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 |
|---|---|---|---|---|---|---|---|---|
Siyu Liu, Tongqi Wen, A. S. L. Subrahmanyam Pattamatta, and David J.
Srolovitz | A Prompt-Engineered Large Language Model, Deep Learning Workflow for
Materials Classification | null | 10.1016/j.mattod.2024.08.028 | null | cond-mat.mtrl-sci | Large language models (LLMs) have demonstrated rapid progress across a wide
array of domains. Owing to the very large number of parameters and training
data in LLMs, these models inherently encompass an expansive and comprehensive
materials knowledge database, far exceeding the capabilities of individual
researcher. ... | [{'version': 'v1', 'created': 'Wed, 31 Jan 2024 12:31:52 GMT'}, {'version': 'v2', 'created': 'Wed, 27 Mar 2024 13:22:22 GMT'}] | 2024-11-20 |
Hoang-Giang Nguyen, Thanh-Dung Le | Predictive Models based on Deep Learning Algorithms for Tensile
Deformation of AlCoCuCrFeNi High-entropy alloy | null | null | null | cond-mat.mtrl-sci eess.SP | High-entropy alloys (HEAs) stand out between multi-component alloys due to
their attractive microstructures and mechanical properties. In this
investigation, molecular dynamics (MD) simulation and machine learning were
used to ascertain the deformation mechanism of AlCoCuCrFeNi HEAs under the
influence of temperature... | [{'version': 'v1', 'created': 'Fri, 2 Feb 2024 17:17:30 GMT'}] | 2024-02-05 |
Yutack Park, Jaesun Kim, Seungwoo Hwang, and Seungwu Han | Scalable Parallel Algorithm for Graph Neural Network Interatomic
Potentials in Molecular Dynamics Simulations | Journal of Chemical Theory and Computation 20 (2024) 4857-4868 | 10.1021/acs.jctc.4c00190 | null | cond-mat.mtrl-sci | Message-passing graph neural network interatomic potentials (GNN-IPs),
particularly those with equivariant representations such as NequIP, are
attracting significant attention due to their data efficiency and high
accuracy. However, parallelizing GNN-IPs poses challenges because multiple
message-passing layers compli... | [{'version': 'v1', 'created': 'Tue, 6 Feb 2024 08:10:02 GMT'}] | 2024-06-13 |
Zilong Yuan, Zhiming Xu, He Li, Xinle Cheng, Honggeng Tao, Zechen
Tang, Zhiyuan Zhou, Wenhui Duan, Yong Xu | Equivariant Neural Network Force Fields for Magnetic Materials | null | null | null | cond-mat.mtrl-sci | Neural network force fields have significantly advanced ab initio atomistic
simulations across diverse fields. However, their application in the realm of
magnetic materials is still in its early stage due to challenges posed by the
subtle magnetic energy landscape and the difficulty of obtaining training data.
Here w... | [{'version': 'v1', 'created': 'Wed, 7 Feb 2024 13:59:47 GMT'}] | 2024-02-08 |
Elena Stellino, Beatrice D'Al\`o, Elena Blundo, Paolo Postorino,
Antonio Polimeni | Fine-Tuning of the Excitonic Response in Monolayer WS2 Domes via Coupled
Pressure and Strain Variation | null | null | null | cond-mat.mtrl-sci | We present a spectroscopic investigation into the vibrational and
optoelectronic properties of WS2 domes in the 0-0.65 GPa range. The pressure
evolution of the system morphology, deduced by the combined analysis of Raman
and photoluminescence spectra, revealed a significant variation in the dome's
aspect ratio. The m... | [{'version': 'v1', 'created': 'Wed, 7 Feb 2024 14:09:44 GMT'}] | 2024-02-08 |
Miao Liu, Sheng Meng | Recent Breakthrough in AI-Driven Materials Science: Tech Giants
Introduce Groundbreaking Models | Mater. Futures 3 027501 (2024) | 10.1088/2752-5724/ad2e0c | null | cond-mat.mtrl-sci | A close look of Google's GNoME inorganic materials dataset [Nature 624, 80
(2023)], and 11 things you would like to know.
