for chemical experimentation and the human provides feedback on
the experimental outcomes, including both success and failures, for the
in-context learning of AI in the next iteration. This iterative human-AI
interaction enabled GPT-4 to learn from the outcomes, much like an experienced
chemist, by a prompt-learning strategy. Importantly, the system is based on
natural language for both development and operation, eliminating the need for
coding skills, and thus, make it accessible to all chemists. Our collaboration
with GPT-4 Reticular Chemist guided the discovery of an isoreticular series of
MOFs, with each synthesis fine-tuned through iterative feedback and expert
suggestions. This workflow presents a potential for broader applications in
scientific research by harnessing the capability of large language models like
GPT-4 to enhance the feasibility and efficiency of research activities.
|
[{'version': 'v1', 'created': 'Tue, 20 Jun 2023 05:26:44 GMT'}, {'version': 'v2', 'created': 'Wed, 4 Oct 2023 01:38:47 GMT'}]
|
2023-11-01
|
Andreas Erlebach, Martin \v{S}\'ipka, Indranil Saha, Petr Nachtigall,
Christopher J. Heard, Luk\'a\v{s} Grajciar
|
A reactive neural network framework for water-loaded acidic zeolites
| null |
10.1038/s41467-024-48609-2
| null |
cond-mat.mtrl-sci
|
Under operating conditions, the dynamics of water and ions confined within
protonic aluminosilicate zeolite micropores are responsible for many of their
properties, including hydrothermal stability, acidity and catalytic activity.
However, due to high computational cost, operando studies of acidic zeolites
are currently rare and limited to specific cases and simplified models. In this
work, we have developed a general reactive neural network potential (NNP)
attempting to cover the entire class of acidic zeolites, including the full
range of experimentally relevant water concentrations and Si/Al ratios. This
NNP combines dramatic sampling acceleration, retaining the reference metaGGA
DFT level, with the capacity for discovery of new chemistry, such as collective
defect formation mechanisms at the zeolite surface. Furthermore, we exemplify
how the NNP can be used as a basis for further extensions/improvements which
include data-efficient adoption of higher-level (hybrid) references via
$\Delta$-learning and the acceleration of rare event sampling via automatic
construction of collective variables. These developments represent a
significant step towards accurate simulations of realistic catalysts under
operando conditions.
|
[{'version': 'v1', 'created': 'Mon, 3 Jul 2023 10:15:15 GMT'}, {'version': 'v2', 'created': 'Tue, 4 Jul 2023 08:49:15 GMT'}, {'version': 'v3', 'created': 'Thu, 13 Jul 2023 08:27:53 GMT'}, {'version': 'v4', 'created': 'Mon, 18 Dec 2023 08:36:07 GMT'}]
|
2024-05-21
|
Rui Zu, Bo Wang, Jingyang He, Lincoln Weber, Akash Saha, Long-Qing
Chen, Venkatraman Gopalan
|
Optical Second Harmonic Generation in Anisotropic Multilayers with
Complete Multireflection of Linear and Nonlinear Waves using #SHAARP.ml
Package
| null | null | null |
physics.optics cond-mat.mtrl-sci
|
Optical second harmonic generation (SHG) is a nonlinear optical effect widely
used for nonlinear optical microscopy and laser frequency conversion.
Closed-form analytical solution of the nonlinear optical responses is essential
for evaluating the optical responses of new materials whose optical properties
are unknown a priori. A recent open-source code, SHAARP(si), can provide such
closed form solutions for crystals with arbitrary symmetries, orientations, and
anisotropic properties at a single interface. However, optical components are
often in the form of slabs, thin films on substrates, and multilayer
heterostructures with multiple reflections of both the fundamental and up to
ten different SHG waves at each interface, adding significant complexity. Many
approximations have therefore been employed in the existing analytical
approaches, such as slowly varying approximation, weak reflection of the
nonlinear polarization, transparent medium, high crystallographic symmetry,
Kleinman symmetry, easy crystal orientation along a high-symmetry direction,
phase matching conditions and negligible interference among nonlinear waves,
which may lead to large errors in the reported material properties. To avoid
these approximations, we have developed an open-source package named Second
Harmonic Analysis of Anisotropic Rotational Polarimetry in Multilayers
(SHAARP(ml)). The reliability and accuracy are established by experimentally
benchmarking with both the SHG polarimetry and Maker fringes predicted from the
package using standard materials.
|
[{'version': 'v1', 'created': 'Mon, 3 Jul 2023 21:51:07 GMT'}, {'version': 'v2', 'created': 'Thu, 6 Jul 2023 02:38:44 GMT'}, {'version': 'v3', 'created': 'Fri, 7 Jul 2023 04:29:37 GMT'}, {'version': 'v4', 'created': 'Thu, 21 Dec 2023 01:39:30 GMT'}]
|
2023-12-22
|
Paul Lafourcade and Jean-Bernard Maillet and Christophe Denoual and
El\'eonore Duval and Arnaud Allera and Alexandra M. Goryaeva and Mihai-Cosmin
Marinica
|
Robust crystal structure identification at extreme conditions using a
density-independent spectral descriptor and supervised learning
| null | null | null |
cond-mat.mtrl-sci
|
The increased time- and length-scale of classical molecular dynamics
simulations have led to raw data flows surpassing storage capacities,
necessitating on-the-fly integration of structural analysis algorithms. As a
result, algorithms must be computationally efficient, accurate, and stable at
finite temperature to reliably extract the relevant features of the data at
simulation time. In this work, we leverage spectral descriptors to encode local
atomic environments and build crystal structure classification models. In
addition to the classical way spectral descriptors are computed, i.e. over a
fixed radius neighborhood sphere around a central atom, we propose an extension
to make them independent from the material's density. Models are trained on
defect-free crystal structures with moderate thermal noise and elastic
deformation, using the linear discriminant analysis (LDA) method for
dimensionality reduction and logistic regression (LR) for subsequent
classification. The proposed classification model is intentionally designed to
be simple, incorporating only a limited number of parameters. This deliberate
simplicity enables the model to be trained effectively even when working with
small databases. Despite the limited training data, the model still
demonstrates inherent transferability, making it applicable to a broader range
of scenarios and datasets. The accuracy of our models in extreme conditions is
compared to traditional algorithms from the literature, namely adaptive common
neighbor analysis (a-CNA), polyhedral template matching (PTM) and diamond
structure identification (IDS). Finally, we showcase two applications of our
method: tracking a solid-solid BCC-to-HCP phase transformation in Zirconium at
high pressure up to high temperature, and visualizing stress-induced
dislocation loop expansion in single crystal FCC Aluminum containing a
Frank-Read source, at high temperature.
|
[{'version': 'v1', 'created': 'Tue, 4 Jul 2023 08:29:58 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Jul 2023 07:12:20 GMT'}]
|
2023-07-11
|
Xiang Li, Yubing Qian, and Ji Chen
|
Electric Polarization from Many-Body Neural Network Ansatz
| null |
10.1103/PhysRevLett.132.176401
| null |
physics.chem-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph
|
Ab initio calculation of dielectric response with high-accuracy electronic
structure methods is a long-standing problem, for which mean-field approaches
are widely used and electron correlations are mostly treated via approximated
functionals. Here we employ a neural network wavefunction ansatz combined with
quantum Monte Carlo to incorporate correlations into polarization calculations.
On a variety of systems, including isolated atoms, one-dimensional chains,
two-dimensional slabs, and three-dimensional cubes, the calculated results
outperform conventional density functional theory and are consistent with the
most accurate calculations and experimental data. Furthermore, we have studied
the out-of-plane dielectric constant of bilayer graphene using our method and
re-established its thickness dependence. Overall, this approach provides a
powerful tool to consider electron correlation in the modern theory of
polarization.
|
[{'version': 'v1', 'created': 'Wed, 5 Jul 2023 11:38:50 GMT'}, {'version': 'v2', 'created': 'Mon, 14 Aug 2023 02:36:32 GMT'}]
|
2024-06-25
|
Sung-Ho Lee, Jing Li, Valerio Olevano, Benoit Skl\'enard
|
Equivariant graph neural network interatomic potential for Green-Kubo
thermal conductivity in phase change materials
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph
|
Thermal conductivity is a fundamental material property that plays an
essential role in technology, but its accurate evaluation presents a challenge
for theory. In this work, we demonstrate the application of $E(3)$-equivariant
neutral network interatomic potentials within Green-Kubo formalism to determine
the lattice thermal conductivity in amorphous and crystalline materials. We
apply this method to study the thermal conductivity of germanium telluride
(GeTe) as a prototypical phase change material. A single deep learning
interatomic potential is able to describe the phase transitions between the
amorphous, rhombohedral and cubic phases, with critical temperatures in good
agreement with experiments. Furthermore, this approach accurately captures the
pronounced anharmonicity that is present in GeTe, enabling precise calculations
of the thermal conductivity. In contrast, the Boltzmann transport equation
including only three-phonon processes tends to overestimate the thermal
conductivity by approximately a factor of 2 in the crystalline phases.
|
[{'version': 'v1', 'created': 'Wed, 5 Jul 2023 14:37:34 GMT'}, {'version': 'v2', 'created': 'Fri, 15 Mar 2024 08:39:00 GMT'}]
|
2024-03-18
|
Jiewei Cheng, Tingwei Li, Yongyi Wang, Ahmed H. Ati and Qiang Sun
|
The relationship between activated H2 bond length and adsorption
distance on MXenes identified with graph neural network and resonating
valence bond theory
| null |
10.1063/5.0169430
|
The Journal of Chemical Physics 2023, 159, 191101
|
cond-mat.mtrl-sci
|
Motivated by the recent experimental study on hydrogen storage in MXene
multilayers [Nature Nanotechnol. 2021, 16, 331], for the first time we propose
a workflow to computationally screen 23,857 compounds of MXene to explore the
general relation between the activated H2 bond length and adsorption distance.
By using density functional theory (DFT), we generate a dataset to investigate
the adsorption geometries of hydrogen on MXenes, based on which we train
physics-informed atomistic line graph neural networks (ALIGNNs) to predict
adsorption parameters. To fit the results, we further derived a formula that
quantitatively reproduces the dependence of H2 bond length on the adsorption
distance from MXenes within the framework of Pauling's resonating valence bond
(RVB) theory, revealing the impact of transition metal's ligancy and valence on
activating dihydrogen in H2 storage.
|
[{'version': 'v1', 'created': 'Sat, 8 Jul 2023 05:43:11 GMT'}]
|
2024-01-17
|
Rajat Arora
|
A Deep Learning Framework for Solving Hyperbolic Partial Differential
Equations: Part I
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.NA math.AP math.NA
|
Physics informed neural networks (PINNs) have emerged as a powerful tool to
provide robust and accurate approximations of solutions to partial differential
equations (PDEs). However, PINNs face serious difficulties and challenges when
trying to approximate PDEs with dominant hyperbolic character. This research
focuses on the development of a physics informed deep learning framework to
approximate solutions to nonlinear PDEs that can develop shocks or
discontinuities without any a-priori knowledge of the solution or the location
of the discontinuities. The work takes motivation from finite element method
that solves for solution values at nodes in the discretized domain and use
these nodal values to obtain a globally defined solution field. Built on the
rigorous mathematical foundations of the discontinuous Galerkin method, the
framework naturally handles imposition of boundary conditions
(Neumann/Dirichlet), entropy conditions, and regularity requirements. Several
numerical experiments and validation with analytical solutions demonstrate the
accuracy, robustness, and effectiveness of the proposed framework.
|
[{'version': 'v1', 'created': 'Sun, 9 Jul 2023 08:27:17 GMT'}]
|
2023-07-11
|
Qi-Jun Hong
|
Deep learning for CALPHAD modeling: Universal parameter learning solely
based on chemical formula
| null | null | null |
cond-mat.mtrl-sci
|
Empowering the creation of thermodynamic and property databases, the CALPHAD
(CALculation of PHAse Diagrams) methodology plays a vital role in enhancing
materials and manufacturing process design. In this study, we propose a deep
learning approach to train parameters in CALPHAD models solely based on
chemical formula. We demonstrate its application through an example of
calculating the mixing parameter of liquids. This work showcases the
integration of CALPHAD and deep learning, highlighting its potential for
achieving automated comprehensive CALPHAD modeling.
|
[{'version': 'v1', 'created': 'Mon, 10 Jul 2023 00:06:31 GMT'}]
|
2023-07-11
|
Qiangqiang Gu, Zhanghao Zhouyin, Shishir Kumar Pandey, Peng Zhang,
Linfeng Zhang and Weinan E
|
Deep learning tight-binding approach for large-scale electronic
simulations at finite temperatures with $ab$ $initio$ accuracy
|
Nat. Commun. 15, 6772 (2024)
|
10.1038/s41467-024-51006-4
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Simulating electronic behavior in materials and devices with realistic large
system sizes remains a formidable task within the $ab$ $initio$ framework due
to its computational intensity. Here we show DeePTB, an efficient deep
learning-based tight-binding approach with $ab$ $initio$ accuracy to address
this issue. By training on structural data and corresponding $ab$ $initio$
eigenvalues, the DeePTB model can efficiently predict tight-binding
Hamiltonians for unseen structures, enabling efficient simulations of
large-size systems under external perturbations such as finite temperatures and
strain. This capability is vital for semiconductor band gap engineering and
materials design. When combined with molecular dynamics, DeePTB facilitates
efficient and accurate finite-temperature simulations of both atomic and
electronic behavior simultaneously. This is demonstrated by computing the
temperature-dependent electronic properties of a gallium phosphide system with
$10^6$ atoms. The availability of DeePTB bridges the gap between accuracy and
scalability in electronic simulations, potentially advancing materials science
and related fields by enabling large-scale electronic structure calculations.
|
[{'version': 'v1', 'created': 'Mon, 10 Jul 2023 15:35:26 GMT'}, {'version': 'v2', 'created': 'Tue, 11 Jul 2023 15:29:44 GMT'}, {'version': 'v3', 'created': 'Wed, 13 Nov 2024 10:40:22 GMT'}]
|
2024-11-14
|
Hao Tang, Boning Li, Yixuan Song, Mengren Liu, Haowei Xu, Guoqing
Wang, Heejung Chung, and Ju Li
|
Reinforcement learning-guided long-timescale simulation of hydrogen
transport in metals
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Atomic diffusion in solids is an important process in various phenomena.
