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stringdate 2007-09-13 00:00:00
2025-05-15 00:00:00
|
|---|---|---|---|---|---|---|---|---|
Nian Wu, Markus Aapro, Joakim S. Jestil\"a, Robert Drost, Miguel
Mart{\i}nez Garc{\i}a, Tomas Torres, Feifei Xiang, Nan Cao, Zhijie He,
Giovanni Bottari, Peter Liljeroth and Adam S. Foster
|
Precise large-scale chemical transformations on surfaces: deep learning
meets scanning probe microscopy with interpretability
| null |
10.1021/jacs.4c14757
| null |
cond-mat.mtrl-sci
|
Scanning Probe Microscopy (SPM) techniques have shown great potential in
fabricating nanoscale structures endowed with exotic quantum properties
achieved through various manipulations of atoms and molecules. However, precise
control requires extensive domain knowledge, which is not necessarily
transferable to new systems and cannot be readily extended to large-scale
operations. Therefore, efficient and autonomous SPM techniques are needed to
learn optimal strategies for new systems, in particular for the challenge of
controlling chemical reactions and hence offering a route to precise atomic and
molecular construction. In this paper, we developed a software infrastructure
named AutoOSS (\textbf{Auto}nomous \textbf{O}n-\textbf{S}urface
\textbf{S}ynthesis) to automate bromine removal from hundreds of
Zn(II)-5,15-bis(4-bromo-2,6-dimethylphenyl)porphyrin (\ch{ZnBr2Me4DPP}) on
Au(111), using neural network models to interpret STM outputs and deep
reinforcement learning models to optimize manipulation parameters. This is
further supported by Bayesian Optimization Structure Search (BOSS) and Density
Functional Theory (DFT) computations to explore 3D structures and reaction
mechanisms based on STM images.
|
[{'version': 'v1', 'created': 'Mon, 30 Sep 2024 07:19:28 GMT'}, {'version': 'v2', 'created': 'Tue, 10 Dec 2024 10:39:20 GMT'}]
|
2024-12-18
|
Brook Wander, Joseph Musielewicz, Raffaele Cheula, John R. Kitchin
|
Accessing Numerical Energy Hessians with Graph Neural Network Potentials
and Their Application in Heterogeneous Catalysis
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Access to the potential energy Hessian enables determination of the Gibbs
free energy, and certain approaches to transition state search and
optimization. Here, we demonstrate that off-the-shelf pretrained Open Catalyst
Project (OCP) machine learned potentials (MLPs) determine the Hessian with
great success (58 cm$^{-1}$ mean absolute error (MAE)) for intermediates
adsorbed to heterogeneous catalyst surfaces. This enables the use of OCP models
for the aforementioned applications. The top performing model, with a simple
offset correction, gives good estimations of the vibrational entropy
contribution to the Gibbs free energy with an MAE of 0.042 eV at 300 K. The
ability to leverage models to capture the translational entropy was also
explored. It was determined that 94% of randomly sampled systems had a
translational entropy greater than 0.1 eV at 300 K. This underscores the need
to go beyond the harmonic approximation to consider the entropy introduced by
adsorbate translation, which increases with temperature. Lastly, we used MLP
determined Hessian information for transition state search and found we were
able to reduce the number of unconverged systems by 65% to 93% overall
convergence, improving on the baseline established by CatTSunami.
|
[{'version': 'v1', 'created': 'Wed, 2 Oct 2024 15:17:10 GMT'}, {'version': 'v2', 'created': 'Sat, 5 Oct 2024 19:24:04 GMT'}]
|
2024-10-08
|
Cong Shen, Yipeng Zhang, Fei Han and Kelin Xia
|
Molecular topological deep learning for polymer property prediction
| null | null | null |
cond-mat.mtrl-sci cs.AI cs.LG
|
Accurate and efficient prediction of polymer properties is of key importance
for polymer design. Traditional experimental tools and density function theory
(DFT)-based simulations for polymer property evaluation, are both expensive and
time-consuming. Recently, a gigantic amount of graph-based molecular models
have emerged and demonstrated huge potential in molecular data analysis. Even
with the great progresses, these models tend to ignore the high-order and
mutliscale information within the data. In this paper, we develop molecular
topological deep learning (Mol-TDL) for polymer property analysis. Our Mol-TDL
incorporates both high-order interactions and multiscale properties into
topological deep learning architecture. The key idea is to represent polymer
molecules as a series of simplicial complices at different scales and build up
simplical neural networks accordingly. The aggregated information from
different scales provides a more accurate prediction of polymer molecular
properties.
|
[{'version': 'v1', 'created': 'Mon, 7 Oct 2024 05:44:02 GMT'}]
|
2024-10-08
|
Sifan Wang, Tong-Rui Liu, Shyam Sankaran, Paris Perdikaris
|
Micrometer: Micromechanics Transformer for Predicting Mechanical
Responses of Heterogeneous Materials
| null | null | null |
cs.CE cond-mat.mtrl-sci cs.LG physics.comp-ph
|
Heterogeneous materials, crucial in various engineering applications, exhibit
complex multiscale behavior, which challenges the effectiveness of traditional
computational methods. In this work, we introduce the Micromechanics
Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for
predicting the mechanical response of heterogeneous materials, bridging the gap
between advanced data-driven methods and complex solid mechanics problems.
Trained on a large-scale high-resolution dataset of 2D fiber-reinforced
composites, Micrometer can achieve state-of-the-art performance in predicting
microscale strain fields across a wide range of microstructures, material
properties under any loading conditions and We demonstrate the accuracy and
computational efficiency of Micrometer through applications in computational
homogenization and multiscale modeling, where Micrometer achieves 1\% error in
predicting macroscale stress fields while reducing computational time by up to
two orders of magnitude compared to conventional numerical solvers. We further
showcase the adaptability of the proposed model through transfer learning
experiments on new materials with limited data, highlighting its potential to
tackle diverse scenarios in mechanical analysis of solid materials. Our work
represents a significant step towards AI-driven innovation in computational
solid mechanics, addressing the limitations of traditional numerical methods
and paving the way for more efficient simulations of heterogeneous materials
across various industrial applications.
|
[{'version': 'v1', 'created': 'Mon, 23 Sep 2024 16:01:37 GMT'}]
|
2024-10-10
|
Tyler Sours, Shivang Agarwal, Marc Cormier, Jordan Crivelli-Decker,
Steffen Ridderbusch, Stephen L. Glazier, Connor P. Aiken, Aayush R. Singh,
Ang Xiao, Omar Allam
|
Early-Cycle Internal Impedance Enables ML-Based Battery Cycle Life
Predictions Across Manufacturers
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Predicting the end-of-life (EOL) of lithium-ion batteries across different
manufacturers presents significant challenges due to variations in electrode
materials, manufacturing processes, cell formats, and a lack of generally
available data. Methods that construct features solely on voltage-capacity
profile data typically fail to generalize across cell chemistries. This study
introduces a methodology that combines traditional voltage-capacity features
with Direct Current Internal Resistance (DCIR) measurements, enabling more
accurate and generalizable EOL predictions. The use of early-cycle DCIR data
captures critical degradation mechanisms related to internal resistance growth,
enhancing model robustness. Models are shown to successfully predict the number
of cycles to EOL for unseen manufacturers of varied electrode composition with
a mean absolute error (MAE) of 150 cycles. This cross-manufacturer
generalizability reduces the need for extensive new data collection and
retraining, enabling manufacturers to optimize new battery designs using
existing datasets. Additionally, a novel DCIR-compatible dataset is released as
part of ongoing efforts to enrich the growing ecosystem of cycling data and
accelerate battery materials development.
|
[{'version': 'v1', 'created': 'Sat, 5 Oct 2024 17:04:25 GMT'}, {'version': 'v2', 'created': 'Tue, 13 May 2025 16:25:33 GMT'}]
|
2025-05-14
|
Joy Datta, Dibakar Datta, Amruth Nadimpally, Nikhil Koratkar
|
Generative AI for Discovering Porous Oxide Materials for Next-Generation
Energy Storage
| null | null | null |
cond-mat.mtrl-sci
|
The key challenge in advancing multivalent-ion batteries lies in finding
suitable intercalation hosts. Open-tunnel oxides, featuring one-dimensional
channels or nanopores, show promise for enabling effective ion transport.
However, the vast range of compositional possibilities renders traditional
experimental and quantum-based methods impractical for large-scale studies.
This work presents a generative AI framework that uses the Crystal Diffusion
Variational Autoencoder (CDVAE) and a fine-tuned Large Language Model (LLM) to
expedite the discovery of stable open-tunneled oxide materials for
multivalent-ion batteries.
By combining machine learning with data mining techniques, five promising
transition metal oxide (TMO) structures are generated. These structures, known
for forming open-tunnel oxide frameworks, are structurally validated through
Density Functional Theory (DFT). The results show that the generated structures
have lower formation energies compared to similar compositions in the Materials
Project (MP) database, indicating improved thermodynamic stability.
Additionally, the graph-based M3GNet model is employed to relax further
generated structures, providing a more computationally efficient alternative to
DFT. Machine learning-based predictions of formation energy, band gap, and
energy above the hull refine the selection process, leading to the
identification of materials with significant potential for real-world battery
applications. This research demonstrates the power of generative AI in rapidly
exploring the vast chemical space of TMOs, offering a new approach to
discovering stable open-tunnel oxides for multivalent-ion batteries. The
results highlight the potential of this approach to contribute to more
sustainable energy storage technologies, addressing the growing concerns
surrounding the scarcity of lithium.
|
[{'version': 'v1', 'created': 'Wed, 9 Oct 2024 00:18:58 GMT'}]
|
2024-10-10
|
Lorenzo Monacelli, Antonio Siciliano, Nicola Marzari
|
A unified quantum framework for electrons and ions: The self-consistent
harmonic approximation on a neural network curved manifold
| null | null | null |
cond-mat.mtrl-sci cond-mat.str-el
|
The numerical solution of the many-body problem of interacting electrons and
ions beyond the adiabatic approximation is a key challenge in condensed matter
physics, chemistry, and materials science. Traditional methods to solve the
multi-component quantum Hamiltonian tend to be specialized electrons or ions
and can suffer from a methodological gap when applied to both electrons and
ions simultaneously. In addition, ionic techniques often limited as $T\to$ 0K,
whereas electronic methods are designed for 0K. Thus, efficient strategies that
simultaneously address the thermal fluctuations of ions at ambient temperature
without struggling to describe the electronic quantum state from first
principles are missing.
This work extends the self-consistent harmonic approximation for the ions to
include also the electrons. The approach minimizes the total free energy by
optimizing an \emph{ansatz} density matrix, solving a fermionic self-consistent
harmonic Hamiltonian on a curved manifold, which is parametrized through a
neural network. We demonstrate that this approach, designed initially for a
flat Cartesian space to treat quantum nuclei at finite temperatures, can
efficiently tackle both the ground and excited state properties of electronic
systems, thus paving the way to a unified quantum description for electrons and
atomic nuclei. Importantly, this approach preserves an analytical expression
for entropy, enabling the direct computation of free energies and phase
diagrams of materials.
We benchmark the numerical implementation in several prototypical cases,
proving that it captures quantum tunneling, electron-ion cusps, and static
electronic correlations in the dissociation of H$_2$, where other mean-field
approaches fail.
|
[{'version': 'v1', 'created': 'Fri, 11 Oct 2024 14:59:57 GMT'}]
|
2024-10-14
|
Leonardo Sabattini, Annalisa Coriolano, Corneel Casert, Stiven Forti,
Edward S. Barnard, Fabio Beltram, Massimiliano Pontil, Stephen Whitelam,
Camilla Coletti and Antonio Rossi
|
Adaptive AI-Driven Material Synthesis: Towards Autonomous 2D Materials
Growth
| null | null | null |
cond-mat.mes-hall cond-mat.mtrl-sci cs.LG
|
Two-dimensional (2D) materials are poised to revolutionize current
solid-state technology with their extraordinary properties. Yet, the primary
challenge remains their scalable production. While there have been significant
advancements, much of the scientific progress has depended on the exfoliation
of materials, a method that poses severe challenges for large-scale
applications. With the advent of artificial intelligence (AI) in materials
science, innovative synthesis methodologies are now on the horizon. This study
explores the forefront of autonomous materials synthesis using an artificial
neural network (ANN) trained by evolutionary methods, focusing on the efficient
production of graphene. Our approach demonstrates that a neural network can
iteratively and autonomously learn a time-dependent protocol for the efficient
growth of graphene, without requiring pretraining on what constitutes an
effective recipe. Evaluation criteria are based on the proximity of the Raman
signature to that of monolayer graphene: higher scores are granted to outcomes
whose spectrum more closely resembles that of an ideal continuous monolayer
structure. This feedback mechanism allows for iterative refinement of the ANN's
time-dependent synthesis protocols, progressively improving sample quality.
Through the advancement and application of AI methodologies, this work makes a
substantial contribution to the field of materials engineering, fostering a new
era of innovation and efficiency in the synthesis process.
|
[{'version': 'v1', 'created': 'Thu, 10 Oct 2024 15:00:52 GMT'}, {'version': 'v2', 'created': 'Mon, 18 Nov 2024 06:57:23 GMT'}]
|
2024-11-19
|
Xinyu Jiang, Haofan Sun, Qiong Nian, Houlong Zhuang
|
Combining Reinforcement Learning with Graph Convolutional Neural
Networks for Efficient Design of TiAl/TiAlN Atomic-Scale Interfaces
| null | null | null |
cond-mat.mtrl-sci
|
Ti/TiN coatings are utilized in a wide variety of engineering applications
due to their superior properties such as high hardness and toughness. Doping Al
into Ti/TiN can also enhance properties and lead to even higher performance.
Therefore, studying the atomic-level behavior of the TiAl/TiAlN interface is
important. However, due to the large number of possible combinations for the 50
mol% Al-doped Ti/TiN system, it is time-consuming to use the DFT-based Monte
Carlo method to find the optimal TiAl/TiAlN system with a high work of
adhesion. In this study, we use a graph convolutional neural network as an
interatomic potential, combined with reinforcement learning, to improve the
efficiency of finding optimal structures with a high work of adhesion. By
inspecting the features of structures in neural networks, we found that the
optimal structures follow a certain pattern of doping Al near the interface.
The electronic structure and bonding analysis indicate that the optimal
TiAl/TiAlN structures have higher bonding strength. We expect our approach to
significantly accelerate the design of advanced ceramic coatings, which can
lead to more durable and efficient materials for engineering applications.
|
[{'version': 'v1', 'created': 'Tue, 15 Oct 2024 01:33:20 GMT'}]
|
2024-10-16
|
Salvatore Romano, Harsharan Kaur, Moritz Zelenka, Pablo Montero De
Hijes, Moritz Eder, Gareth S. Parkinson, Ellen H. G. Backus, Christoph
Dellago
|
Structure of the water/magnetite interface from sum frequency generation
experiments and neural network based molecular dynamics simulations
| null | null | null |
cond-mat.mtrl-sci physics.chem-ph
|
Magnetite, a naturally abundant mineral, frequently interacts with water in
both natural settings and various technical applications, making the study of
its surface chemistry highly relevant. In this work, we investigate the
hydrogen bonding dynamics and the presence of hydroxyl species at the
magnetite-water interface using a combination of neural network potential-based
molecular dynamics simulations and sum frequency generation vibrational
spectroscopy. Our simulations, which involved large water systems, allowed us
to identify distinct interfacial species, such as dissociated hydrogen and
hydroxide ions formed by water dissociation. Notably, water molecules near the
interface exhibited a preference for dipole orientation towards the surface,
with bulk-like water behavior only re-emerging beyond 60 {\AA} from the
surface. The vibrational spectroscopy results aligned well with the
simulations, confirming the presence of a hydrogen bond network in the surface
ad-layers. The analysis revealed that surface-adsorbed hydroxyl groups orient
their hydrogen atoms towards the water bulk. In contrast, hydrogen-bonded water
molecules align with their hydrogen atoms pointing towards the magnetite
surface.
|
[{'version': 'v1', 'created': 'Wed, 16 Oct 2024 16:24:19 GMT'}]
|
2024-10-17
|
Alireza Ghafarollahi and Markus J. Buehler
|
Rapid and Automated Alloy Design with Graph Neural Network-Powered
LLM-Driven Multi-Agent Systems
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cs.AI cs.MA
|
A multi-agent AI model is used to automate the discovery of new metallic
alloys, integrating multimodal data and external knowledge including insights
from physics via atomistic simulations. Our multi-agent system features three
key components: (a) a suite of LLMs responsible for tasks such as reasoning and
planning, (b) a group of AI agents with distinct roles and expertise that
dynamically collaborate, and (c) a newly developed graph neural network (GNN)
model for rapid retrieval of key physical properties. A set of LLM-driven AI
agents collaborate to automate the exploration of the vast design space of
MPEAs, guided by predictions from the GNN. We focus on the NbMoTa family of
body-centered cubic (bcc) alloys, modeled using an ML-based interatomic
potential, and target two key properties: the Peierls barrier and solute/screw
dislocation interaction energy. Our GNN model accurately predicts these
atomic-scale properties, providing a faster alternative to costly brute-force
calculations and reducing the computational burden on multi-agent systems for
physics retrieval. This AI system revolutionizes materials discovery by
reducing reliance on human expertise and overcoming the limitations of direct
all-atom simulations. By synergizing the predictive power of GNNs with the
dynamic collaboration of LLM-based agents, the system autonomously navigates
vast alloy design spaces, identifying trends in atomic-scale material
properties and predicting macro-scale mechanical strength, as demonstrated by
several computational experiments. This approach accelerates the discovery of
advanced alloys and holds promise for broader applications in other complex
systems, marking a significant step forward in automated materials design.
|
[{'version': 'v1', 'created': 'Thu, 17 Oct 2024 17:06:26 GMT'}]
|
2024-10-18
|
A. K. Shargh, C. D. Stiles, J. A. El-Awady
|
Temperature-Dependent Design of BCC High Entropy Alloys: Integrating
Deep Learning, CALPHAD, and Experimental Validation
| null | null | null |
cond-mat.mtrl-sci
|
Due to their compositional complexity, refractory multi-principal element
alloys (RMPEAs) exhibit a diverse range of material properties, making them
highly suitable for several applications. Importantly, single-phase BCC RMPEAs
are known for their superior strength. Nevertheless, identifying the stability
of single-phase BCC is challenging because of the vast compositional space of
RMPEAs. In this study, we develop a deep learning framework that predicts the
fraction of RMPEA phases with high accuracy at several temperatures. Using the
accurate predictive framework, we navigate the compositional space of Ti, Fe,
Al, V, Ni, Nb and Zr elements to search for potentially stable single-phase BCC
RMPEAs and provide design insights that can guide the synthesis of new RMPEAs
in experiments. Furthermore, we develop a mathematical equation that enables
the rapid and accurate identification of single-phase BCC RMPEAs with high
accuracy. The machine learning (ML) model is finally evaluated against a
high-fidelity experimental data from literature, confirming that our model
accurately predicts the constituent phases of the experimental samples while
provides insights that are laborious to extract from experiments.
|
[{'version': 'v1', 'created': 'Fri, 18 Oct 2024 17:11:33 GMT'}]
|
2024-10-21
|
Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim
Isah, Muhammad Nouman Anwar, Tyler Starkus, Prasanna Balaprakash, Stephen Lam
|
Generalizable Prediction Model of Molten Salt Mixture Density with
Chemistry-Informed Transfer Learning
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Optimally designing molten salt applications requires knowledge of their
thermophysical properties, but existing databases are incomplete, and
experiments are challenging. Ideal mixing and Redlich-Kister models are
computationally cheap but lack either accuracy or generality. To address this,
a transfer learning approach using deep neural networks (DNNs) is proposed,
combining Redlich-Kister models, experimental data, and ab initio properties.