| [{'version': 'v1', 'created': 'Thu, 8 Feb 2024 16:39:26 GMT'}] | 2024-03-13 |
Francis G. VanGessel, Efrem Perry, Salil Mohan, Oliver M. Barham, Mark
Cavolowsky | NLP for Knowledge Discovery and Information Extraction from Energetics
Corpora | null | null | null | cs.CL cond-mat.mtrl-sci | We present a demonstration of the utility of NLP for aiding research into
energetic materials and associated systems. The NLP method enables machine
understanding of textual data, offering an automated route to knowledge
discovery and information extraction from energetics text. We apply three
established unsupervise... | [{'version': 'v1', 'created': 'Sat, 10 Feb 2024 14:43:08 GMT'}] | 2024-02-13 |
Xiang Huang, C. Y. Zhao, Hong Wang, Shenghong Ju | AI-assisted inverse design of sequence-ordered high intrinsic thermal
conductivity polymers | Materials Today Physics 44, 101438, 2024 | 10.1016/j.mtphys.2024.101438 | null | cond-mat.soft cond-mat.mtrl-sci physics.app-ph physics.comp-ph | Artificial intelligence (AI) promotes the polymer design paradigm from a
traditional trial-and-error approach to a data-driven style. Achieving high
thermal conductivity (TC) for intrinsic polymers is urgent because of their
importance in the thermal management of many industrial applications such as
microelectronic ... | [{'version': 'v1', 'created': 'Sun, 18 Feb 2024 14:34:57 GMT'}] | 2024-05-01 |
Binh Duong Nguyen, Johannes Steiner, Peter Wellmann, Stefan Sandfeld | Combining unsupervised and supervised learning in microscopy enables
defect analysis of a full 4H-SiC wafer | null | null | null | cs.CV cond-mat.mtrl-sci cs.LG | Detecting and analyzing various defect types in semiconductor materials is an
important prerequisite for understanding the underlying mechanisms as well as
tailoring the production processes. Analysis of microscopy images that reveal
defects typically requires image analysis tasks such as segmentation and object
dete... | [{'version': 'v1', 'created': 'Tue, 20 Feb 2024 20:04:23 GMT'}] | 2024-02-22 |
Bashir Kazimi and Karina Ruzaeva and Stefan Sandfeld | Self-Supervised Learning with Generative Adversarial Networks for
Electron Microscopy | null | null | null | cs.CV cond-mat.mtrl-sci cs.AI cs.LG | In this work, we explore the potential of self-supervised learning with
Generative Adversarial Networks (GANs) for electron microscopy datasets. We
show how self-supervised pretraining facilitates efficient fine-tuning for a
spectrum of downstream tasks, including semantic segmentation, denoising, noise
\& background... | [{'version': 'v1', 'created': 'Wed, 28 Feb 2024 12:25:01 GMT'}, {'version': 'v2', 'created': 'Thu, 18 Jul 2024 09:58:03 GMT'}] | 2024-07-19 |
Dongchen Huang, Junde Liu, Tian Qian, and Hongming Weng | Training-set-free two-stage deep learning for spectroscopic data
de-noising | null | null | null | cond-mat.mtrl-sci cs.LG physics.data-an | De-noising is a prominent step in the spectra post-processing procedure.
Previous machine learning-based methods are fast but mostly based on supervised
learning and require a training set that may be typically expensive in real
experimental measurements. Unsupervised learning-based algorithms are slow and
require ma... | [{'version': 'v1', 'created': 'Thu, 29 Feb 2024 03:31:41 GMT'}, {'version': 'v2', 'created': 'Tue, 5 Mar 2024 12:39:23 GMT'}] | 2024-03-06 |
Fankai Xie, Tenglong Lu, Sheng Meng, Miao Liu | GPTFF: A high-accuracy out-of-the-box universal AI force field for
arbitrary inorganic materials | Science Bulletin, 10.1016/j.scib.2024.08.039 | 10.1016/j.scib.2024.08.039 | null | cond-mat.mtrl-sci | This study introduces a novel AI force field, namely graph-based pre-trained
transformer force field (GPTFF), which can simulate arbitrary inorganic systems
with good precision and generalizability. Harnessing a large trove of the data
and the attention mechanism of transformer algorithms, the model can accurately
pr... | [{'version': 'v1', 'created': 'Thu, 29 Feb 2024 16:30:07 GMT'}] | 2024-09-04 |
Vahe Gharakhanyan, Luke J. Wirth, Jose A. Garrido Torres, Ethan
Eisenberg, Ting Wang, Dallas R. Trinkle, Snigdhansu Chatterjee and Alexander
Urban | Discovering Melting Temperature Prediction Models of Inorganic Solids by
Combining Supervised and Unsupervised Learning | null | null | null | cond-mat.mtrl-sci | The melting temperature is important for materials design because of its
relationship with thermal stability, synthesis, and processing conditions.
Current empirical and computational melting point estimation techniques are
limited in scope, computational feasibility, or interpretability. We report the
development of... | [{'version': 'v1', 'created': 'Tue, 5 Mar 2024 16:23:37 GMT'}] | 2024-03-06 |
Yingjie Zhao and Hongbo Zhou and Zian Zhang and Zhenxing Bo and Baoan
Sun and Minqiang Jiang and Zhiping Xu | Discovering High-Strength Alloys via Physics-Transfer Learning | null | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph | Predicting the strength of materials requires considering various length and
time scales, striking a balance between accuracy and efficiency. Peierls stress
measures material strength by evaluating dislocation resistance to plastic
flow, reliant on elastic lattice responses and crystal slip energy landscape.