However, atomistic simulations of diffusion processes are confronted with the
timescale problem: the accessible simulation time is usually far shorter than
that of experimental interests. In this work, we developed a long-timescale
method using reinforcement learning that simulates diffusion processes. As a
testbed, we simulate hydrogen diffusion in pure metals and a medium entropy
alloy, CrCoNi, getting hydrogen diffusivity reasonably consistent with previous
experiments. We also demonstrate that our method can accelerate the sampling of
low-energy configurations compared to the Metropolis-Hastings algorithm using
hydrogen migration to copper (111) surface sites as an example.
|
[{'version': 'v1', 'created': 'Wed, 5 Jul 2023 13:53:03 GMT'}]
|
2023-07-12
|
Kishan Govind, Daniela Oliveros, Antonin Dlouhy, Marc Legros, Stefan
Sandfeld
|
Deep Learning of Crystalline Defects from TEM images: A Solution for the
Problem of "Never Enough Training Data"
| null | null | null |
cs.CV cond-mat.mtrl-sci
|
Crystalline defects, such as line-like dislocations, play an important role
for the performance and reliability of many metallic devices. Their interaction
and evolution still poses a multitude of open questions to materials science
and materials physics. In-situ TEM experiments can provide important insights
into how dislocations behave and move. During such experiments, the dislocation
microstructure is captured in form of videos. The analysis of individual video
frames can provide useful insights but is limited by the capabilities of
automated identification, digitization, and quantitative extraction of the
dislocations as curved objects. The vast amount of data also makes manual
annotation very time consuming, thereby limiting the use of Deep
Learning-based, automated image analysis and segmentation of the dislocation
microstructure. In this work, a parametric model for generating synthetic
training data for segmentation of dislocations is developed. Even though domain
scientists might dismiss synthetic training images sometimes as too artificial,
our findings show that they can result in superior performance, particularly
regarding the generalizing of the Deep Learning models with respect to
different microstructures and imaging conditions. Additionally, we propose an
enhanced deep learning method optimized for segmenting overlapping or
intersecting dislocation lines. Upon testing this framework on four distinct
real datasets, we find that our synthetic training data are able to yield
high-quality results also on real images-even more so if fine-tune on a few
real images was done.
|
[{'version': 'v1', 'created': 'Wed, 12 Jul 2023 17:37:46 GMT'}]
|
2023-07-13
|
Arthur R. C. McCray, Tao Zhou, Saugat Kandel, Amanda Petford-Long,
Mathew J. Cherukara, Charudatta Phatak
|
AI-enabled Lorentz microscopy for quantitative imaging of nanoscale
magnetic spin textures
| null | null | null |
cond-mat.mtrl-sci physics.app-ph physics.comp-ph
|
The manipulation and control of nanoscale magnetic spin textures is of rising
interest as they are potential foundational units in next-generation computing
paradigms. Achieving this requires a quantitative understanding of the spin
texture behavior under external stimuli using in situ experiments. Lorentz
transmission electron microscopy (LTEM) enables real-space imaging of spin
textures at the nanoscale, but quantitative characterization of in situ data is
extremely challenging. Here, we present an AI-enabled phase-retrieval method
based on integrating a generative deep image prior with an image formation
forward model for LTEM. Our approach uses a single out-of-focus image for phase
retrieval and achieves significantly higher accuracy and robustness to noise
compared to existing methods. Furthermore, our method is capable of isolating
sample heterogeneities from magnetic contrast, as shown by application to
simulated and experimental data. This approach allows quantitative phase
reconstruction of in situ data and can also enable near real-time quantitative
magnetic imaging.
|
[{'version': 'v1', 'created': 'Tue, 18 Jul 2023 20:43:33 GMT'}]
|
2023-07-20
|
Akitaka Nakanishi, Shusuke Kasamatsu, Jun Haruyama, and Osamu Sugino
|
Theoretical analysis of zirconium oxynitride/water interface using
neural network potential
|
J. Phys. Chem. C 2025, 129, 5, 2403-2420
|
10.1021/acs.jpcc.4c05857
| null |
cond-mat.mtrl-sci
|
Zr oxides and oxynitrides are promising candidates to replace precious metal
cathodes in polymer electrolyte fuel cells. Oxygen reduction reaction activity
in this class of materials has been correlated with the amount of oxygen
vacancies, but a microscopic understanding of this correlation is still
lacking. To address this, we simulate a defective Zr$_7$O$_8$N$_4$/H$_2$O
interface model and compare it with a pristine ZrO$_2$/H$_2$O interface model.
First, ab initio replica exchange Monte Carlo sampling was performed to
determine defect segregation at the surface in the oxynitride slab model, then
molecular dynamics accelerated by neural network potentials was used to perform
1000 of 500 ps-long simulations to attain sufficient statistical accuracy of
the solid/liquid interface structure. The presence of oxygen vacancies on the
surface was found to clearly modify the local adsorption structure: water
molecules were found to adsorb preferentially on Zr atoms surrounding oxygen
vacancies, but not on the oxygen vacancies themselves. The fact that oxygen
vacancy sites are free from poisoning by water molecules may explain the
activity enhancement in defective systems. The layering of water molecules was
also modified considerably, which should influence the proton and O$_2$
transport near the interfaces which is another parameter that determines the
overall activity.
|
[{'version': 'v1', 'created': 'Fri, 21 Jul 2023 01:55:18 GMT'}, {'version': 'v2', 'created': 'Wed, 27 Dec 2023 10:48:40 GMT'}]
|
2025-02-18
|
Mingjian Wen, Matthew K. Horton, Jason M. Munro, Patrick Huck, and
Kristin A. Persson
|
An equivariant graph neural network for the elasticity tensors of all
seven crystal systems
| null |
10.1039/D3DD00233K
| null |
cond-mat.mtrl-sci
|
The elasticity tensor that describes the elastic response of a material to
external forces is among the most fundamental properties of materials. The
availability of full elasticity tensors for inorganic crystalline compounds,
however, is limited due to experimental and computational challenges. Here, we
report the materials tensor (MatTen) model for rapid and accurate estimation of
the full fourth-rank elasticity tensors of crystals. Based on equivariant graph
neural networks, MatTen satisfies the two essential requirements for elasticity
tensors: independence of the frame of reference and preservation of material
symmetry. Consequently, it provides a unified treatment of elasticity tensors
for all seven crystal systems across diverse chemical spaces, without the need
to deal with each separately.. MatTen was trained on a dataset of
first-principles elasticity tensors garnered by the Materials Project over the
past several years (we are releasing the data herein) and has broad
applications in predicting the isotropic elastic properties of polycrystalline
materials, examining the anisotropic behavior of single crystals, and
discovering new materials with exceptional mechanical properties. Using MatTen,
we have discovered a hundred new crystals with extremely large maximum
directional Young's modulus and eleven polymorphs of elemental cubic metals
with unconventional spatial orientation of Young's modulus.
|
[{'version': 'v1', 'created': 'Fri, 28 Jul 2023 00:43:51 GMT'}, {'version': 'v2', 'created': 'Mon, 22 Jan 2024 03:04:22 GMT'}]
|
2024-02-12
|
Linkang Zhan, Danfeng Ye, Xinjian Qiu, and Yan Cen
|
Discovery of Stable Hybrid Organic-inorganic Double Perovskites for
High-performance Solar Cells via Machine-learning Algorithms and Crystal
Graph Convolution Neural Network Method
| null | null | null |
cond-mat.mtrl-sci cs.CE physics.comp-ph
|
Hybrid peroskite solar cells are newly emergent high-performance photovoltaic
devices, which suffer from disadvantages such as toxic elements, short-term
stabilities, and so on. Searching for alternative perovskites with high
photovoltaic performances and thermally stabilities is urgent in this field. In
this work, stimulated by the recently proposed materials-genome initiative
project, firstly we build classical machine-learning algorithms for the models
of formation energies, bangdaps and Deybe temperatures for hybrid
organic-inorganic double perovskites, then we choose the high-precision models
to screen a large scale of double-perovskite chemical space, to filter out good
pervoskite candidates for solar cells. We also analyze features of importances
for the the three target properties to reveal the underlying mechanisms and
discover the typical characteristics of high-performances double perovskites.
Secondly we adopt the Crystal graph convolution neural network (CGCNN), to
build precise model for bandgaps of perovskites for further filtering. Finally
we use the ab-initio method to verify the results predicted by the CGCNN
method, and find that, six out of twenty randomly chosen (CH3)2NH2-based HOIDP
candidates possess finite bandgaps, and especially, (CH3)2NH2AuSbCl6 and
(CH3)2NH2CsPdF6 possess the bandgaps of 0.633 eV and 0.504 eV, which are
appropriate for photovoltaic applications. Our work not only provides a large
scale of potential high-performance double-perovskite candidates for futural
experimental or theoretical verification, but also showcases the effective and
powerful prediction of the combined ML and CGCNN method proposed for the first
time here.
|
[{'version': 'v1', 'created': 'Tue, 1 Aug 2023 12:22:44 GMT'}]
|
2023-08-02
|
Rui Liu, Sen Liu and Xiaoli Zhang
|
Unsupervised Learning of Part Similarity for Goal-Guided Accelerated
Experiment Design in Metal Additive Manufacturing
|
Advanced Manufacturing 2024
|
10.55092/am20240006
| null |
physics.data-an cond-mat.mtrl-sci
|
Metal additive manufacturing is gaining broad interest and increased use in
the industrial and academic fields. However, the quantification and
commercialization of standard parts usually require extensive experiments and
expensive post-characterization, which impedes the rapid development and
adaptation of metal AM technologies. In this work, a similarity-based
acceleration (S-acceleration) method for design of experiments is developed to
reduce the time and costs associated with unveiling process-property (porosity
defects) relationships during manufacturing. With S-acceleration, part semantic
features from machine-setting parameters and physics-effects informed
characteristics are explored for measuring mutual part similarities. A
user-defined simplification rate of experiments is proposed to purposely remove
redundant parts before conducting experiments printing without sacrificing
information gain as original full factorial experiment design. This
S-acceleration design of experiments is demonstrated on a Concept Laser M2
machine for the experimental plan of modeling relationships between process
parameters and part porosity defects. The printed part has 2 mm diameter by 4
mm tall pin geometry considering variations in build location and orientation,
laser settings and powder feedstock are held constant. In total, 242 parts are
measured to create a ground truth data set of porosity levels by using X-ray
tomography microscopy. The S-acceleration method is assessed for performance
considering 40%, 50%, and 60% of user-defined experiment simplification rates.
The repeated experiments are removed without ignoring the minority experiments
outlier, assuring a similar process-property relation in the original
experiment plan. The experiment number is significantly reduced based on part
similarity with minimal compromise of model accuracy and obtained knowledge.
|
[{'version': 'v1', 'created': 'Thu, 3 Aug 2023 04:03:01 GMT'}, {'version': 'v2', 'created': 'Wed, 29 May 2024 23:59:43 GMT'}]
|
2024-05-31
|
Mohd Zaki, Jayadeva, Mausam, N. M. Anoop Krishnan
|
MaScQA: A Question Answering Dataset for Investigating Materials Science
Knowledge of Large Language Models
| null | null | null |
cs.CL cond-mat.mtrl-sci
|
Information extraction and textual comprehension from materials literature
are vital for developing an exhaustive knowledge base that enables accelerated
materials discovery. Language models have demonstrated their capability to
answer domain-specific questions and retrieve information from knowledge bases.
However, there are no benchmark datasets in the materials domain that can
evaluate the understanding of the key concepts by these language models. In
this work, we curate a dataset of 650 challenging questions from the materials
domain that require the knowledge and skills of a materials student who has
cleared their undergraduate degree. We classify these questions based on their
structure and the materials science domain-based subcategories. Further, we
evaluate the performance of GPT-3.5 and GPT-4 models on solving these questions
via zero-shot and chain of thought prompting. It is observed that GPT-4 gives
the best performance (~62% accuracy) as compared to GPT-3.5. Interestingly, in
contrast to the general observation, no significant improvement in accuracy is
observed with the chain of thought prompting. To evaluate the limitations, we
performed an error analysis, which revealed conceptual errors (~64%) as the
major contributor compared to computational errors (~36%) towards the reduced
performance of LLMs. We hope that the dataset and analysis performed in this
work will promote further research in developing better materials science
domain-specific LLMs and strategies for information extraction.
|
[{'version': 'v1', 'created': 'Thu, 17 Aug 2023 17:51:05 GMT'}]
|
2023-08-21
|
Jaewoong Choi, Byungju Lee
|
Accelerated materials language processing enabled by GPT
| null | null | null |
cs.CL cond-mat.mtrl-sci
|
Materials language processing (MLP) is one of the key facilitators of
materials science research, as it enables the extraction of structured
information from massive materials science literature. Prior works suggested
high-performance MLP models for text classification, named entity recognition
(NER), and extractive question answering (QA), which require complex model
architecture, exhaustive fine-tuning and a large number of human-labelled
datasets. In this study, we develop generative pretrained transformer
(GPT)-enabled pipelines where the complex architectures of prior MLP models are
replaced with strategic designs of prompt engineering. First, we develop a
GPT-enabled document classification method for screening relevant documents,
achieving comparable accuracy and reliability compared to prior models, with
only small dataset. Secondly, for NER task, we design an entity-centric
prompts, and learning few-shot of them improved the performance on most of
entities in three open datasets. Finally, we develop an GPT-enabled extractive
QA model, which provides improved performance and shows the possibility of
automatically correcting annotations. While our findings confirm the potential
of GPT-enabled MLP models as well as their value in terms of reliability and
practicability, our scientific methods and systematic approach are applicable
to any materials science domain to accelerate the information extraction of
scientific literature.
|
[{'version': 'v1', 'created': 'Fri, 18 Aug 2023 07:31:13 GMT'}]
|
2023-08-21
|
Nianze Tao and Hiromi Morimoto and Stefano Leoni
|
Comprehensive Molecular Representation from Equivariant Transformer
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci physics.atm-clus physics.chem-ph
|
The tradeoff between precision and performance in molecular simulations can
nowadays be addressed by machine-learned force fields (MLFF), which combine
\textit{ab initio} accuracy with force field numerical efficiency. Different
from conventional force fields however, incorporating relevant electronic
degrees of freedom into MLFFs becomes important. Here, we implement an
equivariant transformer that embeds molecular net charge and spin state without
additional neural network parameters. The model trained on a singlet/triplet
non-correlated \ce{CH2} dataset can identify different spin states and shows
state-of-the-art extrapolation capability. Therein, self-attention sensibly
captures non-local effects, which, as we show, can be finely tuned over the
network hyper-parameters. We indeed found that Softmax activation functions
utilised in the self-attention mechanism of graph networks outperformed
ReLU-like functions in prediction accuracy. Increasing the attention
temperature from $\tau = \sqrt{d}$ to $\sqrt{2d}$ further improved the
extrapolation capability, indicating a weighty role of nonlocality.
Additionally, a weight initialisation method was purposed that sensibly
accelerated the training process.
|
[{'version': 'v1', 'created': 'Mon, 21 Aug 2023 14:39:29 GMT'}, {'version': 'v2', 'created': 'Thu, 7 Mar 2024 10:12:47 GMT'}]
|
2024-03-08
|
Dongyu Bai, Yihan Nie, Jing Shang, Minghao Liu, Yang Yang, Haifei
Zhan, Liangzhi Kou and Yuantong Gu
|
Ferroelectric Domain and Switching Dynamics in Curved In2Se3: First
Principle and Deep Learning Molecular Dynamics Simulations
| null | null | null |
cond-mat.mtrl-sci
|
Complex strain status can exist in 2D materials during their synthesis
process, resulting in significant impacts on the physical and chemical
properties. Despite their prevalence in experiments, their influence on the
material properties and the corresponding mechanism are often understudied due
to the lack of effective simulation methods. In this work, we investigated the
effects of bending, rippling, and bubbling on the ferroelectric domains in
In2Se3 monolayer by density functional theory (DFT) and deep learning molecular
dynamics (DLMD) simulations. The analysis of the tube model shows that bending
deformation imparts asymmetry into the system, and the polarization direction
tends to orient towards the tensile side, which has a lower energy state than
the opposite polarization direction. The energy barrier for polarization
switching can be reduced by compressive strain according DFT results. The
dynamics of the polarization switching is investigated by the DLMD simulations.