The approach predicts molten salt density with high accuracy ($r^{2}$ > 0.99,
MAPE < 1%), outperforming the alternatives.
|
[{'version': 'v1', 'created': 'Sat, 19 Oct 2024 14:28:46 GMT'}]
|
2024-10-22
|
Mohammed Al-Fahdi, Riccardo Rurali, Jianjun Hu, Christopher Wolverton,
Ming Hu
|
Accelerating Discovery of Extreme Lattice Thermal Conductivity by
Crystal Attention Graph Neural Network (CATGNN) Using Chemical Bonding
Intuitive Descriptors
| null | null | null |
cond-mat.mtrl-sci
|
Searching for technologically promising crystalline materials with desired
thermal transport properties requires an electronic level comprehension of
interatomic interactions and chemical intuition to uncover the hidden
structure-property relationship. Here, we propose two chemical bonding
descriptors, namely negative normalized integrated crystal orbital Hamilton
population (normalized -ICOHP) and normalized integrated crystal orbital bond
index (normalized ICOBI) and unravel their strong correlation to both lattice
thermal conductivity (LTC) and rattling effect characterized by mean squared
displacement (MSD). Our new descriptors outperform empirical models and the
sole -ICOHP quantity in closely relating to extreme LTCs by testing on a
first-principles dataset of over 4,500 materials with 62 distinct species. The
Pearson correlation of both descriptors with LTC are significantly higher in
magnitude compared with the traditional simple rule of average mass. We further
develop crystal attention graph neural networks (CATGNN) model and predict our
proposed descriptors of ~200,000 materials from existing databases to screen
potentially ultralow and high LTC materials. We select 367 (533) with low
(high) normalized -ICOHP and ICOBI for first-principles validation. The
validation shows that 106 dynamically stable materials with low normalized
-ICOHP and ICOBI have LTC less than 5 W/mK, among which 68% are less than 2
W/mK, while 13 stable materials with high normalized -ICOHP and ICOBI possess
LTC higher than 100 W/mK. The proposed normalized -ICOHP and normalized ICOBI
descriptors offer deep insights into LTC and MSD from chemical bonding
principles. Considering the cheap computational cost, these descriptors offer a
new reliable and fast route for high-throughput screening of novel crystalline
materials with extreme LTCs for applications such as thermoelectrics and
electronic cooling.
|
[{'version': 'v1', 'created': 'Mon, 21 Oct 2024 14:46:09 GMT'}]
|
2024-10-22
|
Janghoon Ock, Tirtha Vinchurkar, Yayati Jadhav, Amir Barati Farimani
|
Adsorb-Agent: Autonomous Identification of Stable Adsorption
Configurations via Large Language Model Agent
| null | null | null |
cs.CL cond-mat.mtrl-sci
|
Adsorption energy is a key reactivity descriptor in catalysis, enabling
efficient screening for optimal catalysts. However, determining adsorption
energy typically requires evaluating numerous adsorbate-catalyst
configurations. Current algorithmic approaches rely on exhaustive enumeration
of adsorption sites and configurations, which makes the process computationally
intensive and does not inherently guarantee the identification of the global
minimum energy. In this work, we introduce Adsorb-Agent, a Large Language Model
(LLM) agent designed to efficiently identify system-specific stable adsorption
configurations corresponding to the global minimum adsorption energy.
Adsorb-Agent leverages its built-in knowledge and emergent reasoning
capabilities to strategically explore adsorption configurations likely to hold
adsorption energy. By reducing the reliance on exhaustive sampling, it
significantly decreases the number of initial configurations required while
improving the accuracy of adsorption energy predictions. We evaluate
Adsorb-Agent's performance across twenty representative systems encompassing a
range of complexities. The Adsorb-Agent successfully identifies comparable
adsorption energies for 83.7% of the systems and achieves lower energies,
closer to the actual global minimum, for 35% of the systems, while requiring
significantly fewer initial configurations than conventional methods. Its
capability is particularly evident in complex systems, where it identifies
lower adsorption energies for 46.7% of systems involving intermetallic surfaces
and 66.7% of systems with large adsorbate molecules. These results demonstrate
the potential of Adsorb-Agent to accelerate catalyst discovery by reducing
computational costs and improving the reliability of adsorption energy
predictions.
|
[{'version': 'v1', 'created': 'Tue, 22 Oct 2024 03:19:16 GMT'}, {'version': 'v2', 'created': 'Mon, 16 Dec 2024 16:21:00 GMT'}, {'version': 'v3', 'created': 'Wed, 30 Apr 2025 15:05:27 GMT'}]
|
2025-05-01
|
Lu Wang, Hongchan Chen, Bing Wang, Qian Li, Qun Luo and Yuexing Han
|
Deep Learning-Driven Microstructure Characterization and Vickers
Hardness Prediction of Mg-Gd Alloys
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.CV
|
In the field of materials science, exploring the relationship between
composition, microstructure, and properties has long been a critical research
focus. The mechanical performance of solid-solution Mg-Gd alloys is
significantly influenced by Gd content, dendritic structures, and the presence
of secondary phases. To better analyze and predict the impact of these factors,
this study proposes a multimodal fusion learning framework based on image
processing and deep learning techniques. This framework integrates both
elemental composition and microstructural features to accurately predict the
Vickers hardness of solid-solution Mg-Gd alloys. Initially, deep learning
methods were employed to extract microstructural information from a variety of
solid-solution Mg-Gd alloy images obtained from literature and experiments.
This provided precise grain size and secondary phase microstructural features
for performance prediction tasks. Subsequently, these quantitative analysis
results were combined with Gd content information to construct a performance
prediction dataset. Finally, a regression model based on the Transformer
architecture was used to predict the Vickers hardness of Mg-Gd alloys. The
experimental results indicate that the Transformer model performs best in terms
of prediction accuracy, achieving an R^2 value of 0.9. Additionally, SHAP
analysis identified critical values for four key features affecting the Vickers
hardness of Mg-Gd alloys, providing valuable guidance for alloy design. These
findings not only enhance the understanding of alloy performance but also offer
theoretical support for future material design and optimization.
|
[{'version': 'v1', 'created': 'Sun, 27 Oct 2024 10:28:29 GMT'}]
|
2024-10-29
|
Olivier Malenfant-Thuot, Dounia Shaaban Kabakibo, Simon Blackburn,
Bruno Rousseau, and Michel C\^ot\'e
|
Large Scale Raman Spectrum Calculations in Defective 2D Materials using
Deep Learning
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
We introduce a machine learning prediction workflow to study the impact of
defects on the Raman response of 2D materials. By combining the use of
machine-learned interatomic potentials, the Raman-active $\Gamma$-weighted
density of states method and splitting configurations in independant patches,
we are able to reach simulation sizes in the tens of thousands of atoms, with
diagonalization now being the main bottleneck of the simulation. We apply the
method to two systems, isotopic graphene and defective hexagonal boron nitride,
and compare our predicted Raman response to experimental results, with good
agreement. Our method opens up many possibilities for future studies of Raman
response in solid-state physics.
|
[{'version': 'v1', 'created': 'Sun, 27 Oct 2024 11:59:59 GMT'}, {'version': 'v2', 'created': 'Fri, 28 Mar 2025 18:19:05 GMT'}]
|
2025-04-01
|
Ryotaro Okabe, Zack West, Abhijatmedhi Chotrattanapituk, Mouyang
Cheng, Denisse C\'ordova Carrizales, Weiwei Xie, Robert J. Cava and Mingda Li
|
Large Language Model-Guided Prediction Toward Quantum Materials
Synthesis
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
The synthesis of inorganic crystalline materials is essential for modern
technology, especially in quantum materials development. However, designing
efficient synthesis workflows remains a significant challenge due to the
precise experimental conditions and extensive trial and error. Here, we present
a framework using large language models (LLMs) to predict synthesis pathways
for inorganic materials, including quantum materials. Our framework contains
three models: LHS2RHS, predicting products from reactants; RHS2LHS, predicting
reactants from products; and TGT2CEQ, generating full chemical equations for
target compounds. Fine-tuned on a text-mined synthesis database, our model
raises accuracy from under 40% with pretrained models, to under 80% using
conventional fine-tuning, and further to around 90% with our proposed
generalized Tanimoto similarity, while maintaining robust to additional
synthesis steps. Our model further demonstrates comparable performance across
materials with varying degrees of quantumness quantified using quantum weight,
indicating that LLMs offer a powerful tool to predict balanced chemical
equations for quantum materials discovery.
|
[{'version': 'v1', 'created': 'Mon, 28 Oct 2024 12:50:46 GMT'}]
|
2024-10-29
|
Mark Neumann, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li,
Hitarth Choubisa, Arthur Hussey, Jonathan Godwin
|
Orb: A Fast, Scalable Neural Network Potential
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
We introduce Orb, a family of universal interatomic potentials for atomistic
modelling of materials. Orb models are 3-6 times faster than existing universal
potentials, stable under simulation for a range of out of distribution
materials and, upon release, represented a 31% reduction in error over other
methods on the Matbench Discovery benchmark. We explore several aspects of
foundation model development for materials, with a focus on diffusion
pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo
and molecular dynamics simulations.
|
[{'version': 'v1', 'created': 'Tue, 29 Oct 2024 22:20:14 GMT'}]
|
2024-10-31
|
\c{C}etin K{\i}l{\i}\c{c} and S\"umeyra G\"uler-K{\i}l{\i}\c{c}
|
Combining graph deep learning and London dispersion interatomic
potentials: A case study on pnictogen chalcohalides
|
J. Chem. Phys. 161, 174106 (2024)
|
10.1063/5.0237101
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Machine-learning interatomic potential models based on graph neural network
architectures have the potential to make atomistic materials modeling widely
accessible due to their computational efficiency, scalability, and broad
applicability. The training datasets for many such models are derived from
density-functional theory calculations, typically using a semilocal
exchange-correlation functional. As a result, long-range interactions such as
London dispersion are often missing in these models. We investigate whether
this missing component can be addressed by combining a graph deep learning
potential with semiempirical dispersion models. We assess this combination by
deriving the equations of state for layered pnictogen chalcohalides BiTeBr and
BiTeI and performing crystal structure optimizations for a broader set of
V-VI-VII compounds with various stoichiometries, many of which possess van der
Waals gaps. We characterize the optimized crystal structures by calculating
their X-ray diffraction patterns and radial distribution function histograms,
which are also used to compute Earth mover's distances to quantify the
dissimilarity between the optimized and corresponding experimental structures.
We find that dispersion-corrected graph deep learning potentials generally
(though not universally) provide a more realistic description of these
compounds due to the inclusion of van der Waals attractions. In particular,
their use results in systematic improvements in predicting not only the van der
Waals gap but also the layer thickness in layered V-VI-VII compounds. Our
results demonstrate that the combined potentials studied here, derived from a
straightforward approach that neither requires fine-tuning the training nor
refitting the potential parameters, can significantly improve the description
of layered polar crystals.
|
[{'version': 'v1', 'created': 'Sat, 2 Nov 2024 09:50:59 GMT'}]
|
2024-11-05
|
Selva Chandrasekaran Selvaraj
|
Graph Neural Networks Based Deep Learning for Predicting Structural and
Electronic Properties
| null | null | null |
cond-mat.dis-nn cond-mat.mtrl-sci
|
This study presents a deep learning approach to predicting structural and
electronic properties of materials using Graph Neural Networks (GNNs).
Leveraging data from the Materials Project database, we construct graph
representations of crystal structures and employ GNNs to predict multiple
properties simultaneously. All crystal structures are from the Materials
Project database, with a total of 158,874 structures used. Our model achieves
high predictive accuracy across various properties, as indicated by \( R^2 \)
values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above
hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for
valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi
energy. These results demonstrate the potential of GNNs in materials science,
offering a powerful tool for rapid screening and discovery of materials with
desired properties.
|
[{'version': 'v1', 'created': 'Mon, 4 Nov 2024 17:57:27 GMT'}, {'version': 'v2', 'created': 'Wed, 18 Dec 2024 18:16:16 GMT'}]
|
2024-12-20
|
Viviane Torres da Silva, Alexandre Rademaker, Krystelle Lionti,
Ronaldo Giro, Geisa Lima, Sandro Fiorini, Marcelo Archanjo, Breno W.
Carvalho, Rodrigo Neumann, Anaximandro Souza, Jo\~ao Pedro Souza, Gabriela de
Valnisio, Carmen Nilda Paz, Renato Cerqueira, Mathias Steiner
|
Automated, LLM enabled extraction of synthesis details for reticular
materials from scientific literature
| null | null | null |
cond-mat.mtrl-sci cs.IR
|
Automated knowledge extraction from scientific literature can potentially
accelerate materials discovery. We have investigated an approach for extracting
synthesis protocols for reticular materials from scientific literature using
large language models (LLMs). To that end, we introduce a Knowledge Extraction
Pipeline (KEP) that automatizes LLM-assisted paragraph classification and
information extraction. By applying prompt engineering with in-context learning
(ICL) to a set of open-source LLMs, we demonstrate that LLMs can retrieve
chemical information from PDF documents, without the need for fine-tuning or
training and at a reduced risk of hallucination. By comparing the performance
of five open-source families of LLMs in both paragraph classification and
information extraction tasks, we observe excellent model performance even if
only few example paragraphs are included in the ICL prompts. The results show
the potential of the KEP approach for reducing human annotations and data
curation efforts in automated scientific knowledge extraction.
|
[{'version': 'v1', 'created': 'Tue, 5 Nov 2024 20:08:23 GMT'}]
|
2024-11-07
|
Di Wu, Pengkun Wang, Shiming Zhou, Bochun Zhang, Liheng Yu, Xi Chen,
Xu Wang, Zhengyang Zhou, Yang Wang, Sujing Wang, Jiangfeng Du
|
A Powder Diffraction-AI Solution for Crystalline Structure
| null | null | null |
cond-mat.mtrl-sci
|
Determining the atomic-level structure of crystalline solids is critically
important across a wide array of scientific disciplines. The challenges
associated with obtaining samples suitable for single-crystal diffraction,
coupled with the limitations inherent in classical structure determination
methods that primarily utilize powder diffraction for most polycrystalline
materials, underscore an urgent need to develop alternative approaches for
elucidating the structures of commonly encountered crystalline compounds. In
this work, we present an artificial intelligence-directed leapfrog model
capable of accurately determining the structures of both organic and
inorganic-organic hybrid crystalline solids through direct analysis of powder
X-ray diffraction data. This model not only offers a comprehensive solution
that effectively circumvents issues related to insoluble challenges in
conventional structure solution methodologies but also demonstrates
applicability to crystal structures across all conceivable space groups.
Furthermore, it exhibits notable compatibility with routine powder diffraction
data typically generated by standard instruments, featuring rapid data
collection and normal resolution levels.
|
[{'version': 'v1', 'created': 'Sat, 9 Nov 2024 04:20:36 GMT'}]
|
2024-11-12
|
Raul Ortega Ochoa, Tejs Vegge and Jes Frellsen
|
MolMiner: Transformer architecture for fragment-based autoregressive
generation of molecular stories
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Deep generative models for molecular discovery have become a very popular
choice in new high-throughput screening paradigms. These models have been
developed inheriting from the advances in natural language processing and
computer vision, achieving ever greater results. However, generative molecular
modelling has unique challenges that are often overlooked. Chemical validity,
interpretability of the generation process and flexibility to variable
molecular sizes are among some of the remaining challenges for generative
models in computational materials design. In this work, we propose an
autoregressive approach that decomposes molecular generation into a sequence of
discrete and interpretable steps using molecular fragments as units, a
'molecular story'. Enforcing chemical rules in the stories guarantees the
chemical validity of the generated molecules, the discrete sequential steps of
a molecular story makes the process transparent improving interpretability, and
the autoregressive nature of the approach allows the size of the molecule to be
a decision of the model. We demonstrate the validity of the approach in a
multi-target inverse design of electroactive organic compounds, focusing on the
target properties of solubility, redox potential, and synthetic accessibility.
Our results show that the model can effectively bias the generation
distribution according to the prompted multi-target objective.
|
[{'version': 'v1', 'created': 'Sun, 10 Nov 2024 22:00:55 GMT'}]
|
2024-11-12
|
Micah Nichols, Christopher D. Barrett, Doyl E. Dickel, Mashroor S.
Nitol, Saryu J. Fensin
|
Predicting Ti-Al Binary Phase Diagram with an Artificial Neural Network
Potential
| null | null | null |
cond-mat.mtrl-sci
|
The microstructure of the Ti-Al binary system is an area of great interest as
it affects material properties and plasticity. Phase transformations induce
microstructural changes; therefore, accurately modeling the phase
transformations of the Ti-Al system is necessary to describe plasticity.
Interatomic potentials can be a powerful tool to model how materials behave;
however, existing potentials lack accuracy in certain aspects. While classical
potentials like the Embedded Atom Method (EAM) and Modified Embedded Atom
Method (MEAM) perform adequately for modeling dilute Al solute within Ti's
$\alpha$ phase, they struggle with accurately predicting plasiticity. In
particular, they struggle with stacking fault energies in intermetallics and to
some extent elastic properties. This hinders their effectiveness in
investigating the plastic behavior of formed intermetallics in Ti-Al alloys.
Classical potentials also fail to predict the $\alpha$ to $\beta$ phase
boundary. Existing machine learning (ML) potentials reproduce the properties of
formed intermetallics with density functional theory (DFT) but do not examine
the $\alpha$ to $\beta$ or $\alpha$ to D0$_{19}$ phase boundaries. This work
uses a rapid artificial neural network (RANN) framework to produce a neural
network potential for the Ti-Al binary system. This potential is capable of
reproducing the Ti-Al binary phase diagram up to 50$\%$ Al concentration. The
present interatomic potential ensures stability and allows results near the
accuracy of DFT. Using Monte Carlo simulations, RANN potential accurately
predicts the $\alpha$ to $\beta$ and $\alpha$ to D0$_{19}$ phase transitions.