Computat... | [{'version': 'v1', 'created': 'Tue, 12 Mar 2024 11:05:05 GMT'}, {'version': 'v2', 'created': 'Sun, 26 Jan 2025 07:32:07 GMT'}] | 2025-01-28 |
Matteo Masto, Vincent Favre-Nicolin, Steven Leake, Tobias Sch\"ulli,
Marie-Ingrid Richard, Ewen Bellec | Patching-based Deep Learning model for the Inpainting of Bragg Coherent
Diffraction patterns affected by detectors' gaps | null | null | null | cond-mat.mtrl-sci | We propose a deep learning algorithm for the inpainting of Bragg Coherent
Diffraction Imaging (BCDI) patterns affected by detector gaps. These regions of
missing intensity can compromise the accuracy of reconstruction algorithms,
inducing artifacts in the final result. It is thus desirable to restore the
intensity in... | [{'version': 'v1', 'created': 'Wed, 13 Mar 2024 15:03:13 GMT'}] | 2024-03-14 |
Zhiqiang Zhao, Wanlin Guo, and Zhuhua Zhang | A general-purpose neural network potential for Ti-Al-Nb alloys towards
large-scale molecular dynamics with ab initio accuracy | null | null | null | cond-mat.mtrl-sci physics.comp-ph | High Nb-containing TiAl alloys exhibit exceptional high-temperature strength
and room-temperature ductility, making them widely used in hot-section
components of automotive and aerospace engines. However, the lack of accurate
interatomic interaction potentials for large-scale modeling severely hampers a
comprehensive... | [{'version': 'v1', 'created': 'Thu, 14 Mar 2024 16:11:14 GMT'}] | 2024-03-15 |
Vincent Bl\"umer, Celal Soyarslan, Ton van den Boogaard | Generative reconstruction of 3D volume elements for Ti-6Al-4V
basketweave microstructure by optimization of CNN-based microstructural
descriptors | null | null | null | cond-mat.mtrl-sci | We present a methodology for the generative reconstruction of 3D Volume
Elements (VE) for numerical multiscale analysis of Ti-6Al-4V processed by
Additive Manufacturing (AM). The basketweave morphology, which is typically
dominant in AM-processed Ti-6Al-4V, is analyzed in conventional Electron
Backscatter Diffusion (... | [{'version': 'v1', 'created': 'Thu, 14 Mar 2024 17:50:24 GMT'}] | 2024-03-15 |
Ryo Murakami, Taisuke T. Sasaki, Hideki Yoshikawa, Yoshitaka
Matsushita, Keitaro Sodeyama, Tadakatsu Ohkubo, Hiroshi Shinotsuka, Kenji
Nagata | Rapid and Robust construction of an ML-ready peak feature table from
X-ray diffraction data using Bayesian peak-top fitting | null | null | null | cond-mat.mtrl-sci stat.AP | To advance the development of materials through data-driven scientific
methods, appropriate methods for building machine learning (ML)-ready feature
tables from measured and computed data must be established. In materials
development, X-ray diffraction (XRD) is an effective technique for analysing
crystal structures ... | [{'version': 'v1', 'created': 'Wed, 7 Feb 2024 01:24:39 GMT'}] | 2024-03-18 |
Xiaoshan Luo, Zhenyu Wang, Pengyue Gao, Jian Lv, Yanchao Wang,
Changfeng Chen and Yanming Ma | Deep learning generative model for crystal structure prediction | npj Comput. Mater., 10, 254 (2024) | 10.1038/s41524-024-01443-y | null | cond-mat.mtrl-sci physics.comp-ph | Recent advances in deep learning generative models (GMs) have created high
capabilities in accessing and assessing complex high-dimensional data, allowing
superior efficiency in navigating vast material configuration space in search
of viable structures. Coupling such capabilities with physically significant
data to ... | [{'version': 'v1', 'created': 'Sat, 16 Mar 2024 07:54:19 GMT'}, {'version': 'v2', 'created': 'Sat, 10 Aug 2024 07:02:27 GMT'}] | 2024-11-13 |
An Chen, Zhilong Wang, Karl Luigi Loza Vidaurre, Yanqiang Han, Simin
Ye, Kehao Tao, Shiwei Wang, Jing Gao, and Jinjin Li | Knowledge-Reuse Transfer Learning Methods in Molecular and Material
Science | null | null | null | cond-mat.mtrl-sci cs.LG physics.chem-ph | Molecules and materials are the foundation for the development of modern
advanced industries such as energy storage systems and semiconductor devices.