The influence of curvature and temperature on the switching time follows the
Arrhenius-style function. For the complex strain status in the rippling and
bubbling model, the lifetime of the local transient polarization is analyzed by
the autocorrelation function, and the size of the stable polarization domain is
identified. Local curvature and temperature can influence the local
polarization dynamics following the proposed Arrhenius-style equation. Through
cross-scale simulations, this study demonstrates the capability of
deep-learning potentials in simulating polarization for ferroelectric
materials. It further reveals the potential to manipulate local polarization in
ferroelectric materials through strain engineering.
|
[{'version': 'v1', 'created': 'Tue, 22 Aug 2023 06:27:11 GMT'}]
|
2023-08-23
|
Ruman Moulik, Ankita Phutela, Sajjan Sheoran, Saswata Bhattacharya
|
Accelerated Neural Network Training through Dimensionality Reduction for
High-Throughput Screening of Topological Materials
| null | null | null |
cond-mat.mtrl-sci cond-mat.str-el
|
Machine Learning facilitates building a large variety of models, starting
from elementary linear regression models to very complex neural networks.
Neural networks are currently limited by the size of data provided and the huge
computational cost of training a model. This is especially problematic when
dealing with a large set of features without much prior knowledge of how good
or bad each individual feature is. We try tackling the problem using
dimensionality reduction algorithms to construct more meaningful features. We
also compare the accuracy and training times of raw data and data transformed
after dimensionality reduction to deduce a sufficient number of dimensions
without sacrificing accuracy. The indicated estimation is done using a lighter
decision tree-based algorithm, AdaBoost, as it trains faster than neural
networks. We have chosen the data from an online database of topological
materials, Materiae. Our final goal is to construct a model to predict the
topological properties of new materials from elementary properties.
|
[{'version': 'v1', 'created': 'Thu, 24 Aug 2023 11:51:20 GMT'}]
|
2023-08-25
|
Shashank Pathrudkar, Ponkrshnan Thiagarajan, Shivang Agarwal, Amartya
S. Banerjee, Susanta Ghosh
|
Electronic Structure Prediction of Multi-million Atom Systems Through
Uncertainty Quantification Enabled Transfer Learning
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph quant-ph
|
The ground state electron density -- obtainable using Kohn-Sham Density
Functional Theory (KS-DFT) simulations -- contains a wealth of material
information, making its prediction via machine learning (ML) models attractive.
However, the computational expense of KS-DFT scales cubically with system size
which tends to stymie training data generation, making it difficult to develop
quantifiably accurate ML models that are applicable across many scales and
system configurations. Here, we address this fundamental challenge by employing
transfer learning to leverage the multi-scale nature of the training data,
while comprehensively sampling system configurations using thermalization. Our
ML models are less reliant on heuristics, and being based on Bayesian neural
networks, enable uncertainty quantification. We show that our models incur
significantly lower data generation costs while allowing confident -- and when
verifiable, accurate -- predictions for a wide variety of bulk systems well
beyond training, including systems with defects, different alloy compositions,
and at unprecedented, multi-million-atom scales. Moreover, such predictions can
be carried out using only modest computational resources.
|
[{'version': 'v1', 'created': 'Thu, 24 Aug 2023 21:41:29 GMT'}, {'version': 'v2', 'created': 'Thu, 14 Sep 2023 19:44:08 GMT'}, {'version': 'v3', 'created': 'Wed, 1 May 2024 13:16:55 GMT'}]
|
2024-05-02
|
Khaled Alrfou, Tian Zhao, Amir Kordijazi
|
Transfer Learning for Microstructure Segmentation with CS-UNet: A Hybrid
Algorithm with Transformer and CNN Encoders
| null | null | null |
cs.CV cond-mat.mtrl-sci
|
Transfer learning improves the performance of deep learning models by
initializing them with parameters pre-trained on larger datasets. Intuitively,
transfer learning is more effective when pre-training is on the in-domain
datasets. A recent study by NASA has demonstrated that the microstructure
segmentation with encoder-decoder algorithms benefits more from CNN encoders
pre-trained on microscopy images than from those pre-trained on natural images.
However, CNN models only capture the local spatial relations in images. In
recent years, attention networks such as Transformers are increasingly used in
image analysis to capture the long-range relations between pixels. In this
study, we compare the segmentation performance of Transformer and CNN models
pre-trained on microscopy images with those pre-trained on natural images. Our
result partially confirms the NASA study that the segmentation performance of
out-of-distribution images (taken under different imaging and sample
conditions) is significantly improved when pre-training on microscopy images.
However, the performance gain for one-shot and few-shot learning is more modest
with Transformers. We also find that for image segmentation, the combination of
pre-trained Transformers and CNN encoders are consistently better than
pre-trained CNN encoders alone. Our dataset (of about 50,000 images) combines
the public portion of the NASA dataset with additional images we collected.
Even with much less training data, our pre-trained models have significantly
better performance for image segmentation. This result suggests that
Transformers and CNN complement each other and when pre-trained on microscopy
images, they are more beneficial to the downstream tasks.
|
[{'version': 'v1', 'created': 'Sat, 26 Aug 2023 16:56:15 GMT'}]
|
2023-08-29
|
Hongshuo Huang, Rishikesh Magar, Changwen Xu and Amir Barati Farimani
|
Materials Informatics Transformer: A Language Model for Interpretable
Materials Properties Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.chem-ph
|
Recently, the remarkable capabilities of large language models (LLMs) have
been illustrated across a variety of research domains such as natural language
processing, computer vision, and molecular modeling. We extend this paradigm by
utilizing LLMs for material property prediction by introducing our model
Materials Informatics Transformer (MatInFormer). Specifically, we introduce a
novel approach that involves learning the grammar of crystallography through
the tokenization of pertinent space group information. We further illustrate
the adaptability of MatInFormer by incorporating task-specific data pertaining
to Metal-Organic Frameworks (MOFs). Through attention visualization, we uncover
the key features that the model prioritizes during property prediction. The
effectiveness of our proposed model is empirically validated across 14 distinct
datasets, hereby underscoring its potential for high throughput screening
through accurate material property prediction.
|
[{'version': 'v1', 'created': 'Wed, 30 Aug 2023 18:34:55 GMT'}, {'version': 'v2', 'created': 'Fri, 1 Sep 2023 12:40:29 GMT'}]
|
2023-09-04
|
Fahrettin Sarcan, Alex J. Armstrong, Yusuf K. Bostan, Esra Kus, Keith
McKenna, Ayse Erol, Yue Wang
|
Ultraviolet-ozone treatment: an effective method for fine-tuning optical
and electrical properties of suspended and substrate-supported MoS2
| null | null | null |
cond-mat.mtrl-sci physics.app-ph physics.optics
|
Ultraviolet-ozone (UV-O3) treatment is a simple but effective technique for
surface cleaning, surface sterilization, doping and oxidation, and is
applicable to a wide range of materials. In this study, we investigated how
UV-O3 treatment affects the optical and electrical properties of molybdenum
disulfide (MoS2), with and without the presence of a dielectric substrate. We
performed detailed photoluminescence (PL) measurements on 1-7 layers of MoS2
with up to 8 minutes of UV-O3 exposure. Density functional theory (DFT)
calculations were carried out to provide insight into oxygen-MoS2 interaction
mechanisms. Our results showed that the influence of UV-O3 treatment on PL
depends on whether the substrate is present, as well as the number of layers.
The PL intensity of the substrate-supported MoS2 decreased dramatically with
the increase of UV-O3 treatment time and was fully quenched after 8 mins.
However, the PL intensity of the suspended flakes was less affected. 4 minutes
of UV-O3 exposure was found to be optimal to produce p-type MoS2, while
maintaining above 80% of the PL intensity and the emission wavelength, compared
to pristine flakes (intrinsically n-type). Our electrical measurements showed
that UV-O3 treatment for more than 6 minutes not only caused a reduction in the
electron density but also deteriorated the hole-dominated transport. It is
revealed that the substrate plays a critical role in the manipulation of the
electrical and optical properties of MoS2, which should be considered in future
device fabrication and applications.
|
[{'version': 'v1', 'created': 'Thu, 7 Sep 2023 12:41:53 GMT'}]
|
2023-09-08
|
Jianan Xie, Ji Liu, Chi Zhang, Xihui Chen, Ping Huai, Jie Zheng,
Xiaofeng Zhang
|
Weakly supervised learning for pattern classification in serial
femtosecond crystallography
|
Opt. Express 31(20), 32909-32924 (2023)
|
10.1364/OE.492311
| null |
cond-mat.mtrl-sci cs.LG physics.optics
|
Serial femtosecond crystallography at X-ray free electron laser facilities
opens a new era for the determination of crystal structure. However, the data
processing of those experiments is facing unprecedented challenge, because the
total number of diffraction patterns needed to determinate a high-resolution
structure is huge. Machine learning methods are very likely to play important
roles in dealing with such a large volume of data. Convolutional neural
networks have made a great success in the field of pattern classification,
however, training of the networks need very large datasets with labels. Th is
heavy dependence on labeled datasets will seriously restrict the application of
networks, because it is very costly to annotate a large number of diffraction
patterns. In this article we present our job on the classification of
diffraction pattern by weakly supervised algorithms, with the aim of reducing
as much as possible the size of the labeled dataset required for training. Our
result shows that weakly supervised methods can significantly reduce the need
for the number of labeled patterns while achieving comparable accuracy to fully
supervised methods.
|
[{'version': 'v1', 'created': 'Sun, 30 Jul 2023 12:42:19 GMT'}, {'version': 'v2', 'created': 'Thu, 21 Sep 2023 06:52:38 GMT'}]
|
2023-09-22
|
Ethan M. Sunshine and Muhammed Shuaibi and Zachary W. Ulissi and John
R. Kitchin
|
Chemical Properties from Graph Neural Network-Predicted Electron
Densities
| null | null | null |
cond-mat.mtrl-sci
|
According to density functional theory, any chemical property can be inferred
from the electron density, making it the most informative attribute of an
atomic structure. In this work, we demonstrate the use of established physical
methods to obtain important chemical properties from model-predicted electron
densities. We introduce graph neural network architectural choices that provide
physically relevant and useful electron density predictions. Despite not
training to predict atomic charges, the model is able to predict atomic charges
with an order of magnitude lower error than a sum of atomic charge densities.
Similarly, the model predicts dipole moments with half the error of the sum of
atomic charge densities method. We demonstrate that larger data sets lead to
more useful predictions in these tasks. These results pave the way for an
alternative path in atomistic machine learning, where data-driven approaches
and existing physical methods are used in tandem to obtain a variety of
chemical properties in an explainable and self-consistent manner.
|
[{'version': 'v1', 'created': 'Sat, 9 Sep 2023 14:31:08 GMT'}]
|
2023-09-12
|
Chen Zhang, Cl\'emence Bos, Stefan Sandfeld, Ruth Schwaiger
|
Unsupervised Learning of Nanoindentation Data to Infer Microstructural
Details of Complex Materials
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
In this study, Cu-Cr composites were studied by nanoindentation. Arrays of
indents were placed over large areas of the samples resulting in datasets
consisting of several hundred measurements of Young's modulus and hardness at
varying indentation depths. The unsupervised learning technique, Gaussian
mixture model, was employed to analyze the data, which helped to determine the
number of "mechanical phases" and the respective mechanical properties.
Additionally, a cross-validation approach was introduced to infer whether the
data quantity was adequate and to suggest the amount of data required for
reliable predictions -- one of the often encountered but difficult to resolve
issues in machine learning of materials science problems.
|
[{'version': 'v1', 'created': 'Tue, 12 Sep 2023 21:45:33 GMT'}]
|
2023-09-14
|
Sadman Sadeed Omee, Lai Wei, Jianjun Hu
|
Crystal structure prediction using neural network potential and
age-fitness Pareto genetic algorithm
| null | null | null |
cond-mat.mtrl-sci cs.LG cs.NE
|
While crystal structure prediction (CSP) remains a longstanding challenge, we
introduce ParetoCSP, a novel algorithm for CSP, which combines a
multi-objective genetic algorithm (MOGA) with a neural network inter-atomic
potential (IAP) model to find energetically optimal crystal structures given
chemical compositions. We enhance the NSGA-III algorithm by incorporating the
genotypic age as an independent optimization criterion and employ the M3GNet
universal IAP to guide the GA search. Compared to GN-OA, a state-of-the-art
neural potential based CSP algorithm, ParetoCSP demonstrated significantly
better predictive capabilities, outperforming by a factor of $2.562$ across
$55$ diverse benchmark structures, as evaluated by seven performance metrics.
Trajectory analysis of the traversed structures of all algorithms shows that
ParetoCSP generated more valid structures than other algorithms, which helped
guide the GA to search more effectively for the optimal structures
|
[{'version': 'v1', 'created': 'Wed, 13 Sep 2023 04:17:28 GMT'}]
|
2023-09-14
|
Rafael Monteiro and Kartik Sau
|
Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate
Optimization Problems
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.AI math.OC math.PR
|
In this paper, we introduce a new heuristics for global optimization in
scenarios where extensive evaluations of the cost function are expensive,
inaccessible, or even prohibitive. The method, which we call
Landscape-Sketch-and-Step (LSS), combines Machine Learning, Stochastic
Optimization, and Reinforcement Learning techniques, relying on historical
information from previously sampled points to make judicious choices of
parameter values where the cost function should be evaluated at. Unlike
optimization by Replica Exchange Monte Carlo methods, the number of evaluations
of the cost function required in this approach is comparable to that used by
Simulated Annealing, quality that is especially important in contexts like
high-throughput computing or high-performance computing tasks, where
evaluations are either computationally expensive or take a long time to be
performed. The method also differs from standard Surrogate Optimization
techniques, for it does not construct a surrogate model that aims at
approximating or reconstructing the objective function. We illustrate our
method by applying it to low dimensional optimization problems (dimensions 1,
2, 4, and 8) that mimick known difficulties of minimization on rugged energy
landscapes often seen in Condensed Matter Physics, where cost functions are
rugged and plagued with local minima. When compared to classical Simulated
Annealing, the LSS shows an effective acceleration of the optimization process.
|
[{'version': 'v1', 'created': 'Thu, 14 Sep 2023 01:53:45 GMT'}, {'version': 'v2', 'created': 'Mon, 2 Oct 2023 15:37:23 GMT'}, {'version': 'v3', 'created': 'Wed, 4 Oct 2023 23:03:48 GMT'}]
|
2023-10-06
|
Rachel K. Luu, Markus J. Buehler
|
BioinspiredLLM: Conversational Large Language Model for the Mechanics of
Biological and Bio-inspired Materials
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.soft cs.LG nlin.AO
|
The study of biological materials and bio-inspired materials science is well
established; however, surprisingly little knowledge has been systematically
translated to engineering solutions. To accelerate discovery and guide
insights, an open-source autoregressive transformer large language model (LLM),
BioinspiredLLM, is reported. The model was finetuned with a corpus of over a
thousand peer-reviewed articles in the field of structural biological and
bio-inspired materials and can be prompted to recall information, assist with
research tasks, and function as an engine for creativity. The model has proven
that it is able to accurately recall information about biological materials and
is further enhanced with enhanced reasoning ability, as well as with
retrieval-augmented generation to incorporate new data during generation that
can also help to traceback sources, update the knowledge base, and connect
knowledge domains. BioinspiredLLM also has been shown to develop sound
hypotheses regarding biological materials design and remarkably so for
materials that have never been explicitly studied before. Lastly, the model
showed impressive promise in collaborating with other generative artificial
intelligence models in a workflow that can reshape the traditional materials
design process. This collaborative generative artificial intelligence method
can stimulate and enhance bio-inspired materials design workflows. Biological
materials are at a critical intersection of multiple scientific fields and
models like BioinspiredLLM help to connect knowledge domains.
|
[{'version': 'v1', 'created': 'Fri, 15 Sep 2023 22:12:44 GMT'}, {'version': 'v2', 'created': 'Mon, 11 Dec 2023 18:05:25 GMT'}]
|
2023-12-12
|
Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst
|
Structure to Property: Chemical Element Embeddings and a Deep Learning
Approach for Accurate Prediction of Chemical Properties
| null | null | null |
physics.chem-ph cond-mat.mtrl-sci cs.LG physics.atm-clus q-bio.QM
|
We introduce the elEmBERT model for chemical classification tasks. It is
based on deep learning techniques, such as a multilayer encoder architecture.