The current potential also exhibits accurate elastic constants and stacking
fault energies for the L1$_0$ and D0$_{19}$ phases.
|
[{'version': 'v1', 'created': 'Tue, 12 Nov 2024 17:36:44 GMT'}]
|
2024-11-13
|
Ziqi Ni, Yahao Li, Kaijia Hu, Kunyuan Han, Ming Xu, Xingyu Chen,
Fengqi Liu, Yicong Ye, Shuxin Bai
|
MatPilot: an LLM-enabled AI Materials Scientist under the Framework of
Human-Machine Collaboration
| null | null | null |
physics.soc-ph cond-mat.mtrl-sci cs.AI
|
The rapid evolution of artificial intelligence, particularly large language
models, presents unprecedented opportunities for materials science research. We
proposed and developed an AI materials scientist named MatPilot, which has
shown encouraging abilities in the discovery of new materials. The core
strength of MatPilot is its natural language interactive human-machine
collaboration, which augments the research capabilities of human scientist
teams through a multi-agent system. MatPilot integrates unique cognitive
abilities, extensive accumulated experience, and ongoing curiosity of
human-beings with the AI agents' capabilities of advanced abstraction, complex
knowledge storage and high-dimensional information processing. It could
generate scientific hypotheses and experimental schemes, and employ predictive
models and optimization algorithms to drive an automated experimental platform
for experiments. It turns out that our system demonstrates capabilities for
efficient validation, continuous learning, and iterative optimization.
|
[{'version': 'v1', 'created': 'Sun, 10 Nov 2024 12:23:44 GMT'}]
|
2024-11-14
|
Jijie Zou, Zhanghao Zhouyin, Dongying Lin, Linfeng Zhang, Shimin Hou,
Qiangqiang Gu
|
Deep Learning Accelerated Quantum Transport Simulations in
Nanoelectronics: From Break Junctions to Field-Effect Transistors
| null | null | null |
cond-mat.mes-hall cond-mat.mtrl-sci cs.LG
|
Quantum transport calculations are essential for understanding and designing
nanoelectronic devices, yet the trade-off between accuracy and computational
efficiency has long limited their practical applications. We present a general
framework that combines the deep learning tight-binding Hamiltonian (DeePTB)
approach with the non-equilibrium Green's Function (NEGF) method, enabling
efficient quantum transport calculations while maintaining first-principles
accuracy. We demonstrate the capabilities of the DeePTB-NEGF framework through
two representative applications: comprehensive simulation of break junction
systems, where conductance histograms show good agreement with experimental
measurements in both metallic contact and single-molecule junction cases; and
simulation of carbon nanotube field effect transistors through self-consistent
NEGF-Poisson calculations, capturing essential physics including the
electrostatic potential and transfer characteristic curves under finite bias
conditions. This framework bridges the gap between first-principles accuracy
and computational efficiency, providing a powerful tool for high-throughput
quantum transport simulations across different scales in nanoelectronics.
|
[{'version': 'v1', 'created': 'Wed, 13 Nov 2024 17:27:32 GMT'}, {'version': 'v2', 'created': 'Sun, 9 Feb 2025 15:25:05 GMT'}]
|
2025-02-11
|
Xuan Hu, Qijun Chen, Nicholas H. Luo, Richy J. Zheng, Shaofan Li
|
A Message Passing Neural Network Surrogate Model for Bond-Associated
Peridynamic Material Correspondence Formulation
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci cs.LG stat.ML
|
Peridynamics is a non-local continuum mechanics theory that offers unique
advantages for modeling problems involving discontinuities and complex
deformations. Within the peridynamic framework, various formulations exist,
among which the material correspondence formulation stands out for its ability
to directly incorporate traditional continuum material models, making it highly
applicable to a range of engineering challenges. A notable advancement in this
area is the bond-associated correspondence model, which not only resolves
issues of material instability but also achieves high computational accuracy.
However, the bond-associated model typically requires higher computational
costs than FEA, which can limit its practical application. To address this
computational challenge, we propose a novel surrogate model based on a
message-passing neural network (MPNN) specifically designed for the
bond-associated peridynamic material correspondence formulation. Leveraging the
similarities between graph structure and the neighborhood connectivity inherent
to peridynamics, we construct an MPNN that can transfers domain knowledge from
peridynamics into a computational graph and shorten the computation time via
GPU acceleration. Unlike conventional graph neural networks that focus on node
features, our model emphasizes edge-based features, capturing the essential
material point interactions in the formulation. A key advantage of this neural
network approach is its flexibility: it does not require fixed neighborhood
connectivity, making it adaptable across diverse configurations and scalable
for complex systems. Furthermore, the model inherently possesses translational
and rotational invariance, enabling it to maintain physical objectivity: a
critical requirement for accurate mechanical modeling.
|
[{'version': 'v1', 'created': 'Tue, 29 Oct 2024 17:42:29 GMT'}]
|
2024-11-15
|
Xiao-Qi Han, Xin-De Wang, Meng-Yuan Xu, Zhen Feng, Bo-Wen Yao,
Peng-Jie Guo, Ze-Feng Gao, Zhong-Yi Lu
|
AI-driven inverse design of materials: Past, present and future
|
Chin. Phys. Lett., 2025
|
10.1088/0256-307X/42/2/027403
| null |
cond-mat.mtrl-sci cond-mat.supr-con cs.AI
|
The discovery of advanced materials is the cornerstone of human technological
development and progress. The structures of materials and their corresponding
properties are essentially the result of a complex interplay of multiple
degrees of freedom such as lattice, charge, spin, symmetry, and topology. This
poses significant challenges for the inverse design methods of materials.
Humans have long explored new materials through a large number of experiments
and proposed corresponding theoretical systems to predict new material
properties and structures. With the improvement of computational power,
researchers have gradually developed various electronic structure calculation
methods, such as the density functional theory and high-throughput
computational methods. Recently, the rapid development of artificial
intelligence technology in the field of computer science has enabled the
effective characterization of the implicit association between material
properties and structures, thus opening up an efficient paradigm for the
inverse design of functional materials. A significant progress has been made in
inverse design of materials based on generative and discriminative models,
attracting widespread attention from researchers. Considering this rapid
technological progress, in this survey, we look back on the latest advancements
in AI-driven inverse design of materials by introducing the background, key
findings, and mainstream technological development routes. In addition, we
summarize the remaining issues for future directions. This survey provides the
latest overview of AI-driven inverse design of materials, which can serve as a
useful resource for researchers.
|
[{'version': 'v1', 'created': 'Thu, 14 Nov 2024 13:25:04 GMT'}, {'version': 'v2', 'created': 'Wed, 27 Nov 2024 05:20:40 GMT'}, {'version': 'v3', 'created': 'Fri, 29 Nov 2024 05:10:56 GMT'}, {'version': 'v4', 'created': 'Thu, 20 Feb 2025 03:47:54 GMT'}]
|
2025-02-21
|
Mashaekh Tausif Ehsan, Saifuddin Zafar, Apurba Sarker, Sourav Das
Suvro, Mohammad Nasim Hasan
|
Graph neural network framework for energy mapping of hybrid monte-carlo
molecular dynamics simulations of Medium Entropy Alloys
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Machine learning (ML) methods have drawn significant interest in material
design and discovery. Graph neural networks (GNNs), in particular, have
demonstrated strong potential for predicting material properties. The present
study proposes a graph-based representation for modeling medium-entropy alloys
(MEAs). Hybrid Monte-Carlo molecular dynamics (MC/MD) simulations are employed
to achieve thermally stable structures across various annealing temperatures in
an MEA. These simulations generate dump files and potential energy labels,
which are used to construct graph representations of the atomic configurations.
Edges are created between each atom and its 12 nearest neighbors without
incorporating explicit edge features. These graphs then serve as input for a
Graph Convolutional Neural Network (GCNN) based ML model to predict the
system's potential energy. The GCNN architecture effectively captures the local
environment and chemical ordering within the MEA structure. The GCNN-based ML
model demonstrates strong performance in predicting potential energy at
different steps, showing satisfactory results on both the training data and
unseen configurations. Our approach presents a graph-based modeling framework
for MEAs and high-entropy alloys (HEAs), which effectively captures the local
chemical order (LCO) within the alloy structure. This allows us to predict key
material properties influenced by LCO in both MEAs and HEAs, providing deeper
insights into how atomic-scale arrangements affect the properties of these
alloys.
|
[{'version': 'v1', 'created': 'Wed, 20 Nov 2024 19:22:40 GMT'}]
|
2024-11-22
|
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal, Fariha Agbere,
Qianxiang Ai, Nawaf Alampara, Alexander Al-Feghali, Mehrad Ansari, Dmytro
Antypov, Amro Aswad, Jiaru Bai, Viktoriia Baibakova, Devi Dutta Biswajeet,
Erik Bitzek, Joshua D. Bocarsly, Anna Borisova, Andres M Bran, L. Catherine
Brinson, Marcel Moran Calderon, Alessandro Canalicchio, Victor Chen, Yuan
Chiang, Defne Circi, Benjamin Charmes, Vikrant Chaudhary, Zizhang Chen,
Min-Hsueh Chiu, Judith Clymo, Kedar Dabhadkar, Nathan Daelman, Archit Datar,
Wibe A. de Jong, Matthew L. Evans, Maryam Ghazizade Fard, Giuseppe Fisicaro,
Abhijeet Sadashiv Gangan, Janine George, Jose D. Cojal Gonzalez, Michael
G\"otte, Ankur K. Gupta, Hassan Harb, Pengyu Hong, Abdelrahman Ibrahim, Ahmed
Ilyas, Alishba Imran, Kevin Ishimwe, Ramsey Issa, Kevin Maik Jablonka, Colin
Jones, Tyler R. Josephson, Greg Juhasz, Sarthak Kapoor, Rongda Kang, Ghazal
Khalighinejad, Sartaaj Khan, Sascha Klawohn, Suneel Kuman, Alvin Noe Ladines,
Sarom Leang, Magdalena Lederbauer, Sheng-Lun (Mark) Liao, Hao Liu, Xuefeng
Liu, Stanley Lo, Sandeep Madireddy, Piyush Ranjan Maharana, Shagun
Maheshwari, Soroush Mahjoubi, Jos\'e A. M\'arquez, Rob Mills, Trupti Mohanty,
Bernadette Mohr, Seyed Mohamad Moosavi, Alexander Mo{\ss}hammer, Amirhossein
D. Naghdi, Aakash Naik, Oleksandr Narykov, Hampus N\"asstr\"om, Xuan Vu
Nguyen, Xinyi Ni, Dana O'Connor, Teslim Olayiwola, Federico Ottomano, Aleyna
Beste Ozhan, Sebastian Pagel, Chiku Parida, Jaehee Park, Vraj Patel, Elena
Patyukova, Martin Hoffmann Petersen, Luis Pinto, Jos\'e M. Pizarro, Dieter
Plessers, Tapashree Pradhan, Utkarsh Pratiush, Charishma Puli, Andrew Qin,
Mahyar Rajabi, Francesco Ricci, Elliot Risch, Marti\~no R\'ios-Garc\'ia,
Aritra Roy, Tehseen Rug, Hasan M Sayeed, Markus Scheidgen, Mara
Schilling-Wilhelmi, Marcel Schloz, Fabian Sch\"oppach, Julia Schumann,
Philippe Schwaller, Marcus Schwarting, Samiha Sharlin, Kevin Shen, Jiale Shi,
Pradip Si, Jennifer D'Souza, Taylor Sparks, Suraj Sudhakar, Leopold Talirz,
Dandan Tang, Olga Taran, Carla Terboven, Mark Tropin, Anastasiia Tsymbal,
Katharina Ueltzen, Pablo Andres Unzueta, Archit Vasan, Tirtha Vinchurkar,
Trung Vo, Gabriel Vogel, Christoph V\"olker, Jan Weinreich, Faradawn Yang,
Mohd Zaki, Chi Zhang, Sylvester Zhang, Weijie Zhang, Ruijie Zhu, Shang Zhu,
Jan Janssen, Calvin Li, Ian Foster, Ben Blaiszik
|
Reflections from the 2024 Large Language Model (LLM) Hackathon for
Applications in Materials Science and Chemistry
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.chem-ph
|
Here, we present the outcomes from the second Large Language Model (LLM)
Hackathon for Applications in Materials Science and Chemistry, which engaged
participants across global hybrid locations, resulting in 34 team submissions.
The submissions spanned seven key application areas and demonstrated the
diverse utility of LLMs for applications in (1) molecular and material property
prediction; (2) molecular and material design; (3) automation and novel
interfaces; (4) scientific communication and education; (5) research data
management and automation; (6) hypothesis generation and evaluation; and (7)
knowledge extraction and reasoning from scientific literature. Each team
submission is presented in a summary table with links to the code and as brief
papers in the appendix. Beyond team results, we discuss the hackathon event and
its hybrid format, which included physical hubs in Toronto, Montreal, San
Francisco, Berlin, Lausanne, and Tokyo, alongside a global online hub to enable
local and virtual collaboration. Overall, the event highlighted significant
improvements in LLM capabilities since the previous year's hackathon,
suggesting continued expansion of LLMs for applications in materials science
and chemistry research. These outcomes demonstrate the dual utility of LLMs as
both multipurpose models for diverse machine learning tasks and platforms for
rapid prototyping custom applications in scientific research.
|
[{'version': 'v1', 'created': 'Wed, 20 Nov 2024 23:08:01 GMT'}, {'version': 'v2', 'created': 'Fri, 3 Jan 2025 01:55:35 GMT'}]
|
2025-01-06
|
Weiyi Gong, Tao Sun, Hexin Bai, Jeng-Yuan Tsai, Haibin Ling, Qimin Yan
|
Graph Transformer Networks for Accurate Band Structure Prediction: An
End-to-End Approach
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Predicting electronic band structures from crystal structures is crucial for
understanding structure-property correlations in materials science.
First-principles approaches are accurate but computationally intensive. Recent
years, machine learning (ML) has been extensively applied to this field, while
existing ML models predominantly focus on band gap predictions or indirect band
structure estimation via solving predicted Hamiltonians. An end-to-end model to
predict band structure accurately and efficiently is still lacking. Here, we
introduce a graph Transformer-based end-to-end approach that directly predicts
band structures from crystal structures with high accuracy. Our method
leverages the continuity of the k-path and treat continuous bands as a
sequence. We demonstrate that our model not only provides accurate band
structure predictions but also can derive other properties (such as band gap,
band center, and band dispersion) with high accuracy. We verify the model
performance on large and diverse datasets.
|
[{'version': 'v1', 'created': 'Mon, 25 Nov 2024 15:22:03 GMT'}]
|
2024-11-26
|
Naoki Matsumura, Yuta Yoshimoto, Tamio Yamazaki, Tomohito Amano,
Tomoyuki Noda, Naoki Ebata, Takatoshi Kasano and Yasufumi Sakai
|
Generator of Neural Network Potential for Molecular Dynamics:
Constructing Robust and Accurate Potentials with Active Learning for
Nanosecond-scale Simulations
| null |
10.1021/acs.jctc.4c01613
| null |
cond-mat.mtrl-sci physics.comp-ph
|
Neural network potentials (NNPs) enable large-scale molecular dynamics (MD)
simulations of systems containing >10,000 atoms with the accuracy comparable to
ab initio methods and play a crucial role in material studies. Although NNPs
are valuable for short-duration MD simulations, maintaining the stability of
long-duration MD simulations remains challenging due to the uncharted regions
of the potential energy surface (PES). Currently, there is no effective
methodology to address this issue. To overcome this challenge, we developed an
automatic generator of robust and accurate NNPs based on an active learning
(AL) framework. This generator provides a fully integrated solution
encompassing initial dataset creation, NNP training, evaluation, sampling of
additional structures, screening, and labeling. Crucially, our approach uses a
sampling strategy that focuses on generating unstable structures with short
interatomic distances, combined with a screening strategy that efficiently
samples these configurations based on interatomic distances and structural
features. This approach greatly enhances the MD simulation stability, enabling
nanosecond-scale simulations. We evaluated the performance of our NNP generator
in terms of its MD simulation stability and physical properties by applying it
to liquid propylene glycol (PG) and polyethylene glycol (PEG). The generated
NNPs enable stable MD simulations of systems with >10,000 atoms for 20 ns. The
predicted physical properties, such as the density and self-diffusion
coefficient, show excellent agreement with the experimental values. This work
represents a remarkable advance in the generation of robust and accurate NNPs
for organic materials, paving the way for long-duration MD simulations of
complex systems.
|
[{'version': 'v1', 'created': 'Tue, 26 Nov 2024 08:03:13 GMT'}, {'version': 'v2', 'created': 'Wed, 19 Mar 2025 07:20:57 GMT'}, {'version': 'v3', 'created': 'Tue, 8 Apr 2025 07:53:26 GMT'}]
|
2025-04-09
|
Navin Rajapriya, Kotaro Kawajiri
|
Deep Learning for GWP Prediction: A Framework Using PCA, Quantile
Transformation, and Ensemble Modeling
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.chem-ph
|
Developing environmentally sustainable refrigerants is critical for
mitigating the impact of anthropogenic greenhouse gases on global warming. This
study presents a predictive modeling framework to estimate the 100-year global
warming potential (GWP 100) of single-component refrigerants using a fully
connected neural network implemented on the Multi-Sigma platform. Molecular
descriptors from RDKit, Mordred, and alvaDesc were utilized to capture various
chemical features. The RDKit-based model achieved the best performance, with a
Root Mean Square Error (RMSE) of 481.9 and an R2 score of 0.918, demonstrating
superior predictive accuracy and generalizability. Dimensionality reduction
through Principal Component Analysis (PCA) and quantile transformation were
applied to address the high-dimensional and skewed nature of the
dataset,enhancing model stability and performance. Factor analysis identified
vital molecular features, including molecular weight, lipophilicity, and
functional groups, such as nitriles and allylic oxides, as significant
contributors to GWP values. These insights provide actionable guidance for
designing environmentally sustainable refrigerants. Integrating RDKit
descriptors with Multi-Sigma's framework, which includes PCA, quantile
transformation, and neural networks, provides a scalable solution for the rapid
virtual screening of low-GWP refrigerants. This approach can potentially
accelerate the identification of eco-friendly alternatives, directly
contributing to climate mitigation by enabling the design of next-generation
refrigerants aligned with global sustainability objectives.
|
[{'version': 'v1', 'created': 'Thu, 28 Nov 2024 13:16:12 GMT'}]
|
2024-12-02
|
Alex Kutana, Koki Yoshimochi, Ryoji Asahi
|
Dielectric tensor of perovskite oxides at finite temperature using
equivariant graph neural network potentials
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Atomistic simulations of properties of materials at finite temperatures are
computationally demanding and require models that are more efficient than the
ab initio approaches. Machine learning (ML) and artificial intelligence (AI)
address this issue by enabling accurate models with close to ab initio
accuracy. Here, we demonstrate the utility of ML models in capturing properties
of realistic materials by performing finite temperature molecular dynamics
simulations of perovskite oxides using a force field based on equivariant graph
neural networks. The models demonstrate efficient learning from a small
training dataset of energies, forces, stresses, and tensors of Born effective
charges. We qualitatively capture the temperature dependence of the dielectric
tensor and structural phase transitions in calcium titanate.
|
[{'version': 'v1', 'created': 'Wed, 4 Dec 2024 18:37:23 GMT'}]
|
2024-12-05
|
In Won Yeu, Annika Stuke, Jon L.pez-Zorrilla, James M. Stevenson,
David R. Reichman, Richard A. Friesner, Alexander Urban, and Nongnuch Artrith
|
Scalable Training of Neural Network Potentials for Complex Interfaces
Through Data Augmentation
| null | null | null |
cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph
|
Artificial neural network (ANN) potentials enable highly accurate atomistic
simulations of complex materials at unprecedented scales. Despite their
promise, training ANN potentials to represent intricate potential energy
surfaces (PES) with transferability to diverse chemical environments remains
computationally intensive, especially when atomic force data are incorporated
to improve PES gradients. Here, we present an efficient ANN potential training
methodology that uses Gaussian process regression (GPR) to incorporate atomic
forces into ANN training, leading to accurate PES models with fewer additional
first-principles calculations and a reduced computational effort for training.