However, traditional trial-and-error methods or theoretical calculations are
highly resource-intensive, and extremely long R&D (Research and Development)
periods canno... | [{'version': 'v1', 'created': 'Sat, 2 Mar 2024 12:41:25 GMT'}] | 2024-03-21 |
Yubo Qi, Weiyi Gong, Qimin Yan | Bridging deep learning force fields and electronic structures with a
physics-informed approach | null | null | null | cond-mat.mtrl-sci | This work presents a physics-informed neural network approach bridging
deep-learning force field and electronic structure simulations, illustrated
through twisted two-dimensional large-scale material systems. The deep
potential molecular dynamics model is adopted as the backbone, and electronic
structure simulation i... | [{'version': 'v1', 'created': 'Wed, 20 Mar 2024 15:33:46 GMT'}, {'version': 'v2', 'created': 'Mon, 1 Apr 2024 03:28:47 GMT'}] | 2024-04-02 |
Orlando A. Mendible, Jonathan K. Whitmer, and Yamil J. Col\'on | Considerations in the use of ML interaction potentials for free energy
calculations | null | 10.1063/5.0252043 | null | physics.chem-ph cond-mat.mtrl-sci cs.LG | Machine learning force fields (MLFFs) promise to accurately describe the
potential energy surface of molecules at the ab initio level of theory with
improved computational efficiency. Within MLFFs, equivariant graph neural
networks (EQNNs) have shown great promise in accuracy and performance and are
the focus of this... | [{'version': 'v1', 'created': 'Wed, 20 Mar 2024 19:49:21 GMT'}, {'version': 'v2', 'created': 'Tue, 13 May 2025 13:22:54 GMT'}, {'version': 'v3', 'created': 'Wed, 14 May 2025 14:50:01 GMT'}] | 2025-05-15 |
Brian H. Lee, James P. Larentzos, John K. Brennan, and Alejandro
Strachan | Graph neural network coarse-grain force field for the molecular crystal
RDX | null | null | null | cond-mat.mes-hall cond-mat.mtrl-sci | Condense phase molecular systems organize in wide range of distinct molecular
configurations, including amorphous melt and glass as well as crystals often
exhibiting polymorphism, that originate from their intricate intra- and
intermolecular forces. While accurate coarse-grain (CG) models for these
materials are crit... | [{'version': 'v1', 'created': 'Fri, 22 Mar 2024 15:06:06 GMT'}] | 2024-03-25 |
Zhendong Cao, Xiaoshan Luo, Jian Lv and Lei Wang | Space Group Informed Transformer for Crystalline Materials Generation | null | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph | We introduce CrystalFormer, a transformer-based autoregressive model
specifically designed for space group-controlled generation of crystalline
materials. The incorporation of space group symmetry significantly simplifies
the crystal space, which is crucial for data and compute efficient generative
modeling of crysta... | [{'version': 'v1', 'created': 'Sat, 23 Mar 2024 06:01:45 GMT'}, {'version': 'v2', 'created': 'Fri, 16 Aug 2024 02:57:35 GMT'}] | 2024-08-19 |
Xiang Huang and Shenghong Ju | Tutorial: AI-assisted exploration and active design of polymers with
high intrinsic thermal conductivity | Journal of Applied Physics 135, 171101, 2024 | 10.1063/5.0201522 | null | cond-mat.soft cond-mat.mtrl-sci physics.app-ph physics.chem-ph physics.comp-ph | Designing polymers with high intrinsic thermal conductivity (TC) is
critically important for the thermal management of organic electronics and
photonics. However, this is a challenging task owing to the diversity of the
chemical space and the barriers to advanced synthetic
experiments/characterization techniques for ... | [{'version': 'v1', 'created': 'Sat, 23 Mar 2024 16:52:56 GMT'}] | 2024-05-09 |
Yuqi Song, Rongzhi Dong, Lai Wei, Qin Li, Jianjun Hu | AlphaCrystal-II: Distance matrix based crystal structure prediction
using deep learning | null | null | null | cond-mat.mtrl-sci cs.LG | Computational prediction of stable crystal structures has a profound impact
on the large-scale discovery of novel functional materials. However, predicting
the crystal structure solely from a material's composition or formula is a
promising yet challenging task, as traditional ab initio crystal structure
prediction (... | [{'version': 'v1', 'created': 'Sun, 7 Apr 2024 05:17:43 GMT'}] | 2024-04-09 |
Tomoya Shiota, Kenji Ishihara, Wataru Mizukami | Lowering the Exponential Wall: Accelerating High-Entropy Alloy Catalysts
Screening using Local Surface Energy Descriptors from Neural Network
Potentials | null | null | null | quant-ph cond-mat.mtrl-sci | Computational screening is indispensable for the efficient design of
high-entropy alloys (HEAs), which hold considerable potential for catalytic
applications. However, the chemical space of HEAs is exponentially vast with
respect to the number of constituent elements, making even machine
learning-based screening calc... | [{'version': 'v1', 'created': 'Fri, 12 Apr 2024 11:54:06 GMT'}, {'version': 'v2', 'created': 'Sun, 6 Oct 2024 10:28:27 GMT'}, {'version': 'v3', 'created': 'Mon, 27 Jan 2025 08:54:38 GMT'}] | 2025-01-28 |
Zhuo Diao, Keiichi Ueda, Linfeng Hou, Fengxuan Li, Hayato Yamashita,
Masayuki Abe | AI-equipped scanning probe microscopy for autonomous site-specific
atomic-level characterization at room temperature | null | null | null | physics.comp-ph cond-mat.mtrl-sci | We present an advanced scanning probe microscopy system enhanced with
artificial intelligence (AI-SPM) designed for self-driving atomic-scale
measurements. This system expertly identifies and manipulates atomic positions
with high precision, autonomously performing tasks such as spectroscopic data
acquisition and ato... | [{'version': 'v1', 'created': 'Wed, 17 Apr 2024 08:25:42 GMT'}] | 2024-04-18 |
Shinnosuke Hattori and Qiang Zhu | Study of Entropy-Driven Polymorphic Stability for Aspirin Using Accurate
Neural Network Interatomic Potential | null | null | null | cond-mat.mtrl-sci | In this study, we present a systematic computational investigation to analyze
the long debated crystal stability of two well known aspirin polymorphs,
labeled as Form I and Form II. Specifically, we developed a strategy to collect
training configurations covering diverse interatomic interactions between
representativ... | [{'version': 'v1', 'created': 'Wed, 17 Apr 2024 17:34:52 GMT'}, {'version': 'v2', 'created': 'Fri, 19 Apr 2024 16:12:58 GMT'}] | 2024-04-22 |
Adva Baratz, Galit Cohen, Sivan Refaely-Abramson | Unsupervised learning approach to quantum wavepacket dynamics from
coupled temporal-spatial correlations | null | null | null | cond-mat.mtrl-sci | Understanding complex quantum dynamics in realistic materials requires
insight into the underlying correlations dominating the interactions between
the participating particles. Due to the wealth of information involved in these
processes, applying artificial intelligence methods is compelling. Yet,
unsupervised data-... | [{'version': 'v1', 'created': 'Thu, 18 Apr 2024 08:20:30 GMT'}] | 2024-04-19 |
Wonseok Lee, Yeonghun Kang, Taeun Bae, Jihan Kim | Harnessing Large Language Model to collect and analyze Metal-organic
framework property dataset | null | null | null | cond-mat.mtrl-sci | This research was focused on the efficient collection of experimental
Metal-Organic Framework (MOF) data from scientific literature to address the
challenges of accessing hard-to-find data and improving the quality of
information available for machine learning studies in materials science.