We demonstrate the opportunities offered by our approach on sets of organic,
inorganic and crystalline compounds. In particular, we developed and tested the
model using the Matbench and Moleculenet benchmarks, which include crystal
properties and drug design-related benchmarks. We also conduct an analysis of
vector representations of chemical compounds, shedding light on the underlying
patterns in structural data. Our model exhibits exceptional predictive
capabilities and proves universally applicable to molecular and material
datasets. For instance, on the Tox21 dataset, we achieved an average precision
of 96%, surpassing the previously best result by 10%.
|
[{'version': 'v1', 'created': 'Sun, 17 Sep 2023 19:41:32 GMT'}, {'version': 'v2', 'created': 'Wed, 31 Jul 2024 22:27:28 GMT'}, {'version': 'v3', 'created': 'Sat, 17 Aug 2024 12:06:04 GMT'}]
|
2024-08-20
|
S.M. Frolov and P. Zhang and B. Zhang and Y. Jiang and S. Byard and
S.R. Mudi and J. Chen and A.-H. Chen and M. Hocevar and M. Gupta and C.
Riggert and V.S. Pribiag
|
"Smoking gun" signatures of topological milestones in trivial materials
by measurement fine-tuning and data postselection
| null | null | null |
cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el cond-mat.supr-con
|
Exploring the topology of electronic bands is a way to realize new states of
matter with possible implications for information technology. Because bands
cannot always be observed directly, a central question is how to tell that a
topological regime has been achieved. Experiments are often guided by a
prediction of a unique signal or a pattern, called "the smoking gun". Examples
include peaks in conductivity, microwave resonances, and shifts in interference
fringes. However, many condensed matter experiments are performed on relatively
small, micron or nanometer-scale, specimens. These structures are in the
so-called mesoscopic regime, between atomic and macroscopic physics, where
phenomenology is particularly rich. In this paper, we demonstrate that the
trivial effects of quantum confinement, quantum interference and charge
dynamics in nanostructures can reproduce accepted smoking gun signatures of
triplet supercurrents, Majorana modes, topological Josephson junctions and
fractionalized particles. The examples we use correspond to milestones of
topological quantum computing: qubit spectroscopy, fusion and braiding. None of
the samples we use are in the topological regime. The smoking gun patterns are
achieved by fine-tuning during data acquisition and by subsequent data
selection to pick non-representative examples out of a fluid multitude of
similar patterns that do not generally fit the "smoking gun" designation.
Building on this insight, we discuss ways that experimentalists can rigorously
delineate between topological and non-topological effects, and the effects of
fine-tuning by deeper analysis of larger volumes of data.
|
[{'version': 'v1', 'created': 'Sun, 17 Sep 2023 20:25:31 GMT'}]
|
2023-09-19
|
Taoyuze Lv, Zhicheng Zhong, Yuhang Liang, Feng Li, Jun Huang, Rongkun
Zheng
|
Deep Charge: A Deep Learning Model of Electron Density from One-Shot
Density Functional Theory Calculation
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Electron charge density is a fundamental physical quantity, determining
various properties of matter. In this study, we have proposed a deep-learning
model for accurate charge density prediction. Our model naturally preserves
physical symmetries and can be effectively trained from one-shot density
functional theory calculation toward high accuracy. It captures detailed atomic
environment information, ensuring accurate predictions of charge density across
bulk, surface, molecules, and amorphous structures. This implementation
exhibits excellent scalability and provides efficient analyses of material
properties in large-scale condensed matter systems.
|
[{'version': 'v1', 'created': 'Tue, 26 Sep 2023 03:36:13 GMT'}]
|
2023-09-27
|
Julio J. Vald\'es and Alain B. Tchagang
|
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets
Using Unsupervised Learning
| null | null | null |
physics.chem-ph cond-mat.mtrl-sci cs.LG
|
This paper explores the internal structure of two quantum mechanics datasets
(QM7b, QM9), composed of several thousands of organic molecules and described
in terms of electronic properties. Understanding the structure and
characteristics of this kind of data is important when predicting the atomic
composition from the properties in inverse molecular designs. Intrinsic
dimension analysis, clustering, and outlier detection methods were used in the
study. They revealed that for both datasets the intrinsic dimensionality is
several times smaller than the descriptive dimensions. The QM7b data is
composed of well defined clusters related to atomic composition. The QM9 data
consists of an outer region predominantly composed of outliers, and an inner
core region that concentrates clustered, inliner objects. A significant
relationship exists between the number of atoms in the molecule and its
outlier/inner nature. Despite the structural differences, the predictability of
variables of interest for inverse molecular design is high. This is exemplified
with models estimating the number of atoms of the molecule from both the
original properties, and from lower dimensional embedding spaces.
|
[{'version': 'v1', 'created': 'Mon, 25 Sep 2023 23:06:32 GMT'}]
|
2023-09-28
|
Roozbeh Eghbalpoor, Azadeh Sheidaei
|
A peridynamic-informed deep learning model for brittle damage prediction
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
In this study, a novel approach that combines the principles of peridynamic
(PD) theory with PINN is presented to predict quasi-static damage and crack
propagation in brittle materials. To achieve high prediction accuracy and
convergence rate, the linearized PD governing equation is enforced in the
PINN's residual-based loss function. The proposed PD-INN is able to learn and
capture intricate displacement patterns associated with different geometrical
parameters, such as pre-crack position and length. Several enhancements like
cyclical annealing schedule and deformation gradient aware optimization
technique are proposed to ensure the model would not get stuck in its trivial
solution. The model's performance assessment is conducted by monitoring the
behavior of loss function throughout the training process. The PD-INN
predictions are also validated through several benchmark cases with the results
obtained from high-fidelity techniques such as PD direct numerical method and
Extended-Finite Element Method. Our results show the ability of the nonlocal
PD-INN to predict damage and crack propagation accurately and efficiently.
|
[{'version': 'v1', 'created': 'Mon, 2 Oct 2023 17:12:20 GMT'}]
|
2023-10-03
|
Vaibhav Bihani, Utkarsh Pratiush, Sajid Mannan, Tao Du, Zhimin Chen,
Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, N M
Anoop Krishnan
|
EGraFFBench: Evaluation of Equivariant Graph Neural Network Force Fields
for Atomistic Simulations
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Equivariant graph neural networks force fields (EGraFFs) have shown great
promise in modelling complex interactions in atomic systems by exploiting the
graphs' inherent symmetries. Recent works have led to a surge in the
development of novel architectures that incorporate equivariance-based
inductive biases alongside architectural innovations like graph transformers
and message passing to model atomic interactions. However, thorough evaluations
of these deploying EGraFFs for the downstream task of real-world atomistic
simulations, is lacking. To this end, here we perform a systematic benchmarking
of 6 EGraFF algorithms (NequIP, Allegro, BOTNet, MACE, Equiformer, TorchMDNet),
with the aim of understanding their capabilities and limitations for realistic
atomistic simulations. In addition to our thorough evaluation and analysis on
eight existing datasets based on the benchmarking literature, we release two
new benchmark datasets, propose four new metrics, and three challenging tasks.
The new datasets and tasks evaluate the performance of EGraFF to
out-of-distribution data, in terms of different crystal structures,
temperatures, and new molecules. Interestingly, evaluation of the EGraFF models
based on dynamic simulations reveals that having a lower error on energy or
force does not guarantee stable or reliable simulation or faithful replication
of the atomic structures. Moreover, we find that no model clearly outperforms
other models on all datasets and tasks. Importantly, we show that the
performance of all the models on out-of-distribution datasets is unreliable,
pointing to the need for the development of a foundation model for force fields
that can be used in real-world simulations. In summary, this work establishes a
rigorous framework for evaluating machine learning force fields in the context
of atomic simulations and points to open research challenges within this
domain.
|
[{'version': 'v1', 'created': 'Tue, 3 Oct 2023 20:49:00 GMT'}, {'version': 'v2', 'created': 'Fri, 24 Nov 2023 17:26:56 GMT'}]
|
2023-11-27
|
Raj Ghugare, Santiago Miret, Adriana Hugessen, Mariano Phielipp, Glen
Berseth
|
Searching for High-Value Molecules Using Reinforcement Learning and
Transformers
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.AI
|
Reinforcement learning (RL) over text representations can be effective for
finding high-value policies that can search over graphs. However, RL requires
careful structuring of the search space and algorithm design to be effective in
this challenge. Through extensive experiments, we explore how different design
choices for text grammar and algorithmic choices for training can affect an RL
policy's ability to generate molecules with desired properties. We arrive at a
new RL-based molecular design algorithm (ChemRLformer) and perform a thorough
analysis using 25 molecule design tasks, including computationally complex
protein docking simulations. From this analysis, we discover unique insights in
this problem space and show that ChemRLformer achieves state-of-the-art
performance while being more straightforward than prior work by demystifying
which design choices are actually helpful for text-based molecule design.
|
[{'version': 'v1', 'created': 'Wed, 4 Oct 2023 15:40:07 GMT'}]
|
2023-10-05
|
Joshua A. Vita, Dallas R. Trinkle
|
Spline-based neural network interatomic potentials: blending classical
and machine learning models
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
While machine learning (ML) interatomic potentials (IPs) are able to achieve
accuracies nearing the level of noise inherent in the first-principles data to
which they are trained, it remains to be shown if their increased complexities
are strictly necessary for constructing high-quality IPs. In this work, we
introduce a new MLIP framework which blends the simplicity of spline-based MEAM
(s-MEAM) potentials with the flexibility of a neural network (NN) architecture.
The proposed framework, which we call the spline-based neural network potential
(s-NNP), is a simplified version of the traditional NNP that can be used to
describe complex datasets in a computationally efficient manner. We demonstrate
how this framework can be used to probe the boundary between classical and ML
IPs, highlighting the benefits of key architectural changes. Furthermore, we
show that using spline filters for encoding atomic environments results in a
readily interpreted embedding layer which can be coupled with modifications to
the NN to incorporate expected physical behaviors and improve overall
interpretability. Finally, we test the flexibility of the spline filters,
observing that they can be shared across multiple chemical systems in order to
provide a convenient reference point from which to begin performing
cross-system analyses.
|
[{'version': 'v1', 'created': 'Wed, 4 Oct 2023 15:42:26 GMT'}]
|
2023-10-05
|
Hirofumi Tsuruta, Yukari Katsura, Masaya Kumagai
|
DeepCrysTet: A Deep Learning Approach Using Tetrahedral Mesh for
Predicting Properties of Crystalline Materials
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Machine learning (ML) is becoming increasingly popular for predicting
material properties to accelerate materials discovery. Because material
properties are strongly affected by its crystal structure, a key issue is
converting the crystal structure into the features for input to the ML model.
Currently, the most common method is to convert the crystal structure into a
graph and predicting its properties using a graph neural network (GNN). Some
GNN models, such as crystal graph convolutional neural network (CGCNN) and
atomistic line graph neural network (ALIGNN), have achieved highly accurate
predictions of material properties. Despite these successes, using a graph to
represent a crystal structure has the notable limitation of losing the crystal
structure's three-dimensional (3D) information. In this work, we propose
DeepCrysTet, a novel deep learning approach for predicting material properties,
which uses crystal structures represented as a 3D tetrahedral mesh generated by
Delaunay tetrahedralization. DeepCrysTet provides a useful framework that
includes a 3D mesh generation method, mesh-based feature design, and neural
network design. The experimental results using the Materials Project dataset
show that DeepCrysTet significantly outperforms existing GNN models in
classifying crystal structures and achieves state-of-the-art performance in
predicting elastic properties.
|
[{'version': 'v1', 'created': 'Thu, 7 Sep 2023 05:23:52 GMT'}]
|
2023-10-12
|
Ziyi Chen, Fankai Xie, Meng Wan, Yang Yuan, Miao Liu, Zongguo Wang,
Sheng Meng, Yangang Wang
|
MatChat: A Large Language Model and Application Service Platform for
Materials Science
|
Chinese Physics B 32, 118104 (2023)
|
10.1088/1674-1056/ad04cb
| null |
cond-mat.mtrl-sci cs.AI
|
The prediction of chemical synthesis pathways plays a pivotal role in
materials science research. Challenges, such as the complexity of synthesis
pathways and the lack of comprehensive datasets, currently hinder our ability
to predict these chemical processes accurately. However, recent advancements in
generative artificial intelligence (GAI), including automated text generation
and question-answering systems, coupled with fine-tuning techniques, have
facilitated the deployment of large-scale AI models tailored to specific
domains. In this study, we harness the power of the LLaMA2-7B model and enhance
it through a learning process that incorporates 13,878 pieces of structured
material knowledge data. This specialized AI model, named MatChat, focuses on
predicting inorganic material synthesis pathways. MatChat exhibits remarkable
proficiency in generating and reasoning with knowledge in materials science.
Although MatChat requires further refinement to meet the diverse material
design needs, this research undeniably highlights its impressive reasoning
capabilities and innovative potential in the field of materials science.
MatChat is now accessible online and open for use, with both the model and its
application framework available as open source. This study establishes a robust
foundation for collaborative innovation in the integration of generative AI in
materials science.
|
[{'version': 'v1', 'created': 'Wed, 11 Oct 2023 05:11:46 GMT'}]
|
2023-11-03
|
Kin Long Kelvin Lee, Carmelo Gonzales, Matthew Spellings, Mikhail
Galkin, Santiago Miret, Nalini Kumar
|
Towards Foundation Models for Materials Science: The Open MatSci ML
Toolkit
| null |
10.1145/3624062.3626081
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Artificial intelligence and machine learning have shown great promise in
their ability to accelerate novel materials discovery. As researchers and
domain scientists seek to unify and consolidate chemical knowledge, the case
for models with potential to generalize across different tasks within materials
science - so-called "foundation models" - grows with ambitions. This manuscript
reviews our recent progress with development of Open MatSci ML Toolkit, and
details experiments that lay the groundwork for foundation model research and
development with our framework. First, we describe and characterize a new
pretraining task that uses synthetic data generated from symmetry operations,
and reveal complex training dynamics at large scales. Using the pretrained
model, we discuss a number of use cases relevant to foundation model
development: semantic architecture of datasets, and fine-tuning for property
prediction and classification. Our key results show that for simple
applications, pretraining appears to provide worse modeling performance than
training models from random initialization. However, for more complex
instances, such as when a model is required to learn across multiple datasets
and types of targets simultaneously, the inductive bias from pretraining
provides significantly better performance. This insight will hopefully inform
subsequent efforts into creating foundation models for materials science
applications.
|
[{'version': 'v1', 'created': 'Wed, 11 Oct 2023 20:14:07 GMT'}]
|
2023-10-13
|
N. Brun, G. Lambert and L. Bocher
|
Deep Learning for EELS hyperspectral images unmixing -- using
autoencoders
| null | null | null |
physics.data-an cond-mat.mtrl-sci
|
Spatially resolved Electron Energy-Loss Spectroscopy (EELS) conducted in a
Scanning Transmission Electron Microscope (STEM) enables the acquisition of
hyperspectral images (HSIs). Spectral unmixing (SU) is the process of
decomposing each spectrum of an HSI into a combination of representative
spectra (endmembers) corresponding to compounds present in the sample along
with their local proportions (abundances). SU is a complex task, and various
methods have been developed in different communities using HSIs. However, none
of these methods fully satisfy the STEM-EELS requirements. Recent advancements
in remote sensing, which focus on Deep Learning techniques, have the potential
to meet these requirements, particularly Autoencoders (AEs). In this study, the
performance of Deep Learning methods using AE for SU is evaluated, and their
results are compared with traditional methods. Synthetic HSIs have been created
to quantitatively assess the outcomes of the unmixing process using specific
metrics. The methods are subsequently applied to a series of experimental data.
