authors stringlengths 11 2.41k | title stringlengths 38 184 | journal-ref stringclasses 115
values | doi stringlengths 17 34 ⌀ | report-no stringclasses 3
values | categories stringlengths 17 83 | abstract stringlengths 124 1.92k | versions stringlengths 62 689 | update_date stringdate 2007-09-13 00:00:00 2025-05-15 00:00:00 |
|---|---|---|---|---|---|---|---|---|
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 sys... | [{'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$^{... | [{'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 e... | [{'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... | [{'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 typi... | [{'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 qua... | [{'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 s... | [{'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 m... | [{'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... | [{'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 interf... | [{'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,... | [{'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-... | [{'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 appr... | [{'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 negativ... | [{'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 conf... | [{'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... | [{'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 reac... | [{'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 usin... | [{'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 th... | [{'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-functiona... | [{'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. ... | [{'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... | [{'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 primaril... | [{'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 uni... | [{'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 ca... | [{'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 Mat... | [{'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... | [{'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 incorp... | [{'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 sign... | [{'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 ... | [{'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 Cal... | 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 ... | [{'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 mod... | [{'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-dur... | [{'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... | [{'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, ... | [{'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 inte... | [{'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,... | [{'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'}, {'ver... | 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 increas... | [{'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, ... | [{'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 u... | [{'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 ... | [{'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 ma... | [{'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. O... | [{'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 co... | [{'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 a... | [{'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 phy... | [{'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 mu... | [{'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
informati... | [{'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 explo... | [{'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, T... | [{'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 ... | [{'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, T... | 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... | [{'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 diffe... | [{'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
incorp... | [{'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-... | [{'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 ... | [{'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 t... | [{'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 ... | [{'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 art... | [{'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-consum... | [{'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... | [{'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... | [{'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 a... | [{'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 positi... | [{'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 disti... | [{'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, inver... | [{'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 ... | [{'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 mode... | [{'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 g... | [{'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 M... | [{'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 pol... | [{'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 signif... | [{'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 d... | [{'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 ... | [{'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... | [{'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 ca... | [{'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 construc... | [{'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 Adversa... | [{'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 el... | [{'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 g... | [{'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 ... | [{'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. Amon... | [{'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... | [{'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, specifi... | [{'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 abilit... | [{'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,... | [{'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 deco... | [{'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 h... | [{'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 popu... | [{'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 f... | [{'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 correlat... | [{'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 ... | [{'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 ... | [{'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 an... | [{'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 attenti... | [{'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 def... | [{'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 m... | [{'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 t... | [{'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 the... | [{'version': 'v1', 'created': 'Wed, 26 Mar 2025 10:40:30 GMT'}] | 2025-03-27 |
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