Utilizing a chain of advanc... | [{'version': 'v1', 'created': 'Sun, 31 Mar 2024 12:47:24 GMT'}] | 2024-04-23 |
Bowen Hou, Jinyuan Wu, Diana Y. Qiu | Unsupervised Learning of Individual Kohn-Sham States: Interpretable
Representations and Consequences for Downstream Predictions of Many-Body
Effects | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Representation learning for the electronic structure problem is a major
challenge of machine learning in computational condensed matter and materials
physics. Within quantum mechanical first principles approaches, Kohn-Sham
density functional theory (DFT) is the preeminent tool for understanding
electronic structure,... | [{'version': 'v1', 'created': 'Mon, 22 Apr 2024 21:50:50 GMT'}] | 2024-04-24 |
Rajni Chahal, Michael D. Toomey, Logan T. Kearney, Ada Sedova, Joshua
T. Damron, Amit K. Naskar, Santanu Roy | Deep Learning Interatomic Potential Connects Molecular Structural
Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer | null | null | null | cond-mat.mtrl-sci | Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic
stereochemistry resulting from nonselective radical polymerization. As such, an
accurate, fundamental understanding of governing interactions among PAN
molecular units are indispensable to advance the design principles of final
products at re... | [{'version': 'v1', 'created': 'Wed, 24 Apr 2024 20:21:54 GMT'}] | 2024-04-26 |
Jiwei Yu, Zhangwei Wang, Aparna Saksena, Shaolou Wei, Ye Wei, Timoteo
Colnaghi, Andreas Marek, Markus Rampp, Min Song, Baptiste Gault, Yue Li | 3D deep learning for enhanced atom probe tomography analysis of
nanoscale microstructures | null | null | null | cond-mat.mtrl-sci physics.data-an | Quantitative analysis of microstructural features on the nanoscale, including
precipitates, local chemical orderings (LCOs) or structural defects (e.g.
stacking faults) plays a pivotal role in understanding the mechanical and
physical responses of engineering materials. Atom probe tomography (APT), known
for its exce... | [{'version': 'v1', 'created': 'Thu, 25 Apr 2024 11:36:10 GMT'}] | 2024-04-26 |
M. A. Maia, I. B. C. M. Rocha, D. Kova\v{c}evi\'c, F. P. van der Meer | Physically recurrent neural network for rate and path-dependent
heterogeneous materials in a finite strain framework | null | null | null | cond-mat.mtrl-sci cs.LG cs.NA math.NA | In this work, a hybrid physics-based data-driven surrogate model for the
microscale analysis of heterogeneous material is investigated. The proposed
model benefits from the physics-based knowledge contained in the constitutive
models used in the full-order micromodel by embedding them in a neural network.
Following p... | [{'version': 'v1', 'created': 'Fri, 5 Apr 2024 12:40:03 GMT'}] | 2024-04-30 |
Adela Habib and Joshua Finkelstein and Anders M. N. Niklasson | Efficient Mixed-Precision Matrix Factorization of the Inverse Overlap
Matrix in Electronic Structure Calculations with AI-Hardware and GPUs | null | null | null | physics.comp-ph cond-mat.mtrl-sci math-ph math.MP | In recent years, a new kind of accelerated hardware has gained popularity in
the Artificial Intelligence (AI) and Machine Learning (ML) communities which
enables extremely high-performance tensor contractions in reduced precision for
deep neural network calculations. In this article, we exploit Nvidia Tensor
cores, a... | [{'version': 'v1', 'created': 'Mon, 29 Apr 2024 23:53:16 GMT'}] | 2024-05-01 |
Sungwoo Kang | How Graph Neural Network Interatomic Potentials Extrapolate: Role of the
Message-Passing Algorithm | J. Chem. Phys. 161, 244102 (2024) | 10.1063/5.0234287 | null | cond-mat.mtrl-sci | Graph neural network interatomic potentials (GNN-IPs) are gaining significant
attention due to their capability of learning from large datasets.