The findings demonstrate the promising potential of AE as a tool for STEM-EELS
SU, marking a starting point for exploring more sophisticated Neural Networks.
|
[{'version': 'v1', 'created': 'Thu, 12 Oct 2023 13:09:08 GMT'}]
|
2023-10-13
|
Jared K. Averitt, Sajedeh Pourianejad, Olubunmi Ayodele, Kirby
Schmidt, Anthony Trofe, Joseph Starobin, and Tetyana Ignatova
|
Optimized nanodevice fabrication using clean transfer of graphene by
polymer mixture: Experiments and Neural Network based simulations
| null | null | null |
physics.app-ph cond-mat.mtrl-sci
|
In this study, we investigate both experimentally and computationally the
molecular interactions of two distinct polymers with graphene. Our experimental
findings indicate that the use of a polymer mixture reduces the transfer
induced doping and strain in fabricated graphene devices as compared to
conventional single polymer wet transfer. We found that such reduction is
related to the decreased affinity of mixture of polymethyl methacrylate and
angelica lactone polymer for graphene. We investigated changes in binding
energy (BE) of polymer mixture and graphene by considering energy decomposition
analysis using a pre-trained potential neural network. It was found that
numerical simulations accurately predicted two-fold reduction of BE and order
of magnitude reduction of electrostatic interaction between polymers.
|
[{'version': 'v1', 'created': 'Mon, 16 Oct 2023 02:43:11 GMT'}]
|
2023-10-17
|
Pierre Mignon, Abdul-Rahman Allouche, Neil Richard Innis and Colin
Bousige
|
Neural network approach for a rapid prediction of metal-supported
borophene properties
| null |
10.1021/jacs.3c11549
| null |
cond-mat.mtrl-sci physics.chem-ph physics.comp-ph
|
We develop a high-dimensional neural network potential (NNP) to describe the
structural and energetic properties of borophene deposited on silver. This NNP
has the accuracy of DFT calculations while achieving computational speedups of
several orders of magnitude, allowing the study of extensive structures that
may reveal intriguing moir\'e patterns or surface corrugations. We describe an
efficient approach to constructing the training data set using an iterative
technique known as the "adaptive learning approach". The developed NNP
potential is able to produce, with an excellent agreement, the structure,
energy and forces of DFT. Finally, the calculated stability of various
borophene polymorphs, including those not initially included in the training
dataset, shows better stabilization for $\nu\sim0.1$ hole density, and in
particular for the allotrope $\alpha$ ($\nu=\frac{1}{9}$). The stability of
borophene on the metal surface is shown to depend on its orientation, implying
structural corrugation patterns that can only be observed from long time
simulations on extended systems. The NNP also demonstrates its ability to
simulate vibrational densities of states and produce realistic structures, with
simulated STM images closely matching the experimental ones.
|
[{'version': 'v1', 'created': 'Tue, 17 Oct 2023 13:13:23 GMT'}]
|
2023-12-12
|
Brenda S. Ferrari, Matteo Manica, Ronaldo Giro, Teodoro Laino and
Mathias B. Steiner
|
Predicting polymerization reactions via transfer learning using chemical
language models
| null | null | null |
physics.chem-ph cond-mat.mtrl-sci
|
Polymers are candidate materials for a wide range of sustainability
applications such as carbon capture and energy storage. However, computational
polymer discovery lacks automated analysis of reaction pathways and stability
assessment through retro-synthesis. Here, we report the first extension of
transformer-based language models to polymerization reactions for both forward
and retrosynthesis tasks. To that end, we have curated a polymerization dataset
for vinyl polymers covering reactions and retrosynthesis for representative
homo-polymers and co-polymers. Overall, we obtain a forward model Top-4
accuracy of 80% and a backward model Top-4 accuracy of 60%. We further analyze
the model performance with representative polymerization and retro-synthesis
examples and evaluate its prediction quality from a materials science
perspective.
|
[{'version': 'v1', 'created': 'Tue, 17 Oct 2023 17:31:52 GMT'}]
|
2023-10-18
|
Juan C. Verduzco, Ethan Holbrook, and Alejandro Strachan
|
GPT-4 as an interface between researchers and computational software:
improving usability and reproducibility
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
Large language models (LLMs) are playing an increasingly important role in
science and engineering. For example, their ability to parse and understand
human and computer languages makes them powerful interpreters and their use in
applications like code generation are well-documented. We explore the ability
of the GPT-4 LLM to ameliorate two major challenges in computational materials
science: i) the high barriers for adoption of scientific software associated
with the use of custom input languages, and ii) the poor reproducibility of
published results due to insufficient details in the description of simulation
methods. We focus on a widely used software for molecular dynamics simulations,
the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), and
quantify the usefulness of input files generated by GPT-4 from task
descriptions in English and its ability to generate detailed descriptions of
computational tasks from input files. We find that GPT-4 can generate correct
and ready-to-use input files for relatively simple tasks and useful starting
points for more complex, multi-step simulations. In addition, GPT-4's
description of computational tasks from input files can be tuned from a
detailed set of step-by-step instructions to a summary description appropriate
for publications. Our results show that GPT-4 can reduce the number of routine
tasks performed by researchers, accelerate the training of new users, and
enhance reproducibility.
|
[{'version': 'v1', 'created': 'Wed, 4 Oct 2023 14:25:39 GMT'}]
|
2023-10-19
|
Francesca Tavazza and Kamal Choudhary and Brian DeCost
|
Approaches for Uncertainty Quantification of AI-predicted Material
Properties: A Comparison
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
The development of large databases of material properties, together with the
availability of powerful computers, has allowed machine learning (ML) modeling
to become a widely used tool for predicting material performances. While
confidence intervals are commonly reported for such ML models, prediction
intervals, i.e., the uncertainty on each prediction, are not as frequently
available. Here, we investigate three easy-to-implement approaches to determine
such individual uncertainty, comparing them across ten ML quantities spanning
energetics, mechanical, electronic, optical, and spectral properties.
Specifically, we focused on the Quantile approach, the direct machine learning
of the prediction intervals and Ensemble methods.
|
[{'version': 'v1', 'created': 'Thu, 19 Oct 2023 20:20:39 GMT'}]
|
2023-10-23
|
Andre Niyongabo Rubungo, Craig Arnold, Barry P. Rand, Adji Bousso
Dieng
|
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline
Solids From Their Text Descriptions
| null | null | null |
cs.CL cond-mat.mtrl-sci
|
The prediction of crystal properties plays a crucial role in the crystal
design process. Current methods for predicting crystal properties focus on
modeling crystal structures using graph neural networks (GNNs). Although GNNs
are powerful, accurately modeling the complex interactions between atoms and
molecules within a crystal remains a challenge. Surprisingly, predicting
crystal properties from crystal text descriptions is understudied, despite the
rich information and expressiveness that text data offer. One of the main
reasons is the lack of publicly available data for this task. In this paper, we
develop and make public a benchmark dataset (called TextEdge) that contains
text descriptions of crystal structures with their properties. We then propose
LLM-Prop, a method that leverages the general-purpose learning capabilities of
large language models (LLMs) to predict the physical and electronic properties
of crystals from their text descriptions. LLM-Prop outperforms the current
state-of-the-art GNN-based crystal property predictor by about 4% in predicting
band gap, 3% in classifying whether the band gap is direct or indirect, and 66%
in predicting unit cell volume. LLM-Prop also outperforms a finetuned MatBERT,
a domain-specific pre-trained BERT model, despite having 3 times fewer
parameters. Our empirical results may highlight the current inability of GNNs
to capture information pertaining to space group symmetry and Wyckoff sites for
accurate crystal property prediction.
|
[{'version': 'v1', 'created': 'Sat, 21 Oct 2023 14:49:58 GMT'}]
|
2023-10-24
|
Victor Hoffmann (1), Ilias Nahmed (1), Parisa Rastin (1 and 2),
Gu\'ena\"el Cabanes (3), Julien Boisse (4) ((1) ENSMN, (2) LORIA UMR 7503,
(3) LIPN UMR 7030, (4) LEMTA UMR 7563)
|
Deep Learning Approaches for Dynamic Mechanical Analysis of Viscoelastic
Fiber Composites
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.AI
|
The increased adoption of reinforced polymer (RP) composite materials, driven
by eco-design standards, calls for a fine balance between lightness, stiffness,
and effective vibration control. These materials are integral to enhancing
comfort, safety, and energy efficiency. Dynamic Mechanical Analysis (DMA)
characterizes viscoelastic behavior, yet there's a growing interest in using
Machine Learning (ML) to expedite the design and understanding of
microstructures. In this paper we aim to map microstructures to their
mechanical properties using deep neural networks, speeding up the process and
allowing for the generation of microstructures from desired properties.
|
[{'version': 'v1', 'created': 'Fri, 20 Oct 2023 23:33:27 GMT'}]
|
2023-10-25
|
Ashiqur Rasul, Md Shafayat Hossain, Ankan Ghosh Dastider, Himaddri
Roy, M. Zahid Hasan, Quazi D. M. Khosru
|
Topological, or Non-topological? A Deep Learning Based Prediction
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Prediction and discovery of new materials with desired properties are at the
forefront of quantum science and technology research. A major bottleneck in
this field is the computational resources and time complexity related to
finding new materials from ab initio calculations. In this work, an effective
and robust deep learning-based model is proposed by incorporating persistent
homology and graph neural network which offers an accuracy of 91.4% and an F1
score of 88.5% in classifying topological vs. non-topological materials,
outperforming the other state-of-the-art classifier models. The incorporation
of the graph neural network encodes the underlying relation between the atoms
into the model based on their own crystalline structures and thus proved to be
an effective method to represent and process non-euclidean data like molecules
with a relatively shallow network. The persistent homology pipeline in the
suggested neural network is capable of integrating the atom-specific
topological information into the deep learning model, increasing robustness,
and gain in performance. It is believed that the presented work will be an
efficacious tool for predicting the topological class and therefore enable the
high-throughput search for novel materials in this field.
|
[{'version': 'v1', 'created': 'Sun, 29 Oct 2023 05:29:49 GMT'}]
|
2023-10-31
|
Indrashish Saha, Ashwini Gupta, Lori Graham-Brady
|
Prediction of local elasto-plastic stress and strain fields in a
two-phase composite microstructure using a deep convolutional neural network
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Design and analysis of inelastic materials requires prediction of physical
responses that evolve under loading. Numerical simulation of such behavior
using finite element (FE) approaches can call for significant time and
computational effort. To address this challenge, this paper demonstrates a deep
learning (DL) framework that is capable of predicting micro-scale
elasto-plastic strains and stresses in a two-phase medium, at a much greater
speed than traditional FE simulations. The proposed framework uses a deep
convolutional neural network (CNN), specifically a U-Net architecture with 3D
operations, to map the composite microstructure to the corresponding stress and
strain fields under a predetermined load path. In particular, the model is
applied to a two-phase fiber reinforced plastic (FRP) composite microstructure
subjected to a given loading-unloading path, predicting the corresponding
stress and strain fields at discrete intermediate load steps. A novel two-step
training approach provides more accurate predictions of stress, by first
training the model to predict strain fields and then using those strain fields
as input to the model that predicts the stress fields. This efficient
data-driven approach enables accurate prediction of physical fields in
inelastic materials, based solely on microstructure images and loading
information.
|
[{'version': 'v1', 'created': 'Sun, 29 Oct 2023 19:34:53 GMT'}]
|
2023-10-31
|
Markus J. Buehler
|
Generative retrieval-augmented ontologic graph and multi-agent
strategies for interpretive large language model-based materials design
| null | null | null |
cs.CL cond-mat.dis-nn cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph
|
Transformer neural networks show promising capabilities, in particular for
uses in materials analysis, design and manufacturing, including their capacity
to work effectively with both human language, symbols, code, and numerical
data. Here we explore the use of large language models (LLMs) as a tool that
can support engineering analysis of materials, applied to retrieving key
information about subject areas, developing research hypotheses, discovery of
mechanistic relationships across disparate areas of knowledge, and writing and
executing simulation codes for active knowledge generation based on physical
ground truths. When used as sets of AI agents with specific features,
capabilities, and instructions, LLMs can provide powerful problem solution
strategies for applications in analysis and design problems. Our experiments
focus on using a fine-tuned model, MechGPT, developed based on training data in
the mechanics of materials domain. We first affirm how finetuning endows LLMs
with reasonable understanding of domain knowledge. However, when queried
outside the context of learned matter, LLMs can have difficulty to recall
correct information. We show how this can be addressed using
retrieval-augmented Ontological Knowledge Graph strategies that discern how the
model understands what concepts are important and how they are related.
Illustrated for a use case of relating distinct areas of knowledge - here,
music and proteins - such strategies can also provide an interpretable graph
structure with rich information at the node, edge and subgraph level. We
discuss nonlinear sampling strategies and agent-based modeling applied to
complex question answering, code generation and execution in the context of
automated force field development from actively learned Density Functional
Theory (DFT) modeling, and data analysis.
|
[{'version': 'v1', 'created': 'Mon, 30 Oct 2023 20:31:50 GMT'}]
|
2023-11-01
|
Ze-Feng Gao, Shuai Qu, Bocheng Zeng, Yang Liu, Ji-Rong Wen, Hao Sun,
Peng-Jie Guo and Zhong-Yi Lu
|
AI-accelerated Discovery of Altermagnetic Materials
|
National Science Review, Volume 12, Issue 4, April 2025
|
10.1093/nsr/nwaf066
| null |
cond-mat.mtrl-sci cs.AI physics.comp-ph
|
Altermagnetism, a new magnetic phase, has been theoretically proposed and
experimentally verified to be distinct from ferromagnetism and
antiferromagnetism. Although altermagnets have been found to possess many
exotic physical properties, the limited availability of known altermagnetic
materials hinders the study of such properties. Hence, discovering more types
of altermagnetic materials with different properties is crucial for a
comprehensive understanding of altermagnetism and thus facilitating new
applications in the next generation information technologies, e.g., storage
devices and high-sensitivity sensors. Since each altermagnetic material has a
unique crystal structure, we propose an automated discovery approach empowered
by an AI search engine that employs a pre-trained graph neural network to learn
the intrinsic features of the material crystal structure, followed by
fine-tuning a classifier with limited positive samples to predict the
altermagnetism probability of a given material candidate. Finally, we
successfully discovered 50 new altermagnetic materials that cover metals,
semiconductors, and insulators confirmed by the first-principles electronic
structure calculations. The wide range of electronic structural characteristics
reveals that various novel physical properties manifest in these newly
discovered altermagnetic materials, e.g., anomalous Hall effect, anomalous Kerr
effect, and topological property. Noteworthy, we discovered 4 $i$-wave
altermagnetic materials for the first time. Overall, the AI search engine
performs much better than human experts and suggests a set of new altermagnetic
materials with unique properties, outlining its potential for accelerated
discovery of the materials with targeted properties.