Our GPR-ANN approach generates synthetic energy data from force information in
the reference dataset, thus augmenting the training datasets and bypassing
direct force training. Benchmark tests on hybrid density-functional theory data
for ethylene carbonate (EC) molecules and Li metal-EC interfaces, relevant for
lithium metal battery applications, demonstrate that GPR-ANN potentials achieve
accuracies comparable to fully force-trained ANNs with a significantly reduced
computational overhead. Detailed comparisons show that the method improves both
data efficiency and scalability for complex interfaces and heterogeneous
environments. This work establishes the GPR-ANN method as a powerful and
scalable framework for constructing high-fidelity machine learning interatomic
potentials, offering the computational and memory efficiency critical for the
large-scale simulations needed for the simulation of materials interfaces.
|
[{'version': 'v1', 'created': 'Sun, 8 Dec 2024 01:14:14 GMT'}]
|
2024-12-10
|
Yuxuan Zeng, Wei Cao, Yijing Zuo, Tan Peng, Yue Hou, Ling Miao, Yang
Yang, Ziyu Wang, and Jing Shi
|
Accelerating the Exploration of Thermal Materials via a Synergistic
Strategy of DFT and Interpretable Deep Learning
| null | null | null |
cond-mat.mtrl-sci physics.app-ph
|
Lattice thermal conductivity (LTC) is a critical parameter for thermal
transport properties, playing a pivotal role in advancing thermoelectric
materials and thermal management technologies. Traditional computational
methods, such as Density Functional Theory (DFT) and Molecular Dynamics (MD),
are resource-intensive, limiting their applicability for high-throughput LTC
prediction. While AI-driven approaches have made significant strides in
material science, the trade-off between accuracy and interpretability remains a
major bottleneck. In this study, we introduce an interpretable deep learning
framework that enables rapid and accurate LTC prediction, effectively bridging
the gap between interpretability and precision. Leveraging this framework, we
identify and validate four promising thermal conductors/insulators using DFT
and MD. Moreover, by combining sensitivity analysis with DFT calculations, we
uncover novel insights into phonon thermal transport mechanisms, providing a
deeper understanding of the underlying physics. This work not only accelerates
the discovery of thermal materials but also sets a new benchmark for
interpretable AI in material science.
|
[{'version': 'v1', 'created': 'Sun, 8 Dec 2024 14:09:43 GMT'}, {'version': 'v2', 'created': 'Fri, 13 Dec 2024 09:49:07 GMT'}, {'version': 'v3', 'created': 'Thu, 19 Dec 2024 10:26:55 GMT'}, {'version': 'v4', 'created': 'Tue, 24 Dec 2024 07:19:03 GMT'}, {'version': 'v5', 'created': 'Sat, 28 Dec 2024 12:36:15 GMT'}, {'version': 'v6', 'created': 'Sun, 2 Feb 2025 17:21:03 GMT'}, {'version': 'v7', 'created': 'Fri, 14 Feb 2025 05:32:45 GMT'}, {'version': 'v8', 'created': 'Mon, 17 Mar 2025 08:54:13 GMT'}, {'version': 'v9', 'created': 'Mon, 7 Apr 2025 14:16:36 GMT'}]
|
2025-04-08
|
John Knickerbocker, Jean Benoit Heroux, Griselda Bonilla, Hsiang Hsu,
Neng Liu, Adrian Paz Ramos, Francois Arguin, Yan Tribodeau, Badr Terjani,
Mark Schultz, Raghu Kiran Ganti, Linsong Chu, Chinami Marushima, Yoichi
Taira, Sayuri Kohara, Akihiro Horibe, Hiroyuki Mori and Hidetoshi Numata
|
Next generation Co-Packaged Optics Technology to Train & Run Generative
AI Models in Data Centers and Other Computing Applications
| null | null | null |
physics.optics cond-mat.mtrl-sci
|
We report on the successful design and fabrication of optical modules using a
50 micron pitch polymer waveguide interface, integrated for low loss, high
density optical data transfer with very low space requirements on a Si
photonics die. This prototype module meets JEDEC reliability standards and
promises to increase the number of optical fibers that can be connected at the
edge of a chip, a measure known as beachfront density, by six times compared to
state of the art technology. Scalability of the polymer waveguide to less than
20 micron pitch stands to improve the bandwidth density upwards of 10 Tbps/mm.
|
[{'version': 'v1', 'created': 'Mon, 9 Dec 2024 15:25:12 GMT'}]
|
2024-12-10
|
Kai Gu, Yingping Liang, Jiaming Su, Peihan Sun, Jia Peng, Naihua Miao,
Zhimei Sun, Ying Fu, Haizheng Zhong, Jun Zhang
|
Deep Learning Models for Colloidal Nanocrystal Synthesis
| null | null | null |
cond-mat.mtrl-sci cs.AI physics.app-ph
|
Colloidal synthesis of nanocrystals usually includes complex chemical
reactions and multi-step crystallization processes. Despite the great success
in the past 30 years, it remains challenging to clarify the correlations
between synthetic parameters of chemical reaction and physical properties of
nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis
model that correlates synthetic parameters with the final size and shape of
target nanocrystals, using a dataset of 3500 recipes covering 348 distinct
nanocrystal compositions. The size and shape labels were obtained from
transmission electron microscope images using a segmentation model trained with
a semi-supervised algorithm on a dataset comprising 1.2 million nanocrystals.
By applying the reaction intermediate-based data augmentation method and
elaborated descriptors, the synthesis model was able to predict nanocrystal's
size with a mean absolute error of 1.39 nm, while reaching an 89% average
accuracy for shape classification. The synthesis model shows knowledge transfer
capabilities across different nanocrystals with inputs of new recipes. With
that, the influence of chemicals on the final size of nanocrystals was further
evaluated, revealing the importance order of nanocrystal composition, precursor
or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis
model offers a powerful tool to expedite the development of high-quality
nanocrystals.
|
[{'version': 'v1', 'created': 'Sat, 14 Dec 2024 14:18:59 GMT'}]
|
2024-12-17
|
Andrzej Opala, Krzysztof Tyszka, Mateusz K\k{e}dziora, Magdalena
Furman, Amir Rahmani, Stanis{\l}aw \'Swierczewski, Marek Ekielski, Anna
Szerling, Micha{\l} Matuszewski, Barbara Pi\k{e}tka
|
Room temperature exciton-polariton neural network with perovskite
crystal
| null | null | null |
physics.optics cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.quant-gas
|
Limitations of electronics have stimulated the search for novel
unconventional computing platforms that enable energy-efficient and ultra-fast
information processing. Among various systems, exciton-polaritons stand out as
promising candidates for the realization of optical neuromorphic devices. This
is due to their unique hybrid light-matter properties, resulting in strong
optical nonlinearity and excellent transport capabilities. However, previous
implementations of polariton neural networks have been restricted to cryogenic
temperatures, limiting their practical applications. In this work, using
non-equillibrium Bose-Einstein condensation in a monocrystalline perovskite
waveguide, we demonstrate the first room-temperature exciton-polariton neural
network. Its performance is verified in various machine learning tasks,
including binary classification, and object detection. Our result is a crucial
milestone in the development of practical applications of polariton neural
networks and provides new perspectives for optical computing accelerators based
on perovskites.
|
[{'version': 'v1', 'created': 'Sat, 14 Dec 2024 15:33:19 GMT'}]
|
2024-12-17
|
Wenjie Chen (1), Zichang Lin (1), Xinxin Zhang (1), Hao Zhou (2),
Yuegang Zhang (1) ((1) Department of Physics, Tsinghua University,(2)
Institute of AI Industry Research, Tsinghua University)
|
AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials
for Magnesium Batteries
| null | null | null |
cond-mat.mtrl-sci
|
Magnesium-ion batteries hold promise as future energy storage solution, yet
current Mg cathodes are challenged by low voltage and specific capacity.
Herein, we present an AI-driven workflow for discovering high-performance Mg
cathode materials. Utilizing the common characteristics of various ionic
intercalation-type electrodes, we design and train a Crystal Graph
Convolutional Neural Network model that can accurately predicts electrode
voltages for various ions with mean absolute errors (MAE) between 0.25 and 0.33
V. By deploying the trained model to stable Mg compounds from Materials Project
and GNoME AI dataset, we identify 160 high voltage structures out of 15,308
candidates with voltages above 3.0 V and volumetric capacity over 800 Ah/L. We
further train a precise NequIP model to facilitate accurate and rapid
simulations of Mg ionic conductivity. From the 160 high voltage structures, the
machine learning molecular dynamics simulations have selected 23 cathode
materials with both high energy density and high ionic conductivity. This
AI-driven workflow dramatically boosts the efficiency and precision of material
discovery for multivalent ion batteries, paving the way for advanced Mg battery
development.
|
[{'version': 'v1', 'created': 'Sun, 15 Dec 2024 03:02:33 GMT'}]
|
2024-12-17
|
Daniel Kaplan, Adam Zhang, Joanna Blawat, Rongying Jin, Robert J.
Cava, Viktor Oudovenko, Gabriel Kotliar, Anirvan M. Sengupta and Weiwei Xie
|
Deep Learning Based Superconductivity: Prediction and Experimental Tests
|
Eur. Phys. J. Plus (2025) 140:58
|
10.1140/epjp/s13360-024-05947-w
| null |
cs.LG cond-mat.mtrl-sci cond-mat.str-el
|
The discovery of novel superconducting materials is a longstanding challenge
in materials science, with a wealth of potential for applications in energy,
transportation, and computing. Recent advances in artificial intelligence (AI)
have enabled expediting the search for new materials by efficiently utilizing
vast materials databases. In this study, we developed an approach based on deep
learning (DL) to predict new superconducting materials. We have synthesized a
compound derived from our DL network and confirmed its superconducting
properties in agreement with our prediction. Our approach is also compared to
previous work based on random forests (RFs). In particular, RFs require
knowledge of the chem-ical properties of the compound, while our neural net
inputs depend solely on the chemical composition. With the help of hints from
our network, we discover a new ternary compound
$\textrm{Mo}_{20}\textrm{Re}_{6}\textrm{Si}_{4}$, which becomes superconducting
below 5.4 K. We further discuss the existing limitations and challenges
associated with using AI to predict and, along with potential future research
directions.
|
[{'version': 'v1', 'created': 'Tue, 17 Dec 2024 15:33:48 GMT'}]
|
2025-01-27
|
Z. Awan, J. Shabeer, U. Saleem, S. Mehmood, T. Qadeer
|
Physics Informed Neural Network Enhanced Denoising for Atomic Resolution
STEM Imaging
| null | null | null |
cond-mat.mtrl-sci
|
Atomic resolution STEM images often suffer from noise due to low electron
doses and instrument imperfections, hence it is challenging to obtain critical
structural details required for material analysis. To address the problem, we
propose a Physics-Informed Neural Network (PINN) framework for denoising STEM
images. Our method integrates spectral fidelity, total variation, and
brightness/contrast consistency losses to ensure the preservation of fine
structures, smooth regions, and physical signal intensities, maintaining the
structural integrity of the denoised images. Our proposed method effectively
balances noise reduction with the preservation of atomic resolution details and
complements existing methods, seeking to enhance the utility of STEM images in
material characterization and analysis.
|
[{'version': 'v1', 'created': 'Tue, 17 Dec 2024 18:07:19 GMT'}]
|
2024-12-18
|
Yash Pathak, Laxman Prasad Goswami, Bansi Dhar Malhotra, and Rishu
Chaujar
|
Artificial Neural Network based Modelling for Variational Effect on
Double Metal Double Gate Negative Capacitance FET
| null | null | null |
cond-mat.mtrl-sci physics.app-ph
|
In this work, we have implemented an accurate machine-learning approach for
predicting various key analog and RF parameters of Negative Capacitance
Field-Effect Transistors (NCFETs). Visual TCAD simulator and the Python
high-level language were employed for the entire simulation process. However,
the computational cost was found to be excessively high. The machine learning
approach represents a novel method for predicting the effects of different
sources on NCFETs while also reducing computational costs. The algorithm of an
artificial neural network can effectively predict multi-input to single-output
relationships and enhance existing techniques. The analog parameters of Double
Metal Double Gate Negative Capacitance FETs (D2GNCFETs) are demonstrated across
various temperatures ($T$), oxide thicknesses ($T_{ox}$), substrate thicknesses
($T_{sub}$), and ferroelectric thicknesses ($T_{Fe}$). Notably, at $T=300K$,
the switching ratio is higher and the leakage current is $84$ times lower
compared to $T=500K$. Similarly, at ferroelectric thicknesses $T_{Fe}=4nm$, the
switching ratio improves by $5.4$ times compared to $T_{Fe}=8nm$. Furthermore,
at substrate thicknesses $T_{sub}=3nm$, switching ratio increases by $81\%$
from $T_{sub}=7nm$. For oxide thicknesses at $T_{ox}=0.8nm$, the ratio
increases by $41\%$ compared to $T_{ox}=0.4nm$. The analysis reveals that
$T_{Fe}=4nm$, $T=300K$, $T_{ox}=0.8nm$, and $T_{sub}=3nm$ represent the optimal
settings for D2GNCFETs, resulting in significantly improved performance. These
findings can inform various applications in nanoelectronic devices and
integrated circuit (IC) design.
|
[{'version': 'v1', 'created': 'Wed, 18 Dec 2024 11:28:38 GMT'}]
|
2024-12-20
|
Christopher W. Adair, Oliver K. Johnson
|
A Decision Transformer Approach to Grain Boundary Network Optimization
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
As microstructure property models improve, additional information from
crystallographic degrees of freedom and grain boundary networks (GBNs) can be
included in microstructure design problems. However, the high dimensional
nature of including this information precludes the use of many common
optimization approaches and requires less efficient methods to generate quality
designs. Previous work demonstrated that human-in-the-loop optimization,
instantiated as a video game, achieved high-quality, efficient solutions to
these design problems. However, such data is expensive to obtain. In the
present work, we show how a Decision Transformer machine learning (ML) model
can be used to learn from the optimization trajectories generated by human
players, and subsequently solve materials design problems. We compare the ML
optimization trajectories against players and a common global optimization
algorithm: simulated annealing (SA). We find that the ML model exhibits a
validation accuracy of 84% against player decisions, and achieves solutions of
comparable quality to SA (92%), but does so using three orders of magnitude
fewer iterations. We find that the ML model generalizes in important and
surprising ways, including the ability to train using a simple constitutive
structure-property model and then solve microstructure design problems for a
different, higher-fidelity, constitutive structure-property model without any
retraining. These results demonstrate the potential of Decision Transformer
models for the solution of materials design problems.
|
[{'version': 'v1', 'created': 'Thu, 19 Dec 2024 20:51:54 GMT'}]
|
2024-12-23
|
Chen Shen, Siamak Attarian, Yixuan Zhang, Hongbin Zhang, Mark Asta,
Izabela Szlufarska, Dane Morgan
|
SuperSalt: Equivariant Neural Network Force Fields for Multicomponent
Molten Salts System
| null | null | null |
cond-mat.mtrl-sci
|
Molten salts are crucial for clean energy applications, yet exploring their
thermophysical properties across diverse chemical space remains challenging. We
present the development of a machine learning interatomic potential (MLIP)
called SuperSalt, which targets 11-cation chloride melts and captures the
essential physics of molten salts with near-DFT accuracy. Using an efficient
workflow that integrates systems of one, two, and 11 components, the SuperSalt
potential can accurately predict thermophysical properties such as density,
bulk modulus, thermal expansion, and heat capacity. Our model is validated
across a broad chemical space, demonstrating excellent transferability. We
further illustrate how Bayesian optimization combined with SuperSalt can
accelerate the discovery of optimal salt compositions with desired properties.
This work provides a foundation for future studies that allows easy extensions
to more complex systems, such as those containing additional elements.
SuperSalt represents a shift towards a more universal, efficient, and accurate
modeling of molten salts for advanced energy applications.
|
[{'version': 'v1', 'created': 'Thu, 26 Dec 2024 21:32:08 GMT'}]
|
2024-12-30
|
Cameron Cook and Carlos Blanco and Juri Smirnov
|
Deep learning optimal molecular scintillators for dark matter direct
detection
| null | null | null |
hep-ph cond-mat.mtrl-sci hep-ex hep-th physics.ins-det
|
Direct searches for sub-GeV dark matter are limited by the intrinsic quantum
properties of the target material. In this proof-of-concept study, we argue
that this problem is particularly well suited for machine learning. We
demonstrate that a simple neural architecture consisting of a variational
autoencoder and a multi-layer perceptron can efficiently generate unique
molecules with desired properties. In specific, the energy threshold and signal
(quantum) efficiency determine the minimum mass and cross section to which a
detector can be sensitive. Organic molecules present a particularly interesting
class of materials with intrinsically anisotropic electronic responses and
$\mathcal{O}$(few) eV excitation energies. However, the space of possible
organic compounds is intractably large, which makes traditional database
screening challenging. We adopt excitation energies and proxy transition matrix
elements as target properties learned by our network. Our model is able to
generate molecules that are not in even the most expansive quantum chemistry
databases and predict their relevant properties for high-throughput and
efficient screening. Following a massive generation of novel molecules, we use
clustering analysis to identify some of the most promising molecular structures
that optimise the desired molecular properties for dark matter detection.
|
[{'version': 'v1', 'created': 'Mon, 30 Dec 2024 19:00:00 GMT'}, {'version': 'v2', 'created': 'Wed, 8 Jan 2025 17:16:30 GMT'}]
|
2025-01-09
|
Suman Itani, Yibo Zhang, Jiadong Zang
|
Large Language Model-Driven Database for Thermoelectric Materials
| null | null | null |
cond-mat.mtrl-sci cs.DL
|
Thermoelectric materials provide a sustainable way to convert waste heat into
electricity. However, data-driven discovery and optimization of these materials
are challenging because of a lack of a reliable database. Here we developed a
comprehensive database of 7,123 thermoelectric compounds, containing key
information such as chemical composition, structural detail, seebeck
coefficient, electrical and thermal conductivity, power factor, and figure of
merit (ZT). We used the GPTArticleExtractor workflow, powered by large language
models (LLM), to extract and curate data automatically from the scientific
literature published in Elsevier journals. This process enabled the creation of
a structured database that addresses the challenges of manual data collection.