Specifically, universal interatomic potentials based on GNN, usually trained
with crystalline geometries, often exhibit remarkable extrapolative behavior
towards untrained d... | [{'version': 'v1', 'created': 'Wed, 1 May 2024 02:55:15 GMT'}, {'version': 'v2', 'created': 'Tue, 13 Aug 2024 13:50:55 GMT'}, {'version': 'v3', 'created': 'Thu, 5 Dec 2024 06:49:06 GMT'}] | 2025-01-08 |
Jihua Chen, Yue Yuan, Amir Koushyar Ziabari, Xuan Xu, Honghai Zhang,
Panagiotis Christakopoulos, Peter V. Bonnesen, Ilia N. Ivanov, Panchapakesan
Ganesh, Chen Wang, Karen Patino Jaimes, Guang Yang, Rajeev Kumar, Bobby G.
Sumpter, Rigoberto Advincula | AI for Manufacturing and Healthcare: a chemistry and engineering
perspective | null | null | null | cond-mat.mtrl-sci | Artificial Intelligence (AI) approaches are increasingly being applied to
more and more domains of Science, Engineering, Chemistry, and Industries to not
only improve efficiencies and enhance productivity, but also enable new
capabilities. The new opportunities range from automated molecule design and
screening, prop... | [{'version': 'v1', 'created': 'Thu, 2 May 2024 17:50:05 GMT'}] | 2024-05-03 |
Nakul Rampal, Kaiyu Wang, Matthew Burigana, Lingxiang Hou, Juri
Al-Johani, Anna Sackmann, Hanan S. Murayshid, Walaa Abdullah Al-Sumari, Arwa
M. Al-Abdulkarim, Nahla Eid Al-Hazmi, Majed O. Al-Awad, Christian Borgs,
Jennifer T. Chayes, Omar M. Yaghi | Single and Multi-Hop Question-Answering Datasets for Reticular Chemistry
with GPT-4-Turbo | null | null | null | cs.CL cond-mat.mtrl-sci | The rapid advancement in artificial intelligence and natural language
processing has led to the development of large-scale datasets aimed at
benchmarking the performance of machine learning models. Herein, we introduce
'RetChemQA,' a comprehensive benchmark dataset designed to evaluate the
capabilities of such models... | [{'version': 'v1', 'created': 'Fri, 3 May 2024 14:29:54 GMT'}] | 2024-05-06 |
Luis Mart\'in Encinar, Daniele Lanzoni, Andrea Fantasia, Fabrizio
Rovaris, Roberto Bergamaschini, Francesco Montalenti | Quantitative analysis of the prediction performance of a Convolutional
Neural Network evaluating the surface elastic energy of a strained film | null | 10.1016/j.commatsci.2024.113657 | null | physics.comp-ph cond-mat.mtrl-sci | A Deep Learning approach is devised to estimate the elastic energy density
$\rho$ at the free surface of an undulated stressed film. About 190000
arbitrary surface profiles h(x) are randomly generated by Perlin noise and
paired with the corresponding elastic energy density profiles $\rho(x)$,
computed by a semi-analy... | [{'version': 'v1', 'created': 'Sun, 5 May 2024 20:34:16 GMT'}] | 2025-03-04 |
Kamal Choudhary | AtomGPT: Atomistic Generative Pre-trained Transformer for Forward and
Inverse Materials Design | null | null | null | cond-mat.mtrl-sci | Large language models (LLMs) such as generative pretrained transformers
(GPTs) have shown potential for various commercial applications, but their
applicability for materials design remains underexplored. In this article, we
introduce AtomGPT, a model specifically developed for materials design based on
transformer a... | [{'version': 'v1', 'created': 'Mon, 6 May 2024 17:54:54 GMT'}, {'version': 'v2', 'created': 'Sat, 29 Jun 2024 06:24:30 GMT'}] | 2024-07-02 |
Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li,
Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert
Pinsler, Andrew Fowler, Daniel Z\"ugner, Tian Xie, Jake Smith, Lixin Sun,
Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, Ziheng Lu | MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures
and Pressures | null | null | null | cond-mat.mtrl-sci | Accurate and fast prediction of materials properties is central to the
digital transformation of materials design. However, the vast design space and
diverse operating conditions pose significant challenges for accurately
modeling arbitrary material candidates and forecasting their properties. We
present MatterSim, a... | [{'version': 'v1', 'created': 'Wed, 8 May 2024 11:13:30 GMT'}, {'version': 'v2', 'created': 'Fri, 10 May 2024 16:49:52 GMT'}] | 2024-05-13 |
Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep, Andrew
Vlasic, Richard Padbury, and Anh Pham | Hybrid Quantum Graph Neural Network for Molecular Property Prediction | null | null | null | quant-ph cond-mat.mtrl-sci cs.LG | To accelerate the process of materials design, materials science has
increasingly used data driven techniques to extract information from collected
data. Specially, machine learning (ML) algorithms, which span the ML
discipline, have demonstrated ability to predict various properties of
materials with the level of ac... | [{'version': 'v1', 'created': 'Wed, 8 May 2024 16:43:25 GMT'}] | 2024-05-09 |
Bowen Deng, Yunyeong Choi, Peichen Zhong, Janosh Riebesell, Shashwat
Anand, Zhuohan Li, KyuJung Jun, Kristin A. Persson, Gerbrand Ceder | Overcoming systematic softening in universal machine learning
interatomic potentials by fine-tuning | null | null | null | cond-mat.mtrl-sci cs.AI cs.LG | Machine learning interatomic potentials (MLIPs) have introduced a new
paradigm for atomic simulations. Recent advancements have seen the emergence of
universal MLIPs (uMLIPs) that are pre-trained on diverse materials datasets,
providing opportunities for both ready-to-use universal force fields and robust