|
[{'version': 'v1', 'created': 'Wed, 8 Nov 2023 01:06:48 GMT'}, {'version': 'v2', 'created': 'Mon, 13 Nov 2023 02:53:04 GMT'}, {'version': 'v3', 'created': 'Tue, 23 Jul 2024 05:50:15 GMT'}, {'version': 'v4', 'created': 'Tue, 13 May 2025 08:00:39 GMT'}]
|
2025-05-14
|
A. Dana, L. Mu, S. Gelin, S. B. Sinnott, I. Dabo
|
Cluster expansion by transfer learning for phase stability predictions
| null | null | null |
cond-mat.mtrl-sci
|
Recent progress towards universal machine-learned interatomic potentials
holds considerable promise for materials discovery. Yet the accuracy of these
potentials for predicting phase stability may still be limited. In contrast,
cluster expansions provide accurate phase stability predictions but are
computationally demanding to parameterize from first principles, especially for
structures of low dimension or with a large number of components, such as
interfaces or multimetal catalysts. We overcome this trade-off via transfer
learning. Using Bayesian inference, we incorporate prior statistical knowledge
from machine-learned and physics-based potentials, enabling us to sample the
most informative configurations and to efficiently fit first-principles cluster
expansions. This algorithm is tested on Pt:Ni, showing robust convergence of
the mixing energies as a function of sample size with reduced statistical
fluctuations.
|
[{'version': 'v1', 'created': 'Fri, 10 Nov 2023 16:52:51 GMT'}, {'version': 'v2', 'created': 'Mon, 13 Nov 2023 20:07:32 GMT'}, {'version': 'v3', 'created': 'Wed, 15 Nov 2023 13:50:47 GMT'}, {'version': 'v4', 'created': 'Wed, 1 May 2024 18:31:32 GMT'}]
|
2024-05-03
|
Lei Zhang, Markus Stricker
|
MatNexus: A Comprehensive Text Mining and Analysis Suite for Materials
Discover
| null |
10.1016/j.softx.2024.101654
| null |
cond-mat.mtrl-sci cs.CL physics.chem-ph
|
MatNexus is a specialized software for the automated collection, processing,
and analysis of text from scientific articles. Through an integrated suite of
modules, the MatNexus facilitates the retrieval of scientific articles,
processes textual data for insights, generates vector representations suitable
for machine learning, and offers visualization capabilities for word
embeddings. With the vast volume of scientific publications, MatNexus stands
out as an end-to-end tool for researchers aiming to gain insights from
scientific literature in material science, making the exploration of materials,
such as the electrocatalyst examples we show here, efficient and insightful.
|
[{'version': 'v1', 'created': 'Tue, 7 Nov 2023 14:14:36 GMT'}]
|
2024-03-21
|
Bo Ni and Markus J. Buehler
|
MechAgents: Large language model multi-agent collaborations can solve
mechanics problems, generate new data, and integrate knowledge
| null | null | null |
cs.AI cond-mat.dis-nn cond-mat.mtrl-sci cs.CL cs.LG
|
Solving mechanics problems using numerical methods requires comprehensive
intelligent capability of retrieving relevant knowledge and theory,
constructing and executing codes, analyzing the results, a task that has thus
far mainly been reserved for humans. While emerging AI methods can provide
effective approaches to solve end-to-end problems, for instance via the use of
deep surrogate models or various data analytics strategies, they often lack
physical intuition since knowledge is baked into the parametric complement
through training, offering less flexibility when it comes to incorporating
mathematical or physical insights. By leveraging diverse capabilities of
multiple dynamically interacting large language models (LLMs), we can overcome
the limitations of conventional approaches and develop a new class of
physics-inspired generative machine learning platform, here referred to as
MechAgents. A set of AI agents can solve mechanics tasks, here demonstrated for
elasticity problems, via autonomous collaborations. A two-agent team can
effectively write, execute and self-correct code, in order to apply finite
element methods to solve classical elasticity problems in various flavors
(different boundary conditions, domain geometries, meshes, small/finite
deformation and linear/hyper-elastic constitutive laws, and others). For more
complex tasks, we construct a larger group of agents with enhanced division of
labor among planning, formulating, coding, executing and criticizing the
process and results. The agents mutually correct each other to improve the
overall team-work performance in understanding, formulating and validating the
solution. Our framework shows the potential of synergizing the intelligence of
language models, the reliability of physics-based modeling, and the dynamic
collaborations among diverse agents, opening novel avenues for automation of
solving engineering problems.
|
[{'version': 'v1', 'created': 'Tue, 14 Nov 2023 13:49:03 GMT'}]
|
2023-11-15
|
Lalit Yadav
|
Atoms as Words: A Novel Approach to Deciphering Material Properties
using NLP-inspired Machine Learning on Crystallographic Information Files
(CIFs)
| null | null | null |
cond-mat.mtrl-sci
|
In condensed matter physics and materials science, predicting material
properties necessitates understanding intricate many-body interactions.
Conventional methods such as density functional theory (DFT) and molecular
dynamics (MD) often resort to simplifying approximations and are
computationally expensive. Meanwhile, recent machine learning methods use
handcrafted descriptors for material representation which sometimes neglect
vital crystallographic information and are often limited to single property
prediction or a sub-class of crystal structures. In this study, we pioneer an
unsupervised strategy, drawing inspiration from Natural Language Processing
(NLP), to harness the underutilized potential of Crystallographic Information
Files (CIFs). We conceptualize atoms and atomic positions within a CIF
similarly to words in textual content. Using a Word2Vec-inspired technique, we
produce atomic embeddings that capture intricate atomic relationships. Our
model, CIFSemantics, trained on the extensive Material Project dataset, adeptly
predicts 15 distinct material properties from the CIFs. Its performance rivals
specialized models, marking a significant step forward in material property
predictions.
|
[{'version': 'v1', 'created': 'Thu, 16 Nov 2023 02:15:29 GMT'}]
|
2023-11-17
|
Sudarson Roy Pratihar, Deepesh Pai, Manaswita Nag
|
AIMS-EREA -- A framework for AI-accelerated Innovation of Materials for
Sustainability -- for Environmental Remediation and Energy Applications
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
Many environmental remediation and energy applications (conversion and
storage) for sustainability need design and development of green novel
materials. Discovery processes of such novel materials are time taking and
cumbersome due to large number of possible combinations and permutations of
materials structures. Often theoretical studies based on Density Functional
Theory (DFT) and other theories, coupled with Simulations are conducted to
narrow down sample space of candidate materials, before conducting
laboratory-based synthesis and analytical process. With the emergence of
artificial intelligence (AI), AI techniques are being tried in this process too
to ease out simulation time and cost. However tremendous values of previously
published research from various parts of the world are still left as
labor-intensive manual effort and discretion of individual researcher and prone
to human omissions. AIMS-EREA is our novel framework to blend best of breed of
Material Science theory with power of Generative AI to give best impact and
smooth and quickest discovery of material for sustainability. This also helps
to eliminate the possibility of production of hazardous residues and
bye-products of the reactions. AIMS-EREA uses all available resources --
Predictive and Analytical AI on large collection of chemical databases along
with automated intelligent assimilation of deep materials knowledge from
previously published research works through Generative AI. We demonstrate use
of our own novel framework with an example, how this framework can be
successfully applied to achieve desired success in development of
thermoelectric material for waste heat conversion.
|
[{'version': 'v1', 'created': 'Sat, 18 Nov 2023 12:35:45 GMT'}]
|
2023-11-21
|
Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na,
Chanyoung Park
|
Density of States Prediction of Crystalline Materials via Prompt-guided
Multi-Modal Transformer
| null | null | null |
cond-mat.mtrl-sci cs.AI cs.LG
|
The density of states (DOS) is a spectral property of crystalline materials,
which provides fundamental insights into various characteristics of the
materials. While previous works mainly focus on obtaining high-quality
representations of crystalline materials for DOS prediction, we focus on
predicting the DOS from the obtained representations by reflecting the nature
of DOS: DOS determines the general distribution of states as a function of
energy. That is, DOS is not solely determined by the crystalline material but
also by the energy levels, which has been neglected in previous works. In this
paper, we propose to integrate heterogeneous information obtained from the
crystalline materials and the energies via a multi-modal transformer, thereby
modeling the complex relationships between the atoms in the crystalline
materials and various energy levels for DOS prediction. Moreover, we propose to
utilize prompts to guide the model to learn the crystal structural
system-specific interactions between crystalline materials and energies.
Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS,
with various real-world scenarios demonstrate the superiority of
DOSTransformer. The source code for DOSTransformer is available at
https://github.com/HeewoongNoh/DOSTransformer.
|
[{'version': 'v1', 'created': 'Tue, 24 Oct 2023 13:43:17 GMT'}, {'version': 'v2', 'created': 'Thu, 23 Nov 2023 02:00:09 GMT'}]
|
2023-11-27
|
Ehsan Ghane, Martin Fagerstr\"om, and Mohsen Mirkhalaf
|
Recurrent neural networks and transfer learning for elasto-plasticity in
woven composites
| null |
10.1016/j.euromechsol.2024.105378
| null |
cond-mat.mtrl-sci cs.LG
|
As a surrogate for computationally intensive meso-scale simulation of woven
composites, this article presents Recurrent Neural Network (RNN) models.
Leveraging the power of transfer learning, the initialization challenges and
sparse data issues inherent in cyclic shear strain loads are addressed in the
RNN models. A mean-field model generates a comprehensive data set representing
elasto-plastic behavior. In simulations, arbitrary six-dimensional strain
histories are used to predict stresses under random walking as the source task
and cyclic loading conditions as the target task. Incorporating sub-scale
properties enhances RNN versatility. In order to achieve accurate predictions,
the model uses a grid search method to tune network architecture and
hyper-parameter configurations. The results of this study demonstrate that
transfer learning can be used to effectively adapt the RNN to varying strain
conditions, which establishes its potential as a useful tool for modeling
path-dependent responses in woven composites.
|
[{'version': 'v1', 'created': 'Wed, 22 Nov 2023 14:47:54 GMT'}, {'version': 'v2', 'created': 'Thu, 7 Dec 2023 14:59:02 GMT'}]
|
2024-07-08
|
Mohamed Bilal Shakeel, Samir Brahim Belhaouari and Fedwa El Mellouhi
|
Automated Model Training (AMT) GUI: An Opportunity for integrating AI in
the Laboratory Experiment
| null | null | null |
cond-mat.mtrl-sci
|
In the field of materials science, comprehending material properties is often
hindered by the complexity of datasets originating from various sources. This
study introduces the Automated Model Training (AMT) Graphical User Interface
(GUI), specifically crafted for the use of researchers and scientists without a
programming background to design the next set of experiments either in a
chemistry lab or on the computer. The GUI integrates diverse machine learning
models, such as XG-Boost, Random Forest, Support Vector Regression, Linear
Regression, Generalized Additive Model (GAM), and Stack Regressors, offering a
robust toolkit for data analysis. It facilitates the exploration of complex
relationships, non-linear patterns, and predictive accuracy optimization. To
further enhance its utility, the GUI integrates the Particle Swarm Optimization
(PSO) technique, allowing researchers to systematically explore vast parameter
spaces and identify optimal experimental conditions. This synergy between
machine learning and PSO empowers material scientists through a user-friendly
platform for data-driven discovery. The AMT GUI bridges the gap between
traditional experimentation and machine learning, enabling precise and
efficient exploration of the materials research space.
|
[{'version': 'v1', 'created': 'Thu, 23 Nov 2023 04:59:29 GMT'}, {'version': 'v2', 'created': 'Sun, 4 Aug 2024 17:33:54 GMT'}]
|
2024-08-06
|
Kangshu Li, Xiaocang Han, Yanhui Hong, Yuan Meng, Xiang Chen, Junxian
Li, Jing-Yang You, Lin Yao, Wenchao Hu, Zhiyi Xia, Guolin Ke, Linfeng Zhang,
Jin Zhang, Xiaoxu Zhao
|
Single-image based deep learning for precise atomic defects
identification
| null | null | null |
cond-mat.mtrl-sci
|
Defect engineering has been profoundly employed to confer desirable
functionality to materials that pristine lattices inherently lack. Although
single atomic-resolution scanning transmission electron microscopy (STEM)
images are widely accessible for defect engineering, harnessing atomic-scale
images containing various defects through traditional image analysis methods is
hindered by random noise and human bias. Yet the rise of deep learning (DL)
offering an alternative approach, its widespread application is primarily
restricted by the need for large amounts of training data with labeled ground
truth. In this study, we propose a two-stage method to address the problems of
high annotation cost and image noise in the detection of atomic defects in
monolayer 2D materials. In the first stage, to tackle the issue of data
scarcity, we employ a two-state transformation network based on U-GAT-IT for
adding realistic noise to simulated images with pre-located ground truth
labels, thereby infinitely expanding the training dataset. In the second stage,
atomic defects in monolayer 2D materials are effectively detected with high
accuracy using U-Net models trained with the data generated in the first stage,
avoiding random noise and human bias issues. In both stages, we utilize
segmented unit-cell-level images to simplify the model's task and enhance its
accuracy. Our results demonstrate that not only sulfur vacancies, we are also
able to visualize oxygen dopants in monolayer MoS2, which are usually
overwhelmed by random background noise. As the training was based on a few
segmented unit-cell-level realistic images, this method can be readily extended
to other 2D materials. Therefore, our results outline novel ways to train the
model with minimized datasets, offering great opportunities to fully exploit
the power of machine learning (ML) applicable to a broad materials science
community.
|
[{'version': 'v1', 'created': 'Sat, 25 Nov 2023 05:53:34 GMT'}]
|
2023-11-28
|
Takahiro Ishikawa, Yuta Tanaka, and Shinji Tsuneyuki
|
Evolutionary search for superconducting phases in the
lanthanum-nitrogen-hydrogen system with universal neural network potential
| null | null | null |
cond-mat.supr-con cond-mat.mtrl-sci
|
Recently, Grockowiak $\textit{et al.}$ reported "hot superconductivity" in
ternary or multinary compounds based on lanthanum hydride [A. D. Grockowiak
$\textit{et al.}$, Front. Electron. Mater. $\textbf{2}$, 837651 (2022)]. In
this paper, we explored thermodynamically stable phases and superconducting
phases in the lanthanum-nitrogen-hydrogen system (La$_{x}$N$_{y}$H$_{1-x-y}$,
$0 \leq x \leq 1$, $0 \leq y \leq 1$) at pressure of 20$\,$GP. We rapidly and
accurately constructed the formation-enthalpy convex hull using an evolutionary
construction scheme based on density functional theory calculations, extracting
the candidates for stable and moderately metastable compounds by the universal
neural network potential calculations. The convex hull diagram shows that more
than fifty compounds emerge as stable and moderately metastable phases in the
region of $\Delta H \leq 4.4$$\,$mRy/atom. In particular, the compounds are
concentrated on the line of $x = 0.5$ connecting between LaH and LaN. We found
that the superconductivity is gradually enhanced due to N doping for LaH and
the superconducting critical temperature $T_{\rm c}$ reaches 8.77$\,$K in
La$_2$NH with $y = 0.25$. In addition, we predicted that metastable
La$_2$NH$_2$ shows the highest $T_{\rm c}$ value, 14.41$\,$K, of all the
ternary compounds predicted in this study. These results suggest that it is
difficult to obtain the hot superconductivity in the La-H compounds with N at
20$\,$GPa.