The open access database could stimulate data-driven research and advance
thermoelectric material analysis and discovery.
|
[{'version': 'v1', 'created': 'Tue, 31 Dec 2024 17:50:46 GMT'}]
|
2025-01-03
|
Sarah I. Allec, Maxim Ziatdinov
|
Active and transfer learning with partially Bayesian neural networks for
materials and chemicals
| null | null | null |
cond-mat.dis-nn cond-mat.mtrl-sci physics.data-an
|
Active learning, an iterative process of selecting the most informative data
points for exploration, is crucial for efficient characterization of materials
and chemicals property space. Neural networks excel at predicting these
properties but lack the uncertainty quantification needed for active
learning-driven exploration. Fully Bayesian neural networks, in which weights
are treated as probability distributions inferred via advanced Markov Chain
Monte Carlo methods, offer robust uncertainty quantification but at high
computational cost. Here, we show that partially Bayesian neural networks
(PBNNs), where only selected layers have probabilistic weights while others
remain deterministic, can achieve accuracy and uncertainty estimates on active
learning tasks comparable to fully Bayesian networks at lower computational
cost. Furthermore, by initializing prior distributions with weights pre-trained
on theoretical calculations, we demonstrate that PBNNs can effectively leverage
computational predictions to accelerate active learning of experimental data.
We validate these approaches on both molecular property prediction and
materials science tasks, establishing PBNNs as a practical tool for active
learning with limited, complex datasets.
|
[{'version': 'v1', 'created': 'Wed, 1 Jan 2025 20:48:26 GMT'}, {'version': 'v2', 'created': 'Mon, 7 Apr 2025 20:33:33 GMT'}]
|
2025-04-09
|
Akshansh Mishra
|
Advanced Displacement Magnitude Prediction in Multi-Material Architected
Lattice Structure Beams Using Physics Informed Neural Network Architecture
| null | null | null |
cs.AI cond-mat.mtrl-sci cs.CE cs.LG cs.NE
|
This paper proposes an innovative method for predicting deformation in
architected lattice structures that combines Physics-Informed Neural Networks
(PINNs) with finite element analysis. A thorough study was carried out on
FCC-based lattice beams utilizing five different materials (Structural Steel,
AA6061, AA7075, Ti6Al4V, and Inconel 718) under varied edge loads (1000-10000
N). The PINN model blends data-driven learning with physics-based limitations
via a proprietary loss function, resulting in much higher prediction accuracy
than linear regression. PINN outperforms linear regression, achieving greater
R-square (0.7923 vs 0.5686) and lower error metrics (MSE: 0.00017417 vs
0.00036187). Among the materials examined, AA6061 had the highest displacement
sensitivity (0.1014 mm at maximum load), while Inconel718 had better structural
stability.
|
[{'version': 'v1', 'created': 'Tue, 31 Dec 2024 00:15:58 GMT'}]
|
2025-01-08
|
Malte Grunert, Max Gro{\ss}mann, Jonas H\"anseroth, Aaron Fl\"ototto,
Jules Oumard, Johannes Laurenz Wolf, Erich Runge, Christian Dre{\ss}ler
|
Modelling complex proton transport phenomena -- Exploring the limits of
fine-tuning and transferability of foundational machine-learned force fields
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
The solid acids CsH$_2$PO$_4$ and Cs$_7$(H$_4$PO$_4$)(H$_2$PO$_4$)$_8$ pose
significant challenges for the simulation of proton transport phenomena. In
this work, we use the recently developed machine-learned force field (MLFF)
MACE to model the proton dynamics on nanosecond time scales for these systems
and compare its performance with long-term ab initio molecular dynamics (AIMD)
simulations. The MACE-MP-0 foundation model shows remarkable performance for
all observables derived from molecular dynamics (MD) simulations, but minor
quantitative discrepancies remain compared to the AIMD reference data. However,
we show that minimal fine-tuning -- fitting to as little as 1 ps of AIMD data
-- leads to full quantitative agreement between the radial distribution
functions of MACE force field and AIMD simulations. In addition, we show that
traditional long-term AIMD simulations fail to capture the correct qualitative
trends in diffusion coefficients and activation energies for these solid acids
due to the limited accessible time scale. In contrast, accurate and convergent
diffusion coefficients can be reliably obtained through multi-nanosecond long
MD simulations using machine-learned force fields. The obtained qualitative and
quantitative behavior of the converged diffusion coefficients and activation
energies now matches the experimental trends for both solid acids, in contrast
to previous AIMD simulations that yielded a qualitatively wrong picture.
|
[{'version': 'v1', 'created': 'Wed, 8 Jan 2025 23:20:42 GMT'}]
|
2025-01-10
|
Weiqing Zhou, Daye Zheng, Qianrui Liu, Denghui Lu, Yu Liu, Peize Lin,
Yike Huang, Xingliang Peng, Jie J. Bao, Chun Cai, Zuxin Jin, Jing Wu,
Haochong Zhang, Gan Jin, Yuyang Ji, Zhenxiong Shen, Xiaohui Liu, Liang Sun,
Yu Cao, Menglin Sun, Jianchuan Liu, Tao Chen, Renxi Liu, Yuanbo Li, Haozhi
Han, Xinyuan Liang, Taoni Bao, Nuo Chen, Hongxu Ren, Xiaoyang Zhang, Zhaoqing
Liu, Yiwei Fu, Maochang Liu, Zhuoyuan Li, Tongqi Wen, Zechen Tang, Yong Xu,
Wenhui Duan, Xiaoyang Wang, Qiangqiang Gu, Fu-Zhi Dai, Qijing Zheng, Jin
Zhao, Yuzhi Zhang, Qi Ou, Hong Jiang, Shi Liu, Ben Xu, Shenzhen Xu, Xinguo
Ren, Lixin He, Linfeng Zhang, and Mohan Chen
|
ABACUS: An Electronic Structure Analysis Package for the AI Era
| null | null | null |
cond-mat.mtrl-sci cond-mat.mes-hall
|
ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source
software for first-principles electronic structure calculations and molecular
dynamics simulations. It mainly features density functional theory (DFT) and is
compatible with both plane-wave basis sets and numerical atomic orbital basis
sets. ABACUS serves as a platform that facilitates the integration of various
electronic structure methods, such as Kohn-Sham DFT, stochastic DFT,
orbital-free DFT, and real-time time-dependent DFT, etc. In addition, with the
aid of high-performance computing, ABACUS is designed to perform efficiently
and provide massive amounts of first-principles data for generating
general-purpose machine learning potentials, such as DPA models. Furthermore,
ABACUS serves as an electronic structure platform that interfaces with several
AI-assisted algorithms and packages, such as DeePKS-kit, DeePMD, DP-GEN, DeepH,
DeePTB, etc.
|
[{'version': 'v1', 'created': 'Wed, 15 Jan 2025 10:13:57 GMT'}, {'version': 'v2', 'created': 'Tue, 21 Jan 2025 01:59:06 GMT'}]
|
2025-01-22
|
Indrajeet Mandal, Jitendra Soni, Mohd Zaki, Morten M. Smedskjaer,
Katrin Wondraczek, Lothar Wondraczek, Nitya Nand Gosvami and N. M. Anoop
Krishnan
|
Autonomous Microscopy Experiments through Large Language Model Agents
| null | null | null |
cs.CY cond-mat.mtrl-sci cs.AI physics.ins-det
|
The emergence of large language models (LLMs) has accelerated the development
of self-driving laboratories (SDLs) for materials research. Despite their
transformative potential, current SDL implementations rely on rigid, predefined
protocols that limit their adaptability to dynamic experimental scenarios
across different labs. A significant challenge persists in measuring how
effectively AI agents can replicate the adaptive decision-making and
experimental intuition of expert scientists. Here, we introduce AILA
(Artificially Intelligent Lab Assistant), a framework that automates atomic
force microscopy (AFM) through LLM-driven agents. Using AFM as an experimental
testbed, we develop AFMBench-a comprehensive evaluation suite that challenges
AI agents based on language models like GPT-4o and GPT-3.5 to perform tasks
spanning the scientific workflow: from experimental design to results analysis.
Our systematic assessment shows that state-of-the-art language models struggle
even with basic tasks such as documentation retrieval, leading to a significant
decline in performance in multi-agent coordination scenarios. Further, we
observe that LLMs exhibit a tendency to not adhere to instructions or even
divagate to additional tasks beyond the original request, raising serious
concerns regarding safety alignment aspects of AI agents for SDLs. Finally, we
demonstrate the application of AILA on increasingly complex experiments
open-ended experiments: automated AFM calibration, high-resolution feature
detection, and mechanical property measurement. Our findings emphasize the
necessity for stringent benchmarking protocols before deploying AI agents as
laboratory assistants across scientific disciplines.
|
[{'version': 'v1', 'created': 'Wed, 18 Dec 2024 09:35:28 GMT'}]
|
2025-01-22
|
Qinyi Tian, Winston Lindqwister, Manolis Veveakis, Laura E. Dalton
|
Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain
Knowledge for Material Inverse Problems
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.CE
|
Advancements in deep learning and machine learning have improved the ability
to model complex, nonlinear relationships, such as those encountered in complex
material inverse problems. However, the effectiveness of these methods often
depends on large datasets, which are not always available. In this study, the
incorporation of domain-specific knowledge of the mechanical behavior of
material microstructures is investigated to evaluate the impact on the
predictive performance of the models in data-scarce scenarios. To overcome data
limitations, a two-step framework, Learning Latent Hardening (LLH), is
proposed. In the first step of LLH, a Deep Neural Network is employed to
reconstruct full stress-strain curves from randomly selected portions of the
stress-strain curves to capture the latent mechanical response of a material
based on key microstructural features. In the second step of LLH, the results
of the reconstructed stress-strain curves are leveraged to predict key
microstructural features of porous materials. The performance of six deep
learning and/or machine learning models trained with and without domain
knowledge are compared: Convolutional Neural Networks, Deep Neural Networks,
Extreme Gradient Boosting, K-Nearest Neighbors, Long Short-Term Memory, and
Random Forest. The results from the models with domain-specific information
consistently achieved higher $R^2$ values compared to models without prior
knowledge. Models without domain knowledge missed critical patterns linking
stress-strain behavior to microstructural changes, whereas domain-informed
models better identified essential stress-strain features predictive of
microstructure. These findings highlight the importance of integrating
domain-specific knowledge with deep learning to achieve accurate outcomes in
materials science.
|
[{'version': 'v1', 'created': 'Fri, 17 Jan 2025 03:09:25 GMT'}, {'version': 'v2', 'created': 'Sat, 15 Feb 2025 04:15:56 GMT'}, {'version': 'v3', 'created': 'Wed, 9 Apr 2025 03:04:57 GMT'}]
|
2025-04-10
|
Muhammad Usman
|
Nanowire design by deep learning for energy efficient photonic
technologies
| null | null | null |
physics.optics cond-mat.mtrl-sci
|
This work describes our vision and proposal for the design of next generation
photonic devices based on custom-designed semiconductor nanowires. The
integration of multi-million-atom electronic structure and optical simulations
with the supervised machine learning models will pave the way for
transformative nanowire-based technologies, offering opportunities for the next
generation energy-efficient greener photonics.
|
[{'version': 'v1', 'created': 'Sun, 19 Jan 2025 01:15:44 GMT'}]
|
2025-01-22
|
Jinwoong Chae, Sungwook Hong, Sungkyu Kim, Sungroh Yoon, and Gunn Kim
|
CNN-based TEM image denoising from first principles
| null | null | null |
cond-mat.mtrl-sci cs.CV eess.IV
|
Transmission electron microscope (TEM) images are often corrupted by noise,
hindering their interpretation. To address this issue, we propose a deep
learning-based approach using simulated images. Using density functional theory
calculations with a set of pseudo-atomic orbital basis sets, we generate highly
accurate ground truth images. We introduce four types of noise into these
simulations to create realistic training datasets. Each type of noise is then
used to train a separate convolutional neural network (CNN) model. Our results
show that these CNNs are effective in reducing noise, even when applied to
images with different noise levels than those used during training. However, we
observe limitations in some cases, particularly in preserving the integrity of
circular shapes and avoiding visible artifacts between image patches. To
overcome these challenges, we propose alternative training strategies and
future research directions. This study provides a valuable framework for
training deep learning models for TEM image denoising.
|
[{'version': 'v1', 'created': 'Mon, 20 Jan 2025 02:19:26 GMT'}]
|
2025-01-22
|
Ting Bao, Ning Mao, Wenhui Duan, Yong Xu, Adrian Del Maestro, and Yang
Zhang
|
Transfer learning electronic structure: millielectron volt accuracy for
sub-million-atom moir\'e semiconductor
| null | null | null |
cond-mat.mtrl-sci cond-mat.str-el
|
The integration of density functional theory (DFT) with machine learning
enables efficient \textit{ab initio} electronic structure calculations for
ultra-large systems. In this work, we develop a transfer learning framework
tailored for long-wavelength moir\'e systems. To balance efficiency and
accuracy, we adopt a two-step transfer learning strategy: (1) the model is
pre-trained on a large dataset of computationally inexpensive non-twisted
structures until convergence, and (2) the network is then fine-tuned using a
small set of computationally expensive twisted structures. Applying this method
to twisted MoTe$_2$, the neural network model generates the resulting
Hamiltonian for a 1000-atom system in 200 seconds, achieving a mean absolute
error below 0.1 meV. To demonstrate $O(N)$ scalability, we model nanoribbon
systems with up to 0.25 million atoms ($\sim9$ million orbitals), accurately
capturing edge states consistent with predicted Chern numbers. This approach
addresses the challenges of accuracy, efficiency, and scalability, offering a
viable alternative to conventional DFT and enabling the exploration of
electronic topology in large scale moir\'e systems towards simulating realistic
device architectures.
|
[{'version': 'v1', 'created': 'Tue, 21 Jan 2025 19:00:31 GMT'}]
|
2025-01-23
|
Pierre-Paul De Breuck, Hashim A. Piracha, Gian-Marco Rignanese, and
Miguel A. L. Marques
|
A generative material transformer using Wyckoff representation
| null | null | null |
cond-mat.mtrl-sci
|
Materials play a critical role in various technological applications.
Identifying and enumerating stable compounds, those near the convex hull, is
therefore essential. Despite recent progress, generative models either have a
relatively low rate of stable compounds, are computationally expensive, or lack
symmetry. In this work we present Matra-Genoa, an autoregressive transformer
model built on invertible tokenized representations of symmetrized crystals,
including free coordinates. This approach enables sampling from a hybrid action
space. The model is trained across the periodic table and space groups and can
be conditioned on specific properties. We demonstrate its ability to generate
stable, novel, and unique crystal structures by conditioning on the distance to
the convex hull. Resulting structures are 8 times more likely to be stable than
baselines using PyXtal with charge compensation, while maintaining high
computational efficiency. We also release a dataset of 3 million unique
crystals generated by our method, including 4,000 compounds verified by
density-functional theory to be within 0.001 eV/atom of the convex hull.
|
[{'version': 'v1', 'created': 'Mon, 27 Jan 2025 13:46:00 GMT'}, {'version': 'v2', 'created': 'Wed, 29 Jan 2025 18:01:43 GMT'}]
|
2025-01-30
|
Yingjie Zhao and Zhiping Xu
|
Neural Network Modeling of Microstructure Complexity Using Digital
Libraries
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.CE nlin.PS physics.comp-ph
|
Microstructure evolution in matter is often modeled numerically using field
or level-set solvers, mirroring the dual representation of spatiotemporal
complexity in terms of pixel or voxel data, and geometrical forms in vector
graphics. Motivated by this analog, as well as the structural and event-driven
nature of artificial and spiking neural networks, respectively, we evaluate
their performance in learning and predicting fatigue crack growth and Turing
pattern development. Predictions are made based on digital libraries
constructed from computer simulations, which can be replaced by experimental
data to lift the mathematical overconstraints of physics. Our assessment
suggests that the leaky integrate-and-fire neuron model offers superior
predictive accuracy with fewer parameters and less memory usage, alleviating
the accuracy-cost tradeoff in contrast to the common practices in computer
vision tasks. Examination of network architectures shows that these benefits
arise from its reduced weight range and sparser connections. The study
highlights the capability of event-driven models in tackling problems with
evolutionary bulk-phase and interface behaviors using the digital library
approach.
|
[{'version': 'v1', 'created': 'Thu, 30 Jan 2025 07:44:21 GMT'}]
|
2025-01-31
|
Congcong Cui, Guangfeng Wei, Matthias Saba and Lu Han
|
Comprehensive Enumeration of Three-Dimensional Photonic Crystals Enabled
through Deep Learning Assisted Fourier Synthesis
| null | null | null |
physics.optics cond-mat.mtrl-sci
|
Three-dimensional (3D) photonic structures enable numerous applications
through their unique ability to guide, trap, and manipulate light. Constructing
new functional photonic crystals remains a significant challenge since
traditional design principles based on band structure calculations require
numerous time-consuming computations. Additionally, traditional design is based
on enumerated structures making it difficult to find novel functional
geometries. Here, we propose an ultra-fast photonic crystal performance
prediction method to enable efficient structure optimization of arbitrary 3D
photonic crystals even with multiple variable modulation. Our methodology
combines Fourier synthesis-enabling the creation of any smooth geometry within
a crystallographic space group-with deep learning, which facilitates efficient
photonic characterization within the vast parameter space. Over 2 million
structures can be explored within 2 hours using a mainstream desktop
workstation. The ideal structures with desired band properties, such as large
photonic bandgap, specific frequency ranges, etc., could be rapidly discovered.