foundations... | [{'version': 'v1', 'created': 'Sat, 11 May 2024 22:30:47 GMT'}] | 2024-05-14 |
Ashley Lenau, Dennis M. Dimiduk, and Stephen R. Niezgoda | Importance of hyper-parameter optimization during training of
physics-informed deep learning networks | null | null | null | cond-mat.mtrl-sci physics.data-an | Incorporating scientific knowledge into deep learning (DL) models for
materials-based simulations can constrain the network's predictions to be
within the boundaries of the material system. Altering loss functions or adding
physics-based regularization (PBR) terms to reflect material properties informs
a network abou... | [{'version': 'v1', 'created': 'Tue, 14 May 2024 13:21:00 GMT'}, {'version': 'v2', 'created': 'Tue, 21 May 2024 21:31:46 GMT'}] | 2024-05-24 |
Patxi Fernandez-Zelaia, Jason Mayeur, Jiahao Cheng, Yousub Lee, Kevin
Knipe, Kai Kadau | Self-supervised feature distillation and design of experiments for
efficient training of micromechanical deep learning surrogates | null | null | null | cs.CE cond-mat.mtrl-sci | Machine learning surrogate emulators are needed in engineering design and
optimization tasks to rapidly emulate computationally expensive physics-based
models. In micromechanics problems the local full-field response variables are
desired at microstructural length scales. While there has been a great deal of
work on ... | [{'version': 'v1', 'created': 'Thu, 16 May 2024 14:31:30 GMT'}] | 2024-05-17 |
Stephen T. Lam, Shubhojit Banerjee, Rajni Chahal | Uncertainty and Exploration of Deep Learning-based Atomistic Models for
Screening Molten Salt Properties and Compositions | null | null | null | cond-mat.mtrl-sci physics.chem-ph | Due to extreme chemical, thermal, and radiation environments, existing molten
salt property databases lack the necessary experimental thermal properties of
reactor-relevant salt compositions. Meanwhile, simulating these properties
directly is typically either computationally expensive or inaccurate. In recent
years, ... | [{'version': 'v1', 'created': 'Tue, 30 Apr 2024 21:20:55 GMT'}] | 2024-05-20 |
Zijian Du, Luozhijie Jin, Le Shu, Yan Cen, Yuanfeng Xu, Yongfeng Mei
and Hao Zhang | CTGNN: Crystal Transformer Graph Neural Network for Crystal Material
Property Prediction | null | null | null | cond-mat.mtrl-sci physics.comp-ph | The combination of deep learning algorithm and materials science has made
significant progress in predicting novel materials and understanding various
behaviours of materials. Here, we introduced a new model called as the Crystal
Transformer Graph Neural Network (CTGNN), which combines the advantages of
Transformer m... | [{'version': 'v1', 'created': 'Sun, 19 May 2024 10:00:06 GMT'}] | 2024-05-21 |
Chinedu Ekuma | Computational toolkit for predicting thickness of 2D materials using
machine learning and autogenerated dataset by large language model | null | null | null | cond-mat.mtrl-sci cond-mat.str-el | The thickness of 2D materials not only plays a crucial role in determining
the performance of nanoelectronic and optoelectronic devices but also
introduces complexities in predicting volume-dependent properties such as
energy storage capacity, due to the intrinsic vacuum within these materials.
Although a plethora of... | [{'version': 'v1', 'created': 'Fri, 24 May 2024 01:05:47 GMT'}] | 2024-05-27 |
M. Sipil\"a, F. Mehryary, S. Pyysalo, F. Ginter and Milica Todorovi\'c | Question Answering models for information extraction from perovskite
materials science literature | null | null | null | cond-mat.mtrl-sci | Scientific text is a promising source of data in materials science, with
ongoing research into utilising textual data for materials discovery. In this
study, we developed and tested a novel approach to extract material-property
relationships from scientific publications using the Question Answering (QA)
method. QA pe... | [{'version': 'v1', 'created': 'Fri, 24 May 2024 07:24:21 GMT'}, {'version': 'v2', 'created': 'Fri, 13 Sep 2024 11:27:16 GMT'}] | 2024-09-16 |
Avishek Singh and Nirmal Ganguli | Unsupervised Deep Neural Network Approach To Solve Bosonic Systems | null | null | null | cond-mat.mtrl-sci cond-mat.quant-gas | The simulation of quantum many-body systems poses a significant challenge in
physics due to the exponential scaling of Hilbert space with the number of
particles. Traditional methods often struggle with large system sizes and
frustrated lattices. In this research article, we present a novel algorithm
that leverages t... | [{'version': 'v1', 'created': 'Fri, 24 May 2024 12:09:20 GMT'}] | 2024-05-27 |
Avishek Singh and Nirmal Ganguli | Unsupervised Deep Neural Network Approach To Solve Fermionic Systems | null | null | null | cond-mat.mtrl-sci cond-mat.str-el | Solving the Schr\"{o}dinger equation for interacting many-body quantum
systems faces computational challenges due to exponential scaling with system
size. This complexity limits the study of important phenomena in materials
science and physics. We develop an Artificial Neural Network (ANN)-driven
algorithm to simulat... | [{'version': 'v1', 'created': 'Fri, 24 May 2024 12:41:02 GMT'}] | 2024-05-27 |
Haosheng Xu, Dongheng Qian, and Jing Wang | Predicting Many Crystal Properties via an Adaptive Transformer-based
Framework | null | null | null | cond-mat.mtrl-sci cond-mat.mes-hall cs.LG | Machine learning has revolutionized many fields, including materials science.