|
[{'version': 'v1', 'created': 'Sun, 3 Dec 2023 05:28:40 GMT'}]
|
2023-12-05
|
Yiwen Zheng, Prakash Thakolkaran, Agni K. Biswal, Jake A. Smith,
Ziheng Lu, Shuxin Zheng, Bichlien H. Nguyen, Siddhant Kumar, Aniruddh
Vashisth
|
AI-guided inverse design and discovery of recyclable vitrimeric polymers
|
Advanced Science (2024): 2411385
|
10.1002/advs.202411385
| null |
cond-mat.mtrl-sci cs.LG
|
Vitrimer is a new, exciting class of sustainable polymers with the ability to
heal due to their dynamic covalent adaptive network that can go through
associative rearrangement reactions. However, a limited choice of constituent
molecules restricts their property space, prohibiting full realization of their
potential applications. To overcome this challenge, we couple molecular
dynamics (MD) simulations and a novel graph variational autoencoder (VAE)
machine learning model for inverse design of vitrimer chemistries with desired
glass transition temperature (Tg) and synthesize a novel vitrimer polymer. We
build the first vitrimer dataset of one million chemistries and calculate Tg on
8,424 of them by high-throughput MD simulations calibrated by a Gaussian
process model. The proposed novel VAE employs dual graph encoders and a latent
dimension overlapping scheme which allows for individual representation of
multi-component vitrimers. By constructing a continuous latent space containing
necessary information of vitrimers, we demonstrate high accuracy and efficiency
of our framework in discovering novel vitrimers with desirable Tg beyond the
training regime. To validate the effectiveness of our framework in experiments,
we generate novel vitrimer chemistries with a target Tg = 323 K. By
incorporating chemical intuition, we synthesize a vitrimer with Tg of 311-317
K, and experimentally demonstrate healability and flowability. The proposed
framework offers an exciting tool for polymer chemists to design and synthesize
novel, sustainable vitrimer polymers for a facet of applications.
|
[{'version': 'v1', 'created': 'Wed, 6 Dec 2023 18:53:45 GMT'}, {'version': 'v2', 'created': 'Wed, 13 Mar 2024 12:04:36 GMT'}, {'version': 'v3', 'created': 'Wed, 14 Aug 2024 03:25:29 GMT'}, {'version': 'v4', 'created': 'Fri, 6 Sep 2024 05:02:37 GMT'}]
|
2025-01-06
|
Jonathan D Hollenbach, Cassandra M Pate, Haili Jia, James L Hart,
Paulette Clancy, Mitra L Taheri
|
Embedding theory in ML toward real-time tracking of structural dynamics
through hyperspectral datasets
| null | null | null |
cond-mat.mtrl-sci
|
In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental technique
that has traditionally been used to understand how the choice of materials
processing has the ability to change local structure and composition. However,
more recent advances to observe and react to transient changes occurring at the
ultrafast timescales that are now possible with EELS and Transmission Electron
Microscopy (TEM) will require new frameworks for characterization and analysis.
We describe a machine learning (ML) framework for the rapid assessment and
characterization of in operando EELS Spectrum Images (EELS-SI) without the need
for many labeled training datapoints as typically required for deep learning
classification methods. By embedding computationally generated structures and
experimental datasets into an equivalent latent space through Variational
Autoencoders (VAE), we effectively predict the structural changes at latency
scales relevant to closed-loop processing within the TEM. The framework
described in this study is a critical step in enabling automated, on-the-fly
synthesis and characterization which will greatly advance capabilities for
materials discovery and precision engineering of functional materials at the
atomic scale.
|
[{'version': 'v1', 'created': 'Fri, 8 Dec 2023 17:43:08 GMT'}]
|
2023-12-11
|
Ken-ichi Nomura, Ankit Mishra, Tian Sang, Rajiv K. Kalia, Aiichiro
Nakano, Priya Vashishta
|
Molecular Autonomous Pathfinder using Deep Reinforcement Learning
| null | null | null |
cond-mat.mtrl-sci
|
Diffusion in solids is a slow process that dictates rate-limiting processes
in key chemical reactions. Unlike crystalline solids that offer well-defined
diffusion pathways, the lack of similar structural motifs in amorphous or
glassy materials poses a great scientific challenge in estimating slow
diffusion time. To tackle this problem, we have developed an AI-guided
long-time atomistic simulation approach: Molecular Autonomous Pathfinder (MAP)
framework based on Deep Reinforcement Learning (RL), where RL agent is trained
to uncover energy efficient diffusion pathways. We employ Deep Q-Network
architecture with distributed prioritized replay buffer enabling fully online
agent training with accelerated experience sampling by an ensemble of
asynchronous agents. After training, the agents provide atomistic
configurations of diffusion pathways with their energy profile. We use a
piecewise Nudged Elastic Band to refine the energy profile of the obtained
pathway and corresponding diffusion time on the basis of transition state
theory. With MAP, we have successfully identified atomistic mechanisms along
molecular diffusion pathways in amorphous silica, with time scales comparable
to experiments.
|
[{'version': 'v1', 'created': 'Sat, 9 Dec 2023 03:09:20 GMT'}]
|
2023-12-12
|
Zhiling Zheng, Zhiguo He, Omar Khattab, Nakul Rampal, Matei A.
Zaharia, Christian Borgs, Jennifer T. Chayes, Omar M. Yaghi
|
Image and Data Mining in Reticular Chemistry Using GPT-4V
| null | null | null |
cs.AI cond-mat.mtrl-sci cs.CV cs.IR
|
The integration of artificial intelligence into scientific research has
reached a new pinnacle with GPT-4V, a large language model featuring enhanced
vision capabilities, accessible through ChatGPT or an API. This study
demonstrates the remarkable ability of GPT-4V to navigate and obtain complex
data for metal-organic frameworks, especially from graphical sources. Our
approach involved an automated process of converting 346 scholarly articles
into 6240 images, which represents a benchmark dataset in this task, followed
by deploying GPT-4V to categorize and analyze these images using natural
language prompts. This methodology enabled GPT-4V to accurately identify and
interpret key plots integral to MOF characterization, such as nitrogen
isotherms, PXRD patterns, and TGA curves, among others, with accuracy and
recall above 93%. The model's proficiency in extracting critical information
from these plots not only underscores its capability in data mining but also
highlights its potential in aiding the creation of comprehensive digital
databases for reticular chemistry. In addition, the extracted nitrogen isotherm
data from the selected literature allowed for a comparison between theoretical
and experimental porosity values for over 200 compounds, highlighting certain
discrepancies and underscoring the importance of integrating computational and
experimental data. This work highlights the potential of AI in accelerating
scientific discovery and innovation, bridging the gap between computational
tools and experimental research, and paving the way for more efficient,
inclusive, and comprehensive scientific inquiry.
|
[{'version': 'v1', 'created': 'Sat, 9 Dec 2023 05:05:25 GMT'}]
|
2023-12-12
|
Sumner B. Harris, Christopher M. Rouleau, Kai Xiao, Rama K. Vasudevan
|
Deep learning with plasma plume image sequences for anomaly detection
and prediction of growth kinetics during pulsed laser deposition
|
npj Computational Materials 10, 105 (2024)
|
10.1038/s41524-024-01275-w
| null |
cond-mat.mtrl-sci
|
Materials synthesis platforms that are designed for autonomous
experimentation are capable of collecting multimodal diagnostic data that can
be utilized for feedback to optimize material properties. Pulsed laser
deposition (PLD) is emerging as a viable autonomous synthesis tool, and so the
need arises to develop machine learning (ML) techniques that are capable of
extracting information from in situ diagnostics. Here, we demonstrate that
intensified-CCD image sequences of the plasma plume generated during PLD can be
used for anomaly detection and the prediction of thin film growth kinetics. We
developed a multi-output (2$+$1)D convolutional neural network regression model
that extracts deep features from plume dynamics that not only correlate with
the measured chamber pressure and incident laser energy, but more importantly,
predict parameters of an auto-catalytic film growth model derived from in situ
laser reflectivity experiments. Our results are the first demonstration of how
ML with in situ plume diagnostics data in PLD can be utilized to maintain
deposition conditions in an optimal regime. Further, the predictive
capabilities of plume dynamics on the kinetics of film growth or other film
properties prior to deposition provides a means for rapid pre-screening of
growth conditions for the non-expert, which promises to accelerate materials
optimization with PLD.
|
[{'version': 'v1', 'created': 'Thu, 14 Dec 2023 17:04:06 GMT'}]
|
2024-11-01
|
Amirhossein D. Naghdi, Franco Pellegrini, Emine K\"u\c{c}\"ukbenli,
Dario Massa, F. Javier Dominguez Gutierrez, Efthimios Kaxiras, Stefanos
Papanikolaou
|
Neural Network Interatomic Potentials For Open Surface Nano-mechanics
Applications
| null | null | null |
cond-mat.mtrl-sci
|
Material characterization in nano-mechanical tests requires precise
interatomic potentials for the computation of atomic energies and forces with
near-quantum accuracy. For such purposes, we develop a robust neural-network
interatomic potential (NNIP), and we provide a test for the example of
molecular dynamics (MD) nanoindentation, and the case of body-centered cubic
crystalline molybdenum (Mo). We employ a similarity measurement protocol, using
standard local environment descriptors, to select ab initio configurations for
the training dataset that capture the behavior of the indented sample. We find
that it is critical to include generalized stacking fault (GSF) configurations,
featuring a dumbbell interstitial on the surface, to capture dislocation cores,
and also high-temperature configurations with frozen atom layers for the
indenter tip contact. We develop a NNIP with distinct dislocation nucleation
mechanisms, realistic generalized stacking fault energy (GSFE) curves, and an
informative energy landscape for the atoms on the sample surface during
nanoindentation. We compare our NNIP results with nanoindentation simulations,
performed with three existing potentials -- an embedded atom method (EAM)
potential, a gaussian approximation potential (GAP), and a tabulated GAP
(tabGAP) potential -- that predict different dislocation nucleation mechanisms,
and display the absence of essential information on the shear stress at the
sample surface in the elastic region. We believe that these features render
specialized NNIPs essential for simulations of nanoindentation and
nano-mechanics with near-quantum accuracy.
|
[{'version': 'v1', 'created': 'Mon, 18 Dec 2023 00:17:56 GMT'}]
|
2023-12-19
|
Daniel Wines, Kamal Choudhary
|
Data-driven Design of High Pressure Hydride Superconductors using DFT
and Deep Learning
| null |
10.1088/2752-5724/ad4a94
| null |
cond-mat.mtrl-sci cond-mat.supr-con
|
The observation of superconductivity in hydride-based materials under
ultrahigh pressures (for example, H$_3$S and LaH$_{10}$) has fueled the
interest in a more data-driven approach to discovering new high-pressure
hydride superconductors. In this work, we performed density functional theory
(DFT) calculations to predict the critical temperature ($T_c$) of over 900
hydride materials under a pressure range of (0 to 500) GPa, where we found 122
dynamically stable structures with a $T_c$ above MgB$_2$ (39 K). To accelerate
screening, we trained a graph neural network (GNN) model to predict $T_c$ and
demonstrated that a universal machine learned force-field can be used to relax
hydride structures under arbitrary pressures, with significantly reduced cost.
By combining DFT and GNNs, we can establish a more complete map of hydrides
under pressure.
|
[{'version': 'v1', 'created': 'Wed, 20 Dec 2023 01:40:24 GMT'}, {'version': 'v2', 'created': 'Thu, 21 Dec 2023 04:07:32 GMT'}, {'version': 'v3', 'created': 'Sat, 2 Mar 2024 18:08:58 GMT'}, {'version': 'v4', 'created': 'Fri, 31 May 2024 18:03:33 GMT'}]
|
2024-06-04
|
Xinyang Dong, Emanuel Gull, Lei Wang
|
Equivariant neural network for Green's functions of molecules and
materials
|
Phys. Rev. B 109, 075112 (2024)
|
10.1103/PhysRevB.109.075112
| null |
physics.chem-ph cond-mat.mtrl-sci physics.comp-ph
|
The many-body Green's function provides access to electronic properties
beyond density functional theory level in ab inito calculations. In this
manuscript, we propose a deep learning framework for predicting the
finite-temperature Green's function in atomic orbital space, aiming to achieve
a balance between accuracy and efficiency. By predicting the self-energy
matrices in Lehmann representation using an equivariant message passing neural
network, our method respects its analytical property and the $E(3)$
equivariance. The Green's function is obtained from the predicted self-energy
through Dyson equation with target total number of electrons. We present
proof-of-concept benchmark results for both molecules and simple periodic
systems, showing that our method is able to provide accurate estimate of
physical observables such as energy and density of states based on the
predicted Green's function.
|
[{'version': 'v1', 'created': 'Fri, 22 Dec 2023 13:30:49 GMT'}, {'version': 'v2', 'created': 'Mon, 19 Feb 2024 13:04:33 GMT'}]
|
2024-02-20
|
Shi Yin, Xinyang Pan, Xudong Zhu, Tianyu Gao, Haochong Zhang, Feng Wu,
Lixin He
|
Towards Harmonization of SO(3)-Equivariance and Expressiveness: a Hybrid
Deep Learning Framework for Electronic-Structure Hamiltonian Prediction
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci cs.LG
|
Deep learning for predicting the electronic-structure Hamiltonian of quantum
systems necessitates satisfying the covariance laws, among which achieving
SO(3)-equivariance without sacrificing the non-linear expressive capability of
networks remains unsolved. To navigate the harmonization between equivariance
and expressiveness, we propose a deep learning method synergizing two distinct
categories of neural mechanisms as a two-stage encoding and regression
framework. The first stage corresponds to group theory-based neural mechanisms
with inherent SO(3)-equivariant properties prior to the parameter learning
process, while the second stage is characterized by a non-linear 3D graph
Transformer network we propose, featuring high capability on non-linear
expressiveness. The novel combination lies in the point that, the first stage
predicts baseline Hamiltonians with abundant SO(3)-equivariant features
extracted, assisting the second stage in empirical learning of equivariance;
and in turn, the second stage refines the first stage's output as a
fine-grained prediction of Hamiltonians using powerful non-linear neural
mappings, compensating for the intrinsic weakness on non-linear expressiveness
capability of mechanisms in the first stage. Our method enables precise,
generalizable predictions while capturing SO(3)-equivariance under rotational
transformations, and achieves state-of-the-art performance in Hamiltonian
prediction on six benchmark databases.
|
[{'version': 'v1', 'created': 'Mon, 1 Jan 2024 12:57:15 GMT'}, {'version': 'v10', 'created': 'Mon, 17 Jun 2024 08:08:13 GMT'}, {'version': 'v11', 'created': 'Fri, 21 Jun 2024 07:57:48 GMT'}, {'version': 'v2', 'created': 'Tue, 2 Jan 2024 08:36:58 GMT'}, {'version': 'v3', 'created': 'Wed, 3 Jan 2024 02:17:26 GMT'}, {'version': 'v4', 'created': 'Mon, 15 Jan 2024 14:31:50 GMT'}, {'version': 'v5', 'created': 'Thu, 25 Jan 2024 09:16:15 GMT'}, {'version': 'v6', 'created': 'Fri, 2 Feb 2024 08:45:25 GMT'}, {'version': 'v7', 'created': 'Mon, 8 Apr 2024 07:51:57 GMT'}, {'version': 'v8', 'created': 'Tue, 16 Apr 2024 02:04:29 GMT'}, {'version': 'v9', 'created': 'Sun, 5 May 2024 03:51:17 GMT'}]
|
2024-06-25
|
Kamal Choudhary, Kevin Garrity
|
InterMat: Accelerating Band Offset Prediction in Semiconductor
Interfaces with DFT and Deep Learning
| null |
10.1039/D4DD00031E
| null |
cond-mat.mtrl-sci
|
We introduce a computational framework (InterMat) to predict band offsets of
semiconductor interfaces using density functional theory (DFT) and graph neural
networks (GNN). As a first step, we benchmark OptB88vdW generalized gradient
approximation (GGA) work functions and electron affinities for surfaces against
experimental data with accuracies of 0.29 eV and 0.39 eV, respectively.