We systematically confirmed the well-documented assumption that the most
significant photonic bandgaps are found in minimal surface morphologies, in
which the single diamond (dia net) with Fd3m (227) symmetry reigns supreme
among known photonic structures, followed by the chiral single gyroid (srs net)
with I4132 (214) symmetry. Additionally, a less well-known 3D photonic crystal
with lcs topology within Ia3d (230) was rediscovered to exhibit a wide complete
photonic bandgap, comparable to the diamond and the gyroid net. Our method not
only validates the assumed hierarchy of photonic structures but also lays the
foundation for the tailored design of functional materials and offers fresh
insights into the advancement of next-generation optical devices and
information technology.
|
[{'version': 'v1', 'created': 'Thu, 30 Jan 2025 17:07:56 GMT'}]
|
2025-01-31
|
Sumner B. Harris, Patrick T. Gemperline, Christopher M. Rouleau, Rama
K. Vasudevan, Ryan B. Comes
|
Deep learning with reflection high-energy electron diffraction images to
predict cation ratio in Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films
| null |
10.1021/acs.nanolett.5c00787
| null |
cond-mat.mtrl-sci
|
Machine learning (ML) with in situ diagnostics offers a transformative
approach to accelerate, understand, and control thin film synthesis by
uncovering relationships between synthesis conditions and material properties.
In this study, we demonstrate the application of deep learning to predict the
stoichiometry of Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films using reflection
high-energy electron diffraction images acquired during pulsed laser
deposition. A gated convolutional neural network trained for regression of the
Sr atomic fraction achieved accurate predictions with a small dataset of 31
samples. Explainable AI techniques revealed a previously unknown correlation
between diffraction streak features and cation stoichiometry in
Sr$_{2x}$Ti$_{2(1-x)}$O$_{3}$ thin films. Our results demonstrate how ML can be
used to transform a ubiquitous in situ diagnostic tool, that is usually limited
to qualitative assessments, into a quantitative surrogate measurement of
continuously valued thin film properties. Such methods are critically needed to
enable real-time control, autonomous workflows, and accelerate traditional
synthesis approaches.
|
[{'version': 'v1', 'created': 'Thu, 30 Jan 2025 17:41:20 GMT'}, {'version': 'v2', 'created': 'Thu, 27 Mar 2025 14:17:38 GMT'}]
|
2025-04-09
|
Yunyang Li, Lin Huang, Zhihao Ding, Chu Wang, Xinran Wei, Han Yang,
Zun Wang, Chang Liu, Yu Shi, Peiran Jin, Jia Zhang, Mark Gerstein, Tao Qin
|
E2Former: A Linear-time Efficient and Equivariant Transformer for
Scalable Molecular Modeling
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Equivariant Graph Neural Networks (EGNNs) have demonstrated significant
success in modeling microscale systems, including those in chemistry, biology
and materials science. However, EGNNs face substantial computational challenges
due to the high cost of constructing edge features via spherical tensor
products, making them impractical for large-scale systems. To address this
limitation, we introduce E2Former, an equivariant and efficient transformer
architecture that incorporates the Wigner $6j$ convolution (Wigner $6j$ Conv).
By shifting the computational burden from edges to nodes, the Wigner $6j$ Conv
reduces the complexity from $O(|\mathcal{E}|)$ to $ O(| \mathcal{V}|)$ while
preserving both the model's expressive power and rotational equivariance. We
show that this approach achieves a 7x-30x speedup compared to conventional
$\mathrm{SO}(3)$ convolutions. Furthermore, our empirical results demonstrate
that the derived E2Former mitigates the computational challenges of existing
approaches without compromising the ability to capture detailed geometric
information. This development could suggest a promising direction for scalable
and efficient molecular modeling.
|
[{'version': 'v1', 'created': 'Fri, 31 Jan 2025 15:22:58 GMT'}, {'version': 'v2', 'created': 'Mon, 3 Feb 2025 18:46:30 GMT'}]
|
2025-02-06
|
Qiangqiang Gu and Shishir Kumar Pandey and Zhanghao Zhouyin
|
Deep Neural Network for Phonon-Assisted Optical Spectra in
Semiconductors
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Ab initio based accurate simulation of phonon-assisted optical spectra of
semiconductors at finite temperatures remains a formidable challenge, as it
requires large supercells for phonon sampling and computationally expensive
high-accuracy exchange-correlation (XC) functionals. In this work, we present
an efficient approach that combines deep learning tight-binding and potential
models to address this challenge with ab initio fidelity. By leveraging
molecular dynamics for atomic configuration sampling and deep learning-enabled
rapid Hamiltonian evaluation, our approach enables large-scale simulations of
temperature-dependent optical properties using advanced XC functionals (HSE,
SCAN). Demonstrated on silicon and gallium arsenide across temperature 100-400
K, the method accurately captures phonon-induced bandgap renormalization and
indirect/direct absorption processes which are in excellent agreement with
experimental findings over five orders of magnitude. This work establishes a
pathway for high-throughput investigation of electron-phonon coupled phenomena
in complex materials, overcoming traditional computational limitations arising
from large supercell used with computationally expensive XC-functionals.
|
[{'version': 'v1', 'created': 'Sun, 2 Feb 2025 13:37:56 GMT'}, {'version': 'v2', 'created': 'Tue, 6 May 2025 09:49:33 GMT'}]
|
2025-05-07
|
Yoyo Hinuma
|
Direct derivation of anisotropic atomic displacement parameters from
molecular dynamics simulations in extended solids with substitutional
disorder using a neural network potential
| null | null | null |
cond-mat.mtrl-sci
|
Atomic displacement parameters (ADPs) are crystallographic information that
describe the statistical distribution of atoms around an atom site. Anisotropic
ADPs by atom were directly derived from classical molecular dynamics (MD)
simulations using a universal machine-learned potential. The (co)valences of
atom positions were taken over recordings at different time steps in a single
MD simulation. The procedure was demonstrated on extended solids, namely
rocksalt structure MgO and three thermoelectric materials, Ag8SnSe6, Na2In2Sn4,
and BaCu1.14In0.86P2. Unlike the very frequently used lattice dynamics
approach, the MD approach can obtain ADPs in crystals with substitutional
disorder and explicitly at finite temperature, but not under conditions where
atoms migrate in the crystal. The calculated ADP becomes ->0 at temperature ->0
and the ADP is proportional to the temperature when the atom is in a harmonic
potential and the sole contribution to the actual non-zero ADP is from the
zero-point motion. The zero-point motion contribution can be estimated from the
proportionality constant assuming this Einstein model. ADPs from MD simulations
would act as a tool complementing experimental efforts to understand the
crystal structure including the distribution of atoms around atom sites.
|
[{'version': 'v1', 'created': 'Tue, 4 Feb 2025 06:44:01 GMT'}, {'version': 'v2', 'created': 'Mon, 28 Apr 2025 04:34:56 GMT'}]
|
2025-04-29
|
Yoyo Hinuma
|
Neural network potential molecular dynamics simulations of
(La,Ce,Pr,Nd)0.95(Mg,Zn,Pb,Cd,Ca,Sr,Ba)0.05F2.95
|
J. Phys. Chem. B 2024,128,49,12171
|
10.1021/acs.jpcb.4c05624
| null |
cond-mat.mtrl-sci
|
Tysonite structure fluorides doped with divalent cations, represented by
Ce0.95Ca0.05F2.95, are a class of good F- ion conductors together with
fluorite-structured compounds. Computational understanding of the F- conduction
process is difficult because of the complicated interactions between three
symmetrically distinct F sites and the experimentally observed change in the F
diffusion mechanism slightly above room temperature, effectively making first
principles molecular dynamics (FP-MD) simulations, which are often conducted
well above the transition temperature, useless when analyzing behavior below
the transition point. Neural network potential (NNP) MD simulations showed that
the F diffusion coefficient is higher when the divalent dopant cation size is
similar to the trivalent cation size. The diffusion behavior of F in different
sites changes at roughly 500 K in Ce0.95Ca0.05F2.95 because only the F1 site
sublattice contributes to F diffusion below this temperature but the remaining
F2 and F3 sublattices becomes gradually active above this temperature. The
paradox of higher diffusion coefficients in CeF3-based compounds than similar
LaF3-based compounds even though the lattice parameters are larger in the
latter may be caused by a shallower potential of Ce and F in CeF3 compared to
the LaF3 counterparts.
|
[{'version': 'v1', 'created': 'Tue, 4 Feb 2025 15:26:13 GMT'}]
|
2025-02-05
|
Mouyang Cheng, Chu-Liang Fu, Ryotaro Okabe, Abhijatmedhi
Chotrattanapituk, Artittaya Boonkird, Nguyen Tuan Hung, Mingda Li
|
AI-driven materials design: a mini-review
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Materials design is an important component of modern science and technology,
yet traditional approaches rely heavily on trial-and-error and can be
inefficient. Computational techniques, enhanced by modern artificial
intelligence (AI), have greatly accelerated the design of new materials. Among
these approaches, inverse design has shown great promise in designing materials
that meet specific property requirements. In this mini-review, we summarize key
computational advancements for materials design over the past few decades. We
follow the evolution of relevant materials design techniques, from
high-throughput forward machine learning (ML) methods and evolutionary
algorithms, to advanced AI strategies like reinforcement learning (RL) and deep
generative models. We highlight the paradigm shift from conventional screening
approaches to inverse generation driven by deep generative models. Finally, we
discuss current challenges and future perspectives of materials inverse design.
This review may serve as a brief guide to the approaches, progress, and outlook
of designing future functional materials with technological relevance.
|
[{'version': 'v1', 'created': 'Wed, 5 Feb 2025 05:59:15 GMT'}]
|
2025-02-06
|
Madeleine D. Breshears, Rajiv Giridharagopal, David S. Ginger
|
Multi-Output Convolutional Neural Network for Improved Parameter
Extraction in Time-Resolved Electrostatic Force Microscopy Data
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Time-resolved scanning probe microscopy methods, like time-resolved
electrostatic force microscopy (trEFM), enable imaging of dynamic processes
ranging from ion motion in batteries to electronic dynamics in microstructured
thin film semiconductors for solar cells. Reconstructing the underlying
physical dynamics from these techniques can be challenging due to the interplay
of cantilever physics with the actual transient kinetics of interest in the
resulting signal. Previously, quantitative trEFM used empirical calibration of
the cantilever or feed-forward neural networks trained on simulated data to
extract the physical dynamics of interest. Both these approaches are limited by
interpreting the underlying signal as a single exponential function, which
serves as an approximation but does not adequately reflect many realistic
systems. Here, we present a multi-branched, multi-output convolutional neural
network (CNN) that uses the trEFM signal in addition to the physical cantilever
parameters as input. The trained CNN accurately extracts parameters describing
both single-exponential and bi-exponential underlying functions, and more
accurately reconstructs real experimental data in the presence of noise. This
work demonstrates an application of physics-informed machine learning to
complex signal processing tasks, enabling more efficient and accurate analysis
of trEFM.
|
[{'version': 'v1', 'created': 'Wed, 5 Feb 2025 19:37:24 GMT'}]
|
2025-02-07
|
Isa\'ias Rodr\'iguez
|
Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous
Porous Palladium: A Deep Neural Network Approach
| null | null | null |
cond-mat.mtrl-sci cond-mat.dis-nn
|
Amorphous and amorphous porous palladium are key materials for catalysis,
hydrogen storage, and functional applications, but their complex structures
present computational challenges. This study employs a deep neural network
trained on 33,310 atomic configurations from ab initio molecular dynamics
simulations to model their interatomic potential. The AI-driven approach
accurately predicts structural and thermal properties while significantly
reducing computational costs. Validation against density functional theory
confirms its reliability in reproducing forces, energies, and structural
distributions. These findings highlight AI's potential in accelerating the
study of amorphous materials and advancing their applications in energy and
catalysis.
|
[{'version': 'v1', 'created': 'Thu, 30 Jan 2025 23:11:10 GMT'}]
|
2025-02-11
|
Kevin Han Huang, Ni Zhan, Elif Ertekin, Peter Orbanz, Ryan P. Adams
|
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron
Schr\"odinger Equation
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Incorporating group symmetries into neural networks has been a cornerstone of
success in many AI-for-science applications. Diagonal groups of isometries,
which describe the invariance under a simultaneous movement of multiple
objects, arise naturally in many-body quantum problems. Despite their
importance, diagonal groups have received relatively little attention, as they
lack a natural choice of invariant maps except in special cases. We study
different ways of incorporating diagonal invariance in neural network ans\"atze
trained via variational Monte Carlo methods, and consider specifically data
augmentation, group averaging and canonicalization. We show that, contrary to
standard ML setups, in-training symmetrization destabilizes training and can
lead to worse performance. Our theoretical and numerical results indicate that
this unexpected behavior may arise from a unique computational-statistical
tradeoff not found in standard ML analyses of symmetrization. Meanwhile, we
demonstrate that post hoc averaging is less sensitive to such tradeoffs and
emerges as a simple, flexible and effective method for improving neural network
solvers.
|
[{'version': 'v1', 'created': 'Fri, 7 Feb 2025 20:37:25 GMT'}]
|
2025-02-11
|
Ziwei Wei, Shuming Wei, Qibin Zeng, Wanheng Lu, Huajun Liu, and
Kaiyang Zeng
|
PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I
Control in Scanning Probe Microscopy
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.app-ph
|
We have developed a Parallel Integrated Control and Training System,
leveraging the deep reinforcement learning to dynamically adjust the control
strategies in real time for scanning probe microscopy techniques.
|
[{'version': 'v1', 'created': 'Tue, 11 Feb 2025 07:43:46 GMT'}]
|
2025-02-12
|
Emir Kocer, Andreas Singraber, Jonas A. Finkler, Philipp Misof, Tsz
Wai Ko, Christoph Dellago, J\"org Behler
|
Iterative charge equilibration for fourth-generation high-dimensional
neural network potentials
| null | null | null |
cond-mat.mtrl-sci
|
Machine learning potentials (MLP) allow to perform large-scale molecular
dynamics simulations with about the same accuracy as electronic structure
calculations provided that the selected model is able to capture the relevant
physics of the system. For systems exhibiting long-range charge transfer,
fourth-generation MLPs need to be used, which take global information about the
system and electrostatic interactions into account. This can be achieved in a
charge equilibration (QEq) step, but the direct solution (dQEq) of the set of
linear equations results in an unfavorable cubic scaling with system size
making this step computationally demanding for large systems. In this work, we
propose an alternative approach that is based on the iterative solution of the
charge equilibration problem (iQEq) to determine the atomic partial charges. We
have implemented the iQEq method, which scales quadratically with system size,
in the parallel molecular dynamics software LAMMPS for the example of a
fourth-generation high-dimensional neural network potential (4G-HDNNP) intended
to be used in combination with the n2p2 library. The method itself is general
and applicable to many different types of fourth-generation MLPs. An assessment
of the accuracy and the efficiency is presented for a benchmark system of
FeCl$_3$ in water.
|
[{'version': 'v1', 'created': 'Tue, 11 Feb 2025 19:27:33 GMT'}, {'version': 'v2', 'created': 'Mon, 17 Mar 2025 18:40:53 GMT'}]
|
2025-03-19
|
Katherine A Koch, Martin Gomez-Dominguez, Esteban Rojas-Gatjens,
Alexander Evju, K Burak Ucer, Juan-Pablo Correa-Baena, and Ajay Ram Srimath
Kandada
|
Fine-Tuning Exciton Polaron Characteristics via Lattice Engineering in
2D Hybrid Perovskites
| null | null | null |
cond-mat.mtrl-sci
|
The layered structure of 2D metal halide perovskites (MHPs) consisting of an
ionic metal halide octahedral layer electronically separated by an organic
cation, exhibits strong coupling between high-binding-energy excitons and
low-energy lattice phonons. Photoexcitations in these systems are believed to
be exciton polarons, Coulombically bound electron-hole pairs dressed by lattice
vibrations. Understanding and controlling the structural and chemical factors
that govern this interaction is crucial for optimizing exciton recombination,
transport, and many-body interactions. Our study examines the role of the
organic cation in a prototypical 2D-MHP system, phenylethylammonium lead
iodide, (PEA)2PbI4, and its halogenated derivatives, (F/Cl-PEA)2PbI4. These
substitutions allow us to probe polaronic effects while maintaining the average
lattice and electronic structure. Using resonant impulsive stimulated Raman
scattering (RISRS), we analyze the metal-halide sub-lattice motion coupled to
excitons. We apply formalism based on a perturbative expansion of the nonlinear
response function on the experimental data to estimate the Huang-Rhys
parameter, $S=1/2 \Delta^2$, to quantify the lattice displacement ($\Delta$)
due to exciton-phonon coupling. A direct correlation emerges between lattice
displacement and octahedral distortion, with F-PEA exhibiting the largest shift
and Cl-PEA exhibiting the least, significantly influencing the fine structure
features in absorption. Additionally, 2D electronic spectroscopy reveals that
F-PEA, with the strongest polaronic coupling, exhibits the least thermal
dephasing, supporting the polaronic protection hypothesis. Our findings suggest
that systematic organic cation substitution serves as a tunable control for the
fine structure in 2D-MHPs, and offers a pathway to mitigate many-body
scattering effects by tailoring the polaronic coupling.
|
[{'version': 'v1', 'created': 'Wed, 12 Feb 2025 15:57:26 GMT'}]
|
2025-02-13
|
Ziyi Chen, Yang Yuan, Siming Zheng, Jialong Guo, Sihan Liang, Yangang
Wang, Zongguo Wang
|
Transformer-Enhanced Variational Autoencoder for Crystal Structure
Prediction
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
Crystal structure forms the foundation for understanding the physical and
chemical properties of materials. Generative models have emerged as a new
paradigm in crystal structure prediction(CSP), however, accurately capturing
key characteristics of crystal structures, such as periodicity and symmetry,
remains a significant challenge. In this paper, we propose a
Transformer-Enhanced Variational Autoencoder for Crystal Structure Prediction
(TransVAE-CSP), who learns the characteristic distribution space of stable
materials, enabling both the reconstruction and generation of crystal
structures. TransVAE-CSP integrates adaptive distance expansion with
irreducible representation to effectively capture the periodicity and symmetry
of crystal structures, and the encoder is a transformer network based on an
equivariant dot product attention mechanism. Experimental results on the
carbon_24, perov_5, and mp_20 datasets demonstrate that TransVAE-CSP
outperforms existing methods in structure reconstruction and generation tasks
under various modeling metrics, offering a powerful tool for crystal structure
design and optimization.
|
[{'version': 'v1', 'created': 'Thu, 13 Feb 2025 15:45:36 GMT'}]
|
2025-02-14
|
Sebastien R\"ocken and Julija Zavadlav
|
Enhancing Machine Learning Potentials through Transfer Learning across
Chemical Elements
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Machine Learning Potentials (MLPs) can enable simulations of ab initio
accuracy at orders of magnitude lower computational cost. However, their
effectiveness hinges on the availability of considerable datasets to ensure
robust generalization across chemical space and thermodynamic conditions. The
generation of such datasets can be labor-intensive, highlighting the need for
innovative methods to train MLPs in data-scarce scenarios. Here, we introduce
transfer learning of potential energy surfaces between chemically similar
elements. Specifically, we leverage the trained MLP for silicon to initialize
and expedite the training of an MLP for germanium. Utilizing classical force
field and ab initio datasets, we demonstrate that transfer learning surpasses
traditional training from scratch in force prediction, leading to more stable
simulations and improved temperature transferability. These advantages become
even more pronounced as the training dataset size decreases. The out-of-target
property analysis shows that transfer learning leads to beneficial but
sometimes adversarial effects. Our findings demonstrate that transfer learning
across chemical elements is a promising technique for developing accurate and
numerically stable MLPs, particularly in a data-scarce regime.
|
[{'version': 'v1', 'created': 'Wed, 19 Feb 2025 08:20:54 GMT'}]
|
2025-02-20
|
Subhash V.S. Ganti, Lukas Woelfel, and Christopher Kuenneth
|
AI-Driven Discovery of High Performance Polymer Electrodes for
Next-Generation Batteries
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.app-ph
|
The use of transition group metals in electric batteries requires extensive
usage of critical elements like lithium, cobalt and nickel, which poses
significant environmental challenges. Replacing these metals with redox-active
organic materials offers a promising alternative, thereby reducing the carbon
footprint of batteries by one order of magnitude. However, this approach faces
critical obstacles, including the limited availability of suitable redox-active
organic materials and issues such as lower electronic conductivity, voltage,
specific capacity, and long-term stability. To overcome the limitations for
lower voltage and specific capacity, a machine learning (ML) driven battery
informatics framework is developed and implemented. This framework utilizes an
extensive battery dataset and advanced ML techniques to accelerate and enhance
the identification, optimization, and design of redox-active organic materials.
