authors stringlengths 11 2.41k | title stringlengths 38 184 | journal-ref stringclasses 115
values | doi stringlengths 17 34 ⌀ | report-no stringclasses 3
values | categories stringlengths 17 83 | abstract stringlengths 124 1.92k | versions stringlengths 62 689 | update_date stringdate 2007-09-13 00:00:00 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 intr... | [{'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 centere... | [{'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 meth... | [{'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... | [{'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 e... | [{'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
customiz... | [{'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 cons... | [{'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 m... | [{'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. ... | [{'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 o... | [{'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... | 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.
Participan... | [{'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 crit... | [{'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... | [{'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 a... | [{'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 L... | [{'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 po... | [{'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 i... | [{'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 curren... | [{'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... | [{'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 re... | [{'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 ... | [{'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 lattic... | [{'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 d... | [{'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
focuse... | [{'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... | [{'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 ... | [{'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 ... | [{'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 ho... | [{'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
transmissi... | [{'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 addre... | [{'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 m... | [{'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, s... | [{'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 ... | [{'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, ... | [{'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 ... | [{'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 free... | [{'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 effect... | [{'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... | [{'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 ... | [{'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 enc... | [{'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 I... | [{'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), wi... | [{'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... | [{'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... | [{'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, wa... | [{'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.... | [{'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... | [{'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... | [{'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 usin... | [{'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 ... | [{'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 c... | [{'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 th... | [{'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-ba... | [{'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... | [{'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
choice... | [{'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
introduc... | [{'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 comm... | [{'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 art... | [{'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-c... | [{'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) cor... | [{'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... | [{'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 re... | [{'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 ... | [{'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... | [{'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.... | [{'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 withi... | [{'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)
characteriz... | [{'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 dee... | [{'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) fra... | [{'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 ... | [{'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 ... | [{'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 de... | [{'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 ... | [{'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... | [{'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. Meanwhil... | [{'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. Oft... | [{'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 t... | [{'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 ... | [{'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
progr... | [{'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 vario... | [{'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 l... | [{'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 ... | [{'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
ultraf... | [{'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 t... | [{'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-organi... | [{'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 mach... | [{'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) ... | [{'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 pre... | [{'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... | [{'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 expre... | [{'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'}, {'ver... | 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
expe... | [{'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... | [{'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, limit... | [{'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 extr... | [{'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 ... | [{'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 o... | [{'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... | [{'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 o... | [{'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 tr... | [{'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 ... | [{'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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