Similarly, we evaluate band offset values using independent unit (IU) and
alternate slab junction (ASJ) models leading to accuracies of 0.45 eV and 0.22
eV, respectively. We use bulk band structure calculations with the TBmBJ
meta-GGA functional to correct for band gap underestimation when predicting
conduction band properties. During ASJ structure generation, we use Zur
algorithm along with a unified GNN force-field to tackle the conformation
challenges of interface design. At present, we have 607 surface work functions
calculated with DFT, from which we can compute 183921 IU band offsets as well
as 593 directly calculated ASJ band offsets. Finally, as the space of all
possible heterojunctions is too large to simulate with DFT, we develop
generalized GNN models to quickly predict bulk band edges with an accuracy of
0.26 eV. We show how these models can be used to predict relevant quantities
including ionization potentials, electron affinities, and IU-based band
offsets. We establish simple rules using the above models to pre-screen
potential semiconductor devices from a vast pool of nearly 1.4 trillion
candidate interfaces. InterMat is available at website:
https://github.com/usnistgov/intermat
|
[{'version': 'v1', 'created': 'Thu, 4 Jan 2024 01:41:48 GMT'}, {'version': 'v2', 'created': 'Sun, 26 May 2024 18:38:14 GMT'}]
|
2024-05-28
|
Nihang Fu, Lai Wei, Jianjun Hu
|
Physics guided dual Self-supervised learning for structure-based
materials property prediction
| null | null | null |
cond-mat.mtrl-sci
|
Deep learning (DL) models have now been widely used for high-performance
material property prediction for properties such as formation energy and band
gap. However, training such DL models usually requires a large amount of
labeled data, which is usually not available for most materials properties such
as exfoliation energy and elastic properties. Self-supervised learning (SSL)
methods have been proposed to address this data scarcity issue by learning
inherent representations from unlabeled data in various research fields.
Herein, we present DSSL, a physics-guided Dual SSL framework, for graph neural
networks (GNNs) based material property prediction. This hybrid framework
combines node-masking based predictive SSL with atomic coordinate perturbation
based contrastive SSL strategies, allowing it to learn structural embeddings
that capture both local and global information of input crystals. Especially,
we propose to use predicting the macroproperty (e.g. elasticity) related
microproperty such as atomic stiffness as an additional pretext task to achieve
physics-guided pretraining process. We pretrain our DSSL model on the Materials
Project database with unlabeled data and finetune it with ten extra datasets
with different material properties. The experimental results demonstrate that
teaching neural networks some physics using the SSL strategy can bring up to
26.89\% performance improvement compared to the baseline GNN models. Our source
code is now freely available at https://github.com/usccolumbia/DSSL
|
[{'version': 'v1', 'created': 'Wed, 10 Jan 2024 15:49:44 GMT'}]
|
2024-01-11
|
Janosh Riebesell, T. Wesley Surta, Rhys Goodall, Michael Gaultois,
Alpha A Lee
|
Pushing the Pareto front of band gap and permittivity: ML-guided search
for dielectric materials
| null | null | null |
cond-mat.mtrl-sci cs.AI cs.LG physics.chem-ph
|
Materials with high-dielectric constant easily polarize under external
electric fields, allowing them to perform essential functions in many modern
electronic devices. Their practical utility is determined by two conflicting
properties: high dielectric constants tend to occur in materials with narrow
band gaps, limiting the operating voltage before dielectric breakdown. We
present a high-throughput workflow that combines element substitution, ML
pre-screening, ab initio simulation and human expert intuition to efficiently
explore the vast space of unknown materials for potential dielectrics, leading
to the synthesis and characterization of two novel dielectric materials,
CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective
optimization setting with concave Pareto front. While usually considered more
challenging than single-objective optimization, we argue and show preliminary
evidence that the $1/x$-correlation between band gap and permittivity in fact
makes the task more amenable to ML methods by allowing separate models for band
gap and permittivity to each operate in regions of good training support while
still predicting materials of exceptional merit. To our knowledge, this is the
first instance of successful ML-guided multi-objective materials optimization
achieving experimental synthesis and characterization. CsTaTeO6 is a structure
generated via element substitution not present in our reference data sources,
thus exemplifying successful de-novo materials design. Meanwhile, we report the
first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a
band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of
our multi-objective search.
|
[{'version': 'v1', 'created': 'Thu, 11 Jan 2024 11:38:20 GMT'}]
|
2024-01-12
|
Dario Massa, Grzegorz Kaszuba, Stefanos Papanikolaou and Piotr
Sankowski
|
Transfer Learning in Materials Informatics: structure-property
relationships through minimal but highly informative multimodal input
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
In this work we propose simple, effective and computationally efficient
transfer learning approaches for structure-property relation predictions in the
context of materials, with highly informative input from different modalities.
As materials properties stand from their electronic structure, representations
are extracted directly from datasets of electronic charge density profile
images using Neural Networks. We demonstrate transferability of the existing
pre-trained Convolutional Neural Networks and Large Language Models knowledge
to physics domain data, exploring a wide set of compositions for the regression
of energetics- or structure- related properties, and the role of semantic
crystallographic information in the context of multimodal approaches. We test
the applicability of the CLIP multimodal model, and employ as well a training
protocol for building a more interpretable and versatile stacked custom
solution from different pre-trained modalities. The study offers a promising
avenue for enhancing the effectiveness of descriptor identification in physical
systems, shedding light on the power of multimodal transfer learning for
materials property prediction. Instead of using the well-established GNN-based
approaches, we explore the transfer learning of image- and text-based
architectures, which can impact decision making for new low-cost AI methods in
the field of Materials and Chemoinformatics.
|
[{'version': 'v1', 'created': 'Wed, 17 Jan 2024 16:05:19 GMT'}, {'version': 'v2', 'created': 'Mon, 9 Sep 2024 10:12:45 GMT'}, {'version': 'v3', 'created': 'Tue, 10 Dec 2024 13:01:27 GMT'}]
|
2024-12-11
|
Jiao Huang and Qianli Xing and Jinglong Ji and Bo Yang
|
ADA-GNN: Atom-Distance-Angle Graph Neural Network for Crystal Material
Property Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Property prediction is a fundamental task in crystal material research. To
model atoms and structures, structures represented as graphs are widely used
and graph learning-based methods have achieved significant progress. Bond
angles and bond distances are two key structural information that greatly
influence crystal properties. However, most of the existing works only consider
bond distances and overlook bond angles. The main challenge lies in the time
cost of handling bond angles, which leads to a significant increase in
inference time. To solve this issue, we first propose a crystal structure
modeling based on dual scale neighbor partitioning mechanism, which uses a
larger scale cutoff for edge neighbors and a smaller scale cutoff for angle
neighbors. Then, we propose a novel Atom-Distance-Angle Graph Neural Network
(ADA-GNN) for property prediction tasks, which can process node information and
structural information separately. The accuracy of predictions and inference
time are improved with the dual scale modeling and the specially designed
architecture of ADA-GNN. The experimental results validate that our approach
achieves state-of-the-art results in two large-scale material benchmark
datasets on property prediction tasks.
|
[{'version': 'v1', 'created': 'Mon, 22 Jan 2024 09:03:16 GMT'}]
|
2024-01-23
|
Yongtao Liu, Marti Checa, Rama K. Vasudevan
|
Synergizing Human Expertise and AI Efficiency with Language Model for
Microscopy Operation and Automated Experiment Design
| null | null | null |
cs.HC cond-mat.mtrl-sci
|
With the advent of large language models (LLMs), in both the open source and
proprietary domains, attention is turning to how to exploit such artificial
intelligence (AI) systems in assisting complex scientific tasks, such as
material synthesis, characterization, analysis and discovery. Here, we explore
the utility of LLM, particularly ChatGPT4, in combination with application
program interfaces (APIs) in tasks of experimental design, programming
workflows, and data analysis in scanning probe microscopy, using both in-house
developed API and API given by a commercial vendor for instrument control. We
find that the LLM can be especially useful in converting ideations of
experimental workflows to executable code on microscope APIs. Beyond code
generation, we find that the GPT4 is capable of analyzing microscopy images in
a generic sense. At the same time, we find that GPT4 suffers from inability to
extend beyond basic analyses or more in-depth technical experimental design. We
argue that a LLM specifically fine-tuned for individual scientific domains can
potentially be a better language interface for converting scientific ideations
from human experts to executable workflows, such a synergy between human
expertise and LLM efficiency in experimentation can open new door for
accelerating scientific research, enabling effective experimental protocols
archive and sharing in scientific community.
|
[{'version': 'v1', 'created': 'Wed, 24 Jan 2024 20:45:42 GMT'}]
|
2024-01-26
|
Tony Shi, Mason Ma, Jiajie Wu, Chase Post, Elijah Charles, Tony
Schmitz
|
AFSD-Physics: Exploring the governing equations of temperature evolution
during additive friction stir deposition by a human-AI teaming approach
| null |
10.1016/j.mfglet.2024.09.125
| null |
cs.LG cond-mat.mtrl-sci cs.AI
|
This paper presents a modeling effort to explore the underlying physics of
temperature evolution during additive friction stir deposition (AFSD) by a
human-AI teaming approach. AFSD is an emerging solid-state additive
manufacturing technology that deposits materials without melting. However, both
process modeling and modeling of the AFSD tool are at an early stage. In this
paper, a human-AI teaming approach is proposed to combine models based on first
principles with AI. The resulting human-informed machine learning method,
denoted as AFSD-Physics, can effectively learn the governing equations of
temperature evolution at the tool and the build from in-process measurements.
Experiments are designed and conducted to collect in-process measurements for
the deposition of aluminum 7075 with a total of 30 layers. The acquired
governing equations are physically interpretable models with low computational
cost and high accuracy. Model predictions show good agreement with the
measurements. Experimental validation with new process parameters demonstrates
the model's generalizability and potential for use in tool temperature control
and process optimization.
|
[{'version': 'v1', 'created': 'Mon, 29 Jan 2024 19:17:42 GMT'}]
|
2025-02-11
|
Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Oscar Barrera,
Benjamin Geisler, Peter J. Hirschfeld, Richard G. Hennig
|
Accelerating superconductor discovery through tempered deep learning of
the electron-phonon spectral function
| null |
10.1038/s41524-024-01475-4
| null |
cond-mat.supr-con cond-mat.mtrl-sci cs.LG
|
Integrating deep learning with the search for new electron-phonon
superconductors represents a burgeoning field of research, where the primary
challenge lies in the computational intensity of calculating the
electron-phonon spectral function, $\alpha^2F(\omega)$, the essential
ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this
challenge, we adopt a two-step approach. First, we compute $\alpha^2F(\omega)$
for 818 dynamically stable materials. We then train a deep-learning model to
predict $\alpha^2F(\omega)$, using an unconventional training strategy to
temper the model's overfitting, enhancing predictions. Specifically, we train a
Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET),
obtaining an MAE of 0.21, 45 K, and 43 K for the Eliashberg moments derived
from $\alpha^2F(\omega)$: $\lambda$, $\omega_{\log}$, and $\omega_{2}$,
respectively, yielding an MAE of 2.5 K for the critical temperature, $T_c$.
Further, we incorporate domain knowledge of the site-projected phonon density
of states to impose inductive bias into the model's node attributes and enhance
predictions. This methodological innovation decreases the MAE to 0.18, 29 K,
and 28 K, respectively, yielding an MAE of 2.1 K for $T_c$. We illustrate the
practical application of our model in high-throughput screening for high-$T_c$
materials. The model demonstrates an average precision nearly five times higher
than random screening, highlighting the potential of ML in accelerating
superconductor discovery. BETE-NET accelerates the search for high-$T_c$
superconductors while setting a precedent for applying ML in materials
discovery, particularly when data is limited.
|
[{'version': 'v1', 'created': 'Mon, 29 Jan 2024 22:44:28 GMT'}]
|
2025-01-22
|
Yuxiang Wang, He Li, Zechen Tang, Honggeng Tao, Yanzhen Wang, Zilong
Yuan, Zezhou Chen, Wenhui Duan, Yong Xu
|
DeepH-2: Enhancing deep-learning electronic structure via an equivariant
local-coordinate transformer
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci
|
Deep-learning electronic structure calculations show great potential for
revolutionizing the landscape of computational materials research. However,
current neural-network architectures are not deemed suitable for widespread
general-purpose application. Here we introduce a framework of equivariant
local-coordinate transformer, designed to enhance the deep-learning density
functional theory Hamiltonian referred to as DeepH-2. Unlike previous models
such as DeepH and DeepH-E3, DeepH-2 seamlessly integrates the simplicity of
local-coordinate transformations and the mathematical elegance of equivariant
neural networks, effectively overcoming their respective disadvantages. Based
on our comprehensive experiments, DeepH-2 demonstrates superiority over its
predecessors in both efficiency and accuracy, showcasing state-of-the-art
performance. This advancement opens up opportunities for exploring universal
neural network models or even large materials models.
|
[{'version': 'v1', 'created': 'Tue, 30 Jan 2024 13:51:28 GMT'}]
|
2024-01-31
|
Yuan Chiang, Elvis Hsieh, Chia-Hong Chou, Janosh Riebesell
|
LLaMP: Large Language Model Made Powerful for High-fidelity Materials
Knowledge Retrieval and Distillation
| null | null | null |
cs.CL cond-mat.mtrl-sci cs.AI
|
Reducing hallucination of Large Language Models (LLMs) is imperative for use
in the sciences, where reliability and reproducibility are crucial. However,
LLMs inherently lack long-term memory, making it a nontrivial, ad hoc, and
inevitably biased task to fine-tune them on domain-specific literature and
data. Here we introduce LLaMP, a multimodal retrieval-augmented generation
(RAG) framework of hierarchical reasoning-and-acting (ReAct) agents that can
dynamically and recursively interact with computational and experimental data
on Materials Project (MP) and run atomistic simulations via high-throughput
workflow interface. Without fine-tuning, LLaMP demonstrates strong tool usage
ability to comprehend and integrate various modalities of materials science
concepts, fetch relevant data stores on the fly, process higher-order data
(such as crystal structure and elastic tensor), and streamline complex tasks in
computational materials and chemistry. We propose a simple metric combining
uncertainty and confidence estimates to evaluate the self-consistency of
responses by LLaMP and vanilla LLMs. Our benchmark shows that LLaMP effectively
mitigates the intrinsic bias in LLMs, counteracting the errors on bulk moduli,
electronic bandgaps, and formation energies that seem to derive from mixed data
sources. We also demonstrate LLaMP's capability to edit crystal structures and
run annealing molecular dynamics simulations using pre-trained machine-learning
force fields. The framework offers an intuitive and nearly hallucination-free
approach to exploring and scaling materials informatics, and establishes a
pathway for knowledge distillation and fine-tuning other language models. Code
and live demo are available at https://github.com/chiang-yuan/llamp
|
[{'version': 'v1', 'created': 'Tue, 30 Jan 2024 18:37:45 GMT'}, {'version': 'v2', 'created': 'Sun, 2 Jun 2024 07:50:21 GMT'}, {'version': 'v3', 'created': 'Wed, 9 Oct 2024 20:13:51 GMT'}]
|
2024-10-11
|
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