In this contribution, a data-fusion ML coupled meta learning model capable of
predicting the battery properties, voltage and specific capacity, for various
organic negative electrodes and charge carriers (positive electrode materials)
combinations is presented. The ML models accelerate experimentation, facilitate
the inverse design of battery materials, and identify suitable candidates from
three extensive material libraries to advance sustainable energy-storage
technologies.
|
[{'version': 'v1', 'created': 'Wed, 19 Feb 2025 17:32:17 GMT'}]
|
2025-02-20
|
Botian Wang, Yawen Ouyang, Yaohui Li, Yiqun Wang, Haorui Cui, Jianbing
Zhang, Xiaonan Wang, Wei-Ying Ma, Hao Zhou
|
MoMa: A Modular Deep Learning Framework for Material Property Prediction
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
Deep learning methods for material property prediction have been widely
explored to advance materials discovery. However, the prevailing pre-train then
fine-tune paradigm often fails to address the inherent diversity and disparity
of material tasks. To overcome these challenges, we introduce MoMa, a Modular
framework for Materials that first trains specialized modules across a wide
range of tasks and then adaptively composes synergistic modules tailored to
each downstream scenario. Evaluation across 17 datasets demonstrates the
superiority of MoMa, with a substantial 14% average improvement over the
strongest baseline. Few-shot and continual learning experiments further
highlight MoMa's potential for real-world applications. Pioneering a new
paradigm of modular material learning, MoMa will be open-sourced to foster
broader community collaboration.
|
[{'version': 'v1', 'created': 'Fri, 21 Feb 2025 14:12:44 GMT'}, {'version': 'v2', 'created': 'Mon, 17 Mar 2025 12:33:30 GMT'}]
|
2025-03-18
|
Mariia Radova, Wojciech G. Stark, Connor S. Allen, Reinhard J. Maurer,
and Albert P. Bart\'ok
|
Fine-tuning foundation models of materials interatomic potentials with
frozen transfer learning
| null | null | null |
cond-mat.mtrl-sci
|
Machine-learned interatomic potentials are revolutionising atomistic
materials simulations by providing accurate and scalable predictions within the
scope covered by the training data. However, generation of an accurate and
robust training data set remains a challenge, often requiring thousands of
first-principles calculations to achieve high accuracy. Foundation models have
started to emerge with the ambition to create universally applicable potentials
across a wide range of materials. While foundation models can be robust and
transferable, they do not yet achieve the accuracy required to predict reaction
barriers, phase transitions, and material stability. This work demonstrates
that foundation model potentials can reach chemical accuracy when fine-tuned
using transfer learning with partially frozen weights and biases. For two
challenging datasets on reactive chemistry at surfaces and stability and
elastic properties of tertiary alloys, we show that frozen transfer learning
with 10-20% of the data (hundreds of datapoints) achieves similar accuracies to
models trained from scratch (on thousands of datapoints). Moreover, we show
that an equally accurate, but significantly more efficient surrogate model can
be built using the transfer learned potential as the ground truth. In
combination, we present a simulation workflow for machine learning potentials
that improves data efficiency and computational efficiency.
|
[{'version': 'v1', 'created': 'Fri, 21 Feb 2025 16:45:05 GMT'}]
|
2025-02-24
|
Israrul H Hashmi, Rahul Karmakar, Marripelli Maniteja, Kumar Ayush,
Tarak K. Patra
|
An Explainable AI Model for Binary LJ Fluids
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.chem-ph
|
Lennard-Jones (LJ) fluids serve as an important theoretical framework for
understanding molecular interactions. Binary LJ fluids, where two distinct
species of particles interact based on the LJ potential, exhibit rich phase
behavior and provide valuable insights of complex fluid mixtures. Here we
report the construction and utility of an artificial intelligence (AI) model
for binary LJ fluids, focusing on their effectiveness in predicting radial
distribution functions (RDFs) across a range of conditions. The RDFs of a
binary mixture with varying compositions and temperatures are collected from
molecular dynamics (MD) simulations to establish and validate the AI model. In
this AI pipeline, RDFs are discretized in order to reduce the output dimension
of the model. This, in turn, improves the efficacy, and reduce the complexity
of an AI RDF model. The model is shown to predict RDFs for many unknown
mixtures very accurately, especially outside the training temperature range.
Our analysis suggests that the particle size ratio has a higher order impact on
the microstructure of a binary mixture. We also highlight the areas where the
fidelity of the AI model is low when encountering new regimes with different
underlying physics.
|
[{'version': 'v1', 'created': 'Mon, 24 Feb 2025 17:35:01 GMT'}]
|
2025-02-25
|
Yun Hao, Che Fan, Beilin Ye, Wenhao Lu, Zhen Lu, Peilin Zhao, Zhifeng
Gao, Qingyao Wu, Yanhui Liu, and Tongqi Wen
|
Inverse Materials Design by Large Language Model-Assisted Generative
Framework
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
Deep generative models hold great promise for inverse materials design, yet
their efficiency and accuracy remain constrained by data scarcity and model
architecture. Here, we introduce AlloyGAN, a closed-loop framework that
integrates Large Language Model (LLM)-assisted text mining with Conditional
Generative Adversarial Networks (CGANs) to enhance data diversity and improve
inverse design. Taking alloy discovery as a case study, AlloyGAN systematically
refines material candidates through iterative screening and experimental
validation. For metallic glasses, the framework predicts thermodynamic
properties with discrepancies of less than 8% from experiments, demonstrating
its robustness. By bridging generative AI with domain knowledge and validation
workflows, AlloyGAN offers a scalable approach to accelerate the discovery of
materials with tailored properties, paving the way for broader applications in
materials science.
|
[{'version': 'v1', 'created': 'Tue, 25 Feb 2025 11:52:59 GMT'}]
|
2025-02-26
|
Grace Guinan, Addison Salvador, Michelle A. Smeaton, Andrew Glaws,
Hilary Egan, Brian C. Wyatt, Babak Anasori, Kevin R. Fiedler, Matthew J.
Olszta, and Steven R. Spurgeon
|
Mind the Gap: Bridging the Divide Between AI Aspirations and the Reality
of Autonomous Characterization
| null | null | null |
cond-mat.mtrl-sci cs.AI
|
What does materials science look like in the "Age of Artificial
Intelligence?" Each materials domain-synthesis, characterization, and
modeling-has a different answer to this question, motivated by unique
challenges and constraints. This work focuses on the tremendous potential of
autonomous characterization within electron microscopy. We present our recent
advancements in developing domain-aware, multimodal models for microscopy
analysis capable of describing complex atomic systems. We then address the
critical gap between the theoretical promise of autonomous microscopy and its
current practical limitations, showcasing recent successes while highlighting
the necessary developments to achieve robust, real-world autonomy.
|
[{'version': 'v1', 'created': 'Tue, 25 Feb 2025 19:43:47 GMT'}]
|
2025-02-27
|
Boyang Zhang, Zhe Li, Zhongju Wang, Yang Yu, Hark Hoe Tan, Chennupati
Jagadish, Daoyi Dong, Lan Fu
|
Physics-Aware Inverse Design for Nanowire Single-Photon Avalanche
Detectors via Deep Learning
| null | null | null |
physics.app-ph cond-mat.mtrl-sci
|
Single-photon avalanche detectors (SPADs) have enabled various applications
in emerging photonic quantum information technologies in recent years. However,
despite many efforts to improve SPAD's performance, the design of SPADs
remained largely an iterative and time-consuming process where a designer makes
educated guesses of a device structure based on empirical reasoning and solves
the semiconductor drift-diffusion model for it. In contrast, the inverse
problem, i.e., directly inferring a structure needed to achieve desired
performance, which is of ultimate interest to designers, remains an unsolved
problem. We propose a novel physics-aware inverse design workflow for SPADs
using a deep learning model and demonstrate it with an example of finding the
key parameters of semiconductor nanowires constituting the unit cell of an
SPAD, given target photon detection efficiency. Our inverse design workflow is
not restricted to the case demonstrated and can be applied to design
conventional planar structure-based SPADs, photodetectors, and solar cells.
|
[{'version': 'v1', 'created': 'Wed, 26 Feb 2025 05:58:45 GMT'}]
|
2025-02-27
|
Vijay Kumar Sutrakar, Nikhil Morge, Anjana PK and Abhilash PV
|
Design of Resistive Frequency Selective Surface based Radar Absorbing
Structure-A Deep Learning Approach
| null | null | null |
cs.LG cond-mat.mtrl-sci physics.app-ph
|
In this paper, deep learning-based approach for the design of radar absorbing
structure using resistive frequency selective surface is proposed. In the
present design, reflection coefficient is used as input of deep learning model
and the Jerusalem cross based unit cell dimensions is predicted as outcome.
Sequential neural network based deep learning model with adaptive moment
estimation optimizer is used for designing multi frequency band absorbers. The
model is used for designing radar absorber from L to Ka band depending on unit
cell parameters and thickness. The outcome of deep learning model is further
compared with full-wave simulation software and an excellent match is obtained.
The proposed model can be used for the low-cost design of various radar
absorbing structures using a single unit cell and thickness across the band of
frequencies.
|
[{'version': 'v1', 'created': 'Wed, 26 Feb 2025 14:09:13 GMT'}]
|
2025-02-27
|
Lei Zhang and Markus Stricker
|
Electrocatalyst discovery through text mining and multi-objective
optimization
| null | null | null |
cond-mat.mtrl-sci
|
The discovery and optimization of high-performance materials is the basis for
advancing energy conversion technologies. To understand composition-property
relationships, all available data sources should be leveraged: experimental
results, predictions from simulations, and latent knowledge from scientific
texts. Among these three, text-based data sources are still not used to their
full potential. We present an approach combining text mining, Word2Vec
representations of materials and properties, and Pareto front analysis for the
prediction of high-performance candidate materials for electrocatalysis in
regions where other data sources are scarce or non-existent. Candidate
compositions are evaluated on the basis of their similarity to the terms
`conductivity' and `dielectric', which enables reaction-specific candidate
composition predictions for oxygen reduction (ORR), hydrogen evolution (HER),
and oxygen evolution (OER) reactions. This, combined with Pareto optimization,
allows us to significantly reduce the pool of candidate compositions to
high-performing compositions. Our predictions, which are purely based on text
data, match the measured electrochemical activity very well.
|
[{'version': 'v1', 'created': 'Fri, 28 Feb 2025 09:02:03 GMT'}]
|
2025-03-03
|
Keqiang Yan, Xiner Li, Hongyi Ling, Kenna Ashen, Carl Edwards,
Raymundo Arr\'oyave, Marinka Zitnik, Heng Ji, Xiaofeng Qian, Xiaoning Qian,
Shuiwang Ji
|
Invariant Tokenization of Crystalline Materials for Language Model
Enabled Generation
| null | null | null |
cs.LG cond-mat.mtrl-sci
|
We consider the problem of crystal materials generation using language models
(LMs). A key step is to convert 3D crystal structures into 1D sequences to be
processed by LMs. Prior studies used the crystallographic information framework
(CIF) file stream, which fails to ensure SE(3) and periodic invariance and may
not lead to unique sequence representations for a given crystal structure.
Here, we propose a novel method, known as Mat2Seq, to tackle this challenge.
Mat2Seq converts 3D crystal structures into 1D sequences and ensures that
different mathematical descriptions of the same crystal are represented in a
single unique sequence, thereby provably achieving SE(3) and periodic
invariance. Experimental results show that, with language models, Mat2Seq
achieves promising performance in crystal structure generation as compared with
prior methods.
|
[{'version': 'v1', 'created': 'Fri, 28 Feb 2025 20:02:53 GMT'}]
|
2025-03-04
|
Junqi He, Yujie Zhang, Jialu Wang, Tao Wang, Pan Zhang, Chengjie Cai,
Jinxing Yang, Xiao Lin, and Xiaohui Yang
|
Rapid morphology characterization of two-dimensional TMDs and lateral
heterostructures based on deep learning
| null | null | null |
cs.LG cond-mat.mtrl-sci cs.CV physics.optics
|
Two-dimensional (2D) materials and heterostructures exhibit unique physical
properties, necessitating efficient and accurate characterization methods.
Leveraging advancements in artificial intelligence, we introduce a deep
learning-based method for efficiently characterizing heterostructures and 2D
materials, specifically MoS2-MoSe2 lateral heterostructures and MoS2 flakes
with varying shapes and thicknesses. By utilizing YOLO models, we achieve an
accuracy rate of over 94.67% in identifying these materials. Additionally, we
explore the application of transfer learning across different materials, which
further enhances model performance. This model exhibits robust generalization
and anti-interference ability, ensuring reliable results in diverse scenarios.
To facilitate practical use, we have developed an application that enables
real-time analysis directly from optical microscope images, making the process
significantly faster and more cost-effective than traditional methods. This
deep learning-driven approach represents a promising tool for the rapid and
accurate characterization of 2D materials, opening new avenues for research and
development in material science.
|
[{'version': 'v1', 'created': 'Sat, 1 Mar 2025 12:51:32 GMT'}]
|
2025-03-04
|
Onur Boyar, Indra Priyadarsini, Seiji Takeda, Lisa Hamada
|
LLM-Fusion: A Novel Multimodal Fusion Model for Accelerated Material
Discovery
| null | null | null |
cond-mat.mtrl-sci cs.AI cs.LG
|
Discovering materials with desirable properties in an efficient way remains a
significant problem in materials science. Many studies have tackled this
problem by using different sets of information available about the materials.
Among them, multimodal approaches have been found to be promising because of
their ability to combine different sources of information. However, fusion
algorithms to date remain simple, lacking a mechanism to provide a rich
representation of multiple modalities. This paper presents LLM-Fusion, a novel
multimodal fusion model that leverages large language models (LLMs) to
integrate diverse representations, such as SMILES, SELFIES, text descriptions,
and molecular fingerprints, for accurate property prediction. Our approach
introduces a flexible LLM-based architecture that supports multimodal input
processing and enables material property prediction with higher accuracy than
traditional methods. We validate our model on two datasets across five
prediction tasks and demonstrate its effectiveness compared to unimodal and
naive concatenation baselines.
|
[{'version': 'v1', 'created': 'Sun, 2 Mar 2025 21:13:04 GMT'}]
|
2025-03-04
|
Ryosuke Akashi, Mihira Sogal, and Kieron Burke
|
Can machines learn density functionals? Past, present, and future of ML
in DFT
| null | null | null |
physics.comp-ph cond-mat.mtrl-sci physics.chem-ph
|
Density functional theory has become the world's favorite electronic
structure method, and is routinely applied to both materials and molecules.
Here, we review recent attempts to use modern machine-learning to improve
density functional approximations. Many different researchers have tried many
different approaches, but some common themes and lessons have emerged. We
discuss these trends and where they might bring us in the future.
|
[{'version': 'v1', 'created': 'Mon, 3 Mar 2025 16:21:18 GMT'}]
|
2025-03-04
|
Mingjie Wen, Jiahe Han, Wenjuan Li, Xiaoya Chang, Qingzhao Chu,
Dongping Chen
|
A General Neural Network Potential for Energetic Materials with C, H, N,
and O elements
| null | null | null |
cond-mat.mtrl-sci cs.LG
|
The discovery and optimization of high-energy materials (HEMs) are
constrained by the prohibitive computational expense and prolonged development
cycles inherent in conventional approaches. In this work, we develop a general
neural network potential (NNP) that efficiently predicts the structural,
mechanical, and decomposition properties of HEMs composed of C, H, N, and O.
Our framework leverages pre-trained NNP models, fine-tuned using transfer
learning on energy and force data derived from density functional theory (DFT)
calculations. This strategy enables rapid adaptation across 20 different HEM
systems while maintaining DFT-level accuracy, significantly reducing
computational costs. A key aspect of this work is the ability of NNP model to
capture the chemical activity space of HEMs, accurately describe the key atomic
interactions and reaction mechanisms during thermal decomposition. The general
NNP model has been applied in molecular dynamics (MD) simulations and validated
with experimental data for various HEM structures. Results show that the NNP
model accurately predicts the structural, mechanical, and decomposition
properties of HEMs by effectively describing their chemical activity space.
Compared to traditional force fields, it offers superior DFT-level accuracy and
generalization across both microscopic and macroscopic properties, reducing the
computational and experimental costs. This work provides an efficient strategy
for the design and development of HEMs and proposes a promising framework for
integrating DFT, machine learning, and experimental methods in materials
research. (To facilitate further research and practical applications, we
open-source our NNP model on GitHub:
https://github.com/MingjieWen/General-NNP-model-for-C-H-N-O-Energetic-Materials.)
|
[{'version': 'v1', 'created': 'Mon, 3 Mar 2025 03:24:59 GMT'}]
|
2025-03-05
|
Nikita Kazeev, Wei Nong, Ignat Romanov, Ruiming Zhu, Andrey
Ustyuzhanin, Shuya Yamazaki, Kedar Hippalgaonkar
|
Wyckoff Transformer: Generation of Symmetric Crystals
| null | null | null |
cond-mat.mtrl-sci cs.LG physics.comp-ph
|
Symmetry rules that atoms obey when they bond together to form an ordered
crystal play a fundamental role in determining their physical, chemical, and
electronic properties such as electrical and thermal conductivity, optical and
polarization behavior, and mechanical strength. Almost all known crystalline
materials have internal symmetry. Consistently generating stable crystal
structures is still an open challenge, specifically because such symmetry rules
are not accounted for. To address this issue, we propose WyFormer, a generative
model for materials conditioned on space group symmetry. We use Wyckoff
positions as the basis for an elegant, compressed, and discrete structure
representation. To model the distribution, we develop a permutation-invariant
autoregressive model based on the Transformer and an absence of positional
encoding. WyFormer has a unique and powerful synergy of attributes, proven by
extensive experimentation: best-in-class symmetry-conditioned generation,
physics-motivated inductive bias, competitive stability of the generated
structures, competitive material property prediction quality, and unparalleled
inference speed.
|
[{'version': 'v1', 'created': 'Tue, 4 Mar 2025 08:50:10 GMT'}, {'version': 'v2', 'created': 'Fri, 7 Mar 2025 07:46:34 GMT'}]
|
2025-03-10
|
Tsz Wai Ko, Bowen Deng, Marcel Nassar, Luis Barroso-Luque, Runze Liu,
Ji Qi, Elliott Liu, Gerbrand Ceder, Santiago Miret and Shyue Ping Ong
|
Materials Graph Library (MatGL), an open-source graph deep learning
library for materials science and chemistry
| null | null | null |
cond-mat.mtrl-sci physics.chem-ph
|
Graph deep learning models, which incorporate a natural inductive bias for a
collection of atoms, are of immense interest in materials science and
chemistry. Here, we introduce the Materials Graph Library (MatGL), an
open-source graph deep learning library for materials science and chemistry.
Built on top of the popular Deep Graph Library (DGL) and Python Materials
Genomics (Pymatgen) packages, our intention is for MatGL to be an extensible
``batteries-included'' library for the development of advanced graph deep
learning models for materials property predictions and interatomic potentials.
At present, MatGL has efficient implementations for both invariant and
equivariant graph deep learning models, including the Materials 3-body Graph
Network (M3GNet), MatErials Graph Network (MEGNet), Crystal Hamiltonian Graph
Network (CHGNet), TensorNet and SO3Net architectures. MatGL also includes a
variety of pre-trained universal interatomic potentials (aka ``foundational
materials models (FMM)'') and property prediction models are also included for
out-of-box usage, benchmarking and fine-tuning. Finally, MatGL includes support
for Pytorch Lightning for rapid training of models.
|
[{'version': 'v1', 'created': 'Wed, 5 Mar 2025 19:03:21 GMT'}]
|
2025-03-07
|
Zhilong Song, Minggang Ju, Chunjin Ren, Qiang Li, Chongyi Li, Qionghua
Zhou, and Jinlan Wang
|
LLM-Feynman: Leveraging Large Language Models for Universal Scientific
Formula and Theory Discovery
| null | null | null |
cond-mat.mtrl-sci
|
Distilling the underlying principles from data has long propelled scientific
breakthroughs. However, conventional data-driven machine learning -- lacking
deep, contextual domain knowledge -- tend to yield opaque or over-complex
models that are challenging to interpret and generalize. Here, we present
LLM-Feynman, a framework that leverages the embedded expertise of large
language models (LLMs) with systematic optimization to distill concise,
interpretable formula from data and domain knowledge. Our framework seamlessly
integrates automated feature engineering, LLM-based symbolic regression
augmented by self-evaluation and iterative refinement, and formula
interpretation via Monte Carlo tree search. Ablation studies show that
incorporating domain knowledge and self-evaluation yields more accurate formula
at equivalent formula complexity than conventional symbolic regression.
Validation on datasets from Feynman physics lectures confirms that LLM-Feynman
can rediscover over 90% real physical formulas. Moreover, when applied to four
key materials science tasks -- from classifying the synthesizability of 2D and
perovskite structures to predicting ionic conductivity in lithium solid-state
electrolytes and GW bandgaps in 2D materials -- LLM-Feynman consistently yields
interpretable formula with accuracy exceeding 90% and R2 values above 0.8. By
transcending mere data fitting through the integration of deep domain
knowledge, LLM-Feynman establishes a new paradigm for the automated discovery
of generalizable scientific formula and theory across disciplines.
|
[{'version': 'v1', 'created': 'Sun, 9 Mar 2025 08:34:51 GMT'}]
|
2025-03-11
|
Xiang Li, Yixiao Chen, Bohao Li, Haoxiang Chen, Fengcheng Wu, Ji Chen,
Weiluo Ren
|
Deep Learning Sheds Light on Integer and Fractional Topological
Insulators
| null | null | null |
cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci physics.comp-ph
|
Electronic topological phases of matter, characterized by robust boundary
states derived from topologically nontrivial bulk states, are pivotal for
next-generation electronic devices. However, understanding their complex
quantum phases, especially at larger scales and fractional fillings with strong
electron correlations, has long posed a formidable computational challenge.
Here, we employ a deep learning framework to express the many-body wavefunction
of topological states in twisted ${\rm MoTe_2}$ systems, where diverse
topological states are observed. Leveraging neural networks, we demonstrate the
ability to identify and characterize topological phases, including the integer
and fractional Chern insulators as well as the $Z_2$ topological insulators.
Our deep learning approach significantly outperforms traditional methods, not
only in computational efficiency but also in accuracy, enabling us to study
larger systems and differentiate between competing phases such as fractional
Chern insulators and charge density waves. Our predictions align closely with
experimental observations, highlighting the potential of deep learning
techniques to explore the rich landscape of topological and strongly correlated
phenomena.
|
[{'version': 'v1', 'created': 'Fri, 14 Mar 2025 18:00:01 GMT'}]
|
2025-03-18
|
Sk Mujaffar Hossain, Namitha Anna Koshi, Seung-Cheol Lee, G.P Das and
Satadeep Bhattacharjee
|
Deep Neural Network-Based Voltage Prediction for Alkali-Metal-Ion
Battery Materials
| null | null | null |
cond-mat.mtrl-sci physics.chem-ph
|
Accurate voltage prediction of battery materials plays a pivotal role in
advancing energy storage technologies and in the rational design of
high-performance cathode materials. In this work, we present a deep neural
network (DNN) model, built using PyTorch, to estimate the average voltage of
cathode materials across Li-ion, Na-ion, and other alkali-metal-ion batteries.
The model is trained on an extensive dataset from the Materials Project,
incorporating a wide range of descriptors-structural, physical, chemical,
electronic, thermodynamic, and battery-specific-ensuring a comprehensive
representation of material properties. Our model exhibits strong predictive
performance, as corroborated by first-principles density functional theory
(DFT) calculations. The close alignment between the DNN predictions and DFT
outcomes highlights the robustness and accuracy of our machine learning
framework in effectively screening and identifying viable battery materials.
Utilizing this validated model, we successfully propose novel Na-ion battery
compositions, with their predicted behavior confirmed through rigorous
computational assessment. By seamlessly integrating data-driven prediction with
first-principles validation, this study presents an effective framework that
significantly accelerates the discovery and optimization of advanced battery
materials, contributing to the development of more reliable and efficient
energy storage technologies.
|
[{'version': 'v1', 'created': 'Mon, 17 Mar 2025 11:15:31 GMT'}, {'version': 'v2', 'created': 'Thu, 3 Apr 2025 05:10:32 GMT'}]
|
2025-04-04
|
Alex Foutch, Kazuma Kobayashi, Ayodeji Alajo, Dinesh Kumar, Syed
Bahauddin Alam
|
AI-driven Uncertainty Quantification & Multi-Physics Approach to
Evaluate Cladding Materials in a Microreactor
| null | null | null |
physics.ins-det cond-mat.mtrl-sci stat.AP
|
The pursuit of enhanced nuclear safety has spurred the development of
accident-tolerant cladding (ATC) materials for light water reactors (LWRs).
This study investigates the potential of repurposing these ATCs in advanced
reactor designs, aiming to expedite material development and reduce costs. The
research employs a multi-physics approach, encompassing neutronics, heat
transfer, thermodynamics, and structural mechanics, to evaluate four candidate
materials (Haynes 230, Zircaloy-4, FeCrAl, and SiC-SiC) within the context of a
high-temperature, sodium-cooled microreactor, exemplified by the Kilopower
design. While neutronic simulations revealed negligible power profile
variations among the materials, finite element analyses highlighted the
superior thermal stability of SiC-SiC and the favorable stress resistance of
Haynes 230. The high-temperature environment significantly impacted material
performance, particularly for Zircaloy-4 and FeCrAl, while SiC-SiC's inherent
properties limited its ability to withstand stress loads. Additionally,
AI-driven uncertainty quantification and sensitivity analysis were conducted to
assess the influence of material property variations on maximum hoop stress.
The findings underscore the need for further research into high-temperature
material properties to facilitate broader applicability of existing materials
to advanced reactors. Haynes 230 is identified as the most promising candidate
based on the evaluated criteria.
|
[{'version': 'v1', 'created': 'Tue, 18 Mar 2025 19:36:36 GMT'}]
|
2025-03-20
|
Shuya Yamazaki, Wei Nong, Ruiming Zhu, Kostya S. Novoselov, Andrey
Ustyuzhanin, Kedar Hippalgaonkar
|
Multi-property directed generative design of inorganic materials through
Wyckoff-augmented transfer learning
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
Accelerated materials discovery is an urgent demand to drive advancements in
fields such as energy conversion, storage, and catalysis. Property-directed
generative design has emerged as a transformative approach for rapidly
discovering new functional inorganic materials with multiple desired properties
within vast and complex search spaces. However, this approach faces two primary
challenges: data scarcity for functional properties and the multi-objective
optimization required to balance competing tasks. Here, we present a
multi-property-directed generative framework designed to overcome these
limitations and enhance site symmetry-compliant crystal generation beyond P1
(translational) symmetry. By incorporating Wyckoff-position-based data
augmentation and transfer learning, our framework effectively handles sparse
and small functional datasets, enabling the generation of new stable materials
simultaneously conditioned on targeted space group, band gap, and formation
energy. Using this approach, we identified previously unknown thermodynamically
and lattice-dynamically stable semiconductors in tetragonal, trigonal, and
cubic systems, with bandgaps ranging from 0.13 to 2.20 eV, as validated by
density functional theory (DFT) calculations. Additionally, we assessed their
thermoelectric descriptors using DFT, indicating their potential suitability
for thermoelectric applications. We believe our integrated framework represents
a significant step forward in generative design of inorganic materials.
|
[{'version': 'v1', 'created': 'Fri, 21 Mar 2025 01:41:25 GMT'}]
|
2025-03-24
|
Owais Ahmad, Albert Linda, Saumya Ranjan Jha, Somanth Bhowmick
|
Deep Learning Assisted Denoising of Experimental Micrographs
|
Materials Characterization (2025)
|
10.1016/j.matchar.2025.114963
| null |
cond-mat.mtrl-sci
|
Microstructure imaging is crucial in materials science, but experimental
images often introduce noise that obscures critical structural details. This
study presents a novel deep learning approach for robust microstructure image
denoising, combining phase-field simulations, Fourier transform techniques, and
an attention-based neural network. The innovative framework addresses dataset
limitations by synthetically generating training data by combining
computational phase-field microstructures with experimental optical
micrographs. The neural network architecture features an attention mechanism
that dynamically focuses on important microstructural features while
systematically eliminating noise types like scratches and surface
imperfections. Testing on a FeMnNi alloy system demonstrated the model's
exceptional performance across multiple magnifications. By successfully
removing diverse noise patterns while maintaining grain boundary integrity, the
research provides a generalizable deep-learning framework for microstructure
image enhancement with broad applicability in materials science.
|
[{'version': 'v1', 'created': 'Sun, 23 Mar 2025 05:10:54 GMT'}]
|
2025-04-02
|
Chu-Liang Fu, Mouyang Cheng, Nguyen Tuan Hung, Eunbi Rha, Zhantao
Chen, Ryotaro Okabe, Denisse C\'ordova Carrizales, Manasi Mandal, Yongqiang
Cheng, Mingda Li
|
AI-Driven Defect Engineering for Advanced Thermoelectric Materials
| null | null | null |
cond-mat.mtrl-sci
|
Thermoelectric materials offer a promising pathway to directly convert waste
heat to electricity. However, achieving high performance remains challenging
due to intrinsic trade-offs between electrical conductivity, the Seebeck
coefficient, and thermal conductivity, which are further complicated by the
presence of defects. This review explores how artificial intelligence (AI) and
machine learning (ML) are transforming thermoelectric materials design.
Advanced ML approaches including deep neural networks, graph-based models, and
transformer architectures, integrated with high-throughput simulations and
growing databases, effectively capture structure-property relationships in a
complex multiscale defect space and overcome the curse of dimensionality. This
review discusses AI-enhanced defect engineering strategies such as composition
optimization, entropy and dislocation engineering, and grain boundary design,
along with emerging inverse design techniques for generating materials with
targeted properties. Finally, it outlines future opportunities in novel physics
mechanisms and sustainability, highlighting the critical role of AI in
accelerating the discovery of thermoelectric materials.
|
[{'version': 'v1', 'created': 'Mon, 24 Mar 2025 21:07:38 GMT'}]
|
2025-03-26
|
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang,
Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai,
Hongyue Sun, Kenan Song, Xianqiao Wang
|
A Multi-Agent Framework Integrating Large Language Models and Generative
AI for Accelerated Metamaterial Design
| null | null | null |
cond-mat.mtrl-sci cs.RO
|
Metamaterials, renowned for their exceptional mechanical, electromagnetic,
and thermal properties, hold transformative potential across diverse
applications, yet their design remains constrained by labor-intensive
trial-and-error methods and limited data interoperability. Here, we introduce
CrossMatAgent -- a novel multi-agent framework that synergistically integrates
large language models with state-of-the-art generative AI to revolutionize
metamaterial design. By orchestrating a hierarchical team of agents -- each
specializing in tasks such as pattern analysis, architectural synthesis, prompt
engineering, and supervisory feedback -- our system leverages the multimodal
reasoning of GPT-4o alongside the generative precision of DALL-E 3 and a
fine-tuned Stable Diffusion XL model. This integrated approach automates data
augmentation, enhances design fidelity, and produces simulation- and 3D
printing-ready metamaterial patterns. Comprehensive evaluations, including
CLIP-based alignment, SHAP interpretability analyses, and mechanical
simulations under varied load conditions, demonstrate the framework's ability
to generate diverse, reproducible, and application-ready designs. CrossMatAgent
thus establishes a scalable, AI-driven paradigm that bridges the gap between
conceptual innovation and practical realization, paving the way for accelerated
metamaterial development.
|
[{'version': 'v1', 'created': 'Tue, 25 Mar 2025 17:53:25 GMT'}, {'version': 'v2', 'created': 'Sun, 6 Apr 2025 18:58:52 GMT'}]
|
2025-04-08
|
Jason B. Gibson, Ajinkya C. Hire, Philip M. Dee, Benjamin Geisler,
Jung Soo Kim, Zhongwei Li, James J. Hamlin, Gregory R. Stewart, P. J.
Hirschfeld, and Richard G. Hennig
|
Developing a Complete AI-Accelerated Workflow for Superconductor
Discovery
| null | null | null |
cond-mat.supr-con cond-mat.mtrl-sci
|
The evolution of materials discovery has continually transformed, progressing
from empirical experimentation to virtual high-throughput screening, which
leverages computational techniques to fully characterize a material before
synthesis. Despite the successes of these approaches, significant bottlenecks
remain due to the high computational cost of density functional theory (DFT)
calculations required to determine the thermodynamic and dynamic stability of a
material and its functional properties. In particular, discovering new
superconductors is massively impeded by the cost of computing the
electron-phonon spectral functions, which limits the feasible materials space.
Recent advances in machine learning offer an opportunity to accelerate the
superconductor discovery workflow by developing machine-learning-based
surrogates for DFT. Here we present a Bootstrapped Ensemble of Equivariant
Graph Neural Networks (BEE-NET), a ML model that predicts the Eliashberg
spectral function achieves a test mean absolute error of 0.87 K for the
superconducting critical temperature ($T_c$), relative to predictions of the
Allen-Dynes equation using DFT-derived spectral functions. BEE-NET
simultaneously identifies candidate structures with $T_c > 5$ K at a precision
of 86% and a true negative rate of 99.4%. Combined with elemental substitution
and ML interatomic potentials, this models puts us in position to develop a
complete AI-accelerated workflow to identify novel superconductors. The
workflow achieved 87% precision, narrowing 1.3 million candidates to 741 stable
compounds with DFT-confirmed $T_c > 5$ K. We report the prediction and
successful experimental synthesis, characterization, and verification of two
novel superconductors. This work exemplifies the potential of integrating
machine learning, computational methods, and experimental techniques to
revolutionize the field of materials discovery.
|
[{'version': 'v1', 'created': 'Tue, 25 Mar 2025 18:44:53 GMT'}]
|
2025-03-27
|
Yuta Yoshimoto, Naoki Matsumura, Yuto Iwasaki, Hiroshi Nakao, Yasufumi
Sakai
|
Large-Scale, Long-Time Atomistic Simulations of Proton Transport in
Polymer Electrolyte Membranes Using a Neural Network Interatomic Potential
| null | null | null |
cond-mat.mtrl-sci physics.comp-ph
|
In recent years, machine learning interatomic potentials (MLIPs) have
attracted significant attention as a method that enables large-scale, long-time
atomistic simulations while maintaining accuracy comparable to electronic
structure calculations based on density functional theory (DFT) and ab initio
wavefunction theories. However, a challenge with MLIP-based molecular dynamics
(MD) simulations is their lower stability compared to those using conventional
classical potentials. Analyzing highly heterogeneous systems or amorphous
materials often requires large-scale and long-time simulations, necessitating
the development of robust MLIPs that allow for stable MD simulations. In this
study, using our neural network potential (NNP) generator, we construct an NNP
model that enables large-scale, long-time MD simulations of perfluorinated
ionomer membranes (Nafion) across a wide range of hydration levels. We
successfully build a robust deep potential (DP) model by iteratively expanding
the dataset through active-learning loops. Specifically, by combining the
sampling of off-equilibrium structures via non-equilibrium DPMD simulations
with the structure screening in a 3D structural feature space incorporating
minimum interatomic distances, it is possible to significantly enhance the
robustness of the DP model, which allows for stable MD simulations of large
Nafion systems ranging from approximately 10,000 to 20,000 atoms for an
extended duration of 31 ns. The MD simulations employing the developed DP model
yield self-diffusion coefficients of hydrogen atoms that more closely match
experimental values in a wide range of hydration levels compared to previous ab
initio MD simulations of smaller systems.
|
[{'version': 'v1', 'created': 'Wed, 26 Mar 2025 10:40:30 GMT'}]
|
2025-03-27
|
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