However, predicting properties of crystalline materials using machine learning
faces challenges in input encoding, output versatility, and interpretability.
We introduce CrystalBERT, an adaptable transformer-based framework integrating
spac... | [{'version': 'v1', 'created': 'Wed, 29 May 2024 09:56:00 GMT'}, {'version': 'v2', 'created': 'Fri, 13 Dec 2024 06:23:03 GMT'}] | 2024-12-16 |
Harveen Kaur, Flaviano Della Pia, Ilyes Batatia, Xavier R. Advincula,
Benjamin X. Shi, Jinggang Lan, G\'abor Cs\'anyi, Angelos Michaelides, and
Venkat Kapil | Data-efficient fine-tuning of foundational models for first-principles
quality sublimation enthalpies | null | null | null | cond-mat.mtrl-sci physics.chem-ph | Calculating sublimation enthalpies of molecular crystal polymorphs is
relevant to a wide range of technological applications. However, predicting
these quantities at first-principles accuracy -- even with the aid of machine
learning potentials -- is a challenge that requires sub-kJ/mol accuracy in the
potential energ... | [{'version': 'v1', 'created': 'Thu, 30 May 2024 16:18:29 GMT'}] | 2024-05-31 |
Malte Grunert, Max Gro{\ss}mann, Erich Runge | Deep learning of spectra: Predicting the dielectric function of
semiconductors | Phys. Rev. Materials 8, L122201 (2024) | 10.1103/PhysRevMaterials.8.L122201 | null | cond-mat.mtrl-sci | Predicting spectra and related properties such as the dielectric function of
crystalline materials based on machine learning has a huge, hitherto
unexplored, technological potential. For this reason, we create an ab initio
database of 9915 dielectric tensors of semiconductors and insulators calculated
in the independ... | [{'version': 'v1', 'created': 'Wed, 12 Jun 2024 13:21:29 GMT'}, {'version': 'v2', 'created': 'Fri, 20 Dec 2024 12:39:13 GMT'}] | 2024-12-23 |
Huazhang Zhang, Hao-Cheng Thong, Louis Bastogne, Churen Gui, Xu He,
Philippe Ghosez | Finite-temperature properties of antiferroelectric perovskite $\rm
PbZrO_3$ from deep learning interatomic potential | null | null | null | cond-mat.mtrl-sci | The prototypical antiferroelectric perovskite $\rm PbZrO_3$ (PZO) has
garnered considerable attentions in recent years due to its significance in
technological applications and fundamental research. Many unresolved issues in
PZO are associated with large length- and time-scales, as well as finite
temperatures, presen... | [{'version': 'v1', 'created': 'Thu, 13 Jun 2024 11:32:16 GMT'}, {'version': 'v2', 'created': 'Wed, 31 Jul 2024 10:22:29 GMT'}, {'version': 'v3', 'created': 'Wed, 21 Aug 2024 11:51:17 GMT'}] | 2024-08-22 |
Davi M F\'ebba, Kingsley Egbo, William A. Callahan, Andriy Zakutayev | From Text to Test: AI-Generated Control Software for Materials Science
Instruments | null | 10.1039/D4DD00143E | null | cond-mat.mtrl-sci cs.AI | Large language models (LLMs) are transforming the landscape of chemistry and
materials science. Recent examples of LLM-accelerated experimental research
include virtual assistants for parsing synthesis recipes from the literature,
or using the extracted knowledge to guide synthesis and characterization.
Despite these... | [{'version': 'v1', 'created': 'Sun, 23 Jun 2024 21:32:57 GMT'}, {'version': 'v2', 'created': 'Tue, 25 Jun 2024 11:34:15 GMT'}] | 2024-11-12 |
Nguyen Tuan Hung, Ryotaro Okabe, Abhijatmedhi Chotrattanapituk, Mingda
Li | Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical
Spectra from Crystal Structure | null | null | null | cond-mat.mtrl-sci physics.app-ph | Optical properties in solids, such as refractive index and absorption, hold
vast applications ranging from solar panels to sensors, photodetectors, and
transparent displays. However, first-principles computation of optical
properties from crystal structures is a complex task due to the high
convergence criteria and c... | [{'version': 'v1', 'created': 'Mon, 24 Jun 2024 14:02:29 GMT'}] | 2024-06-25 |
Zechen Tang, Nianlong Zou, He Li, Yuxiang Wang, Zilong Yuan, Honggeng
Tao, Yang Li, Zezhou Chen, Boheng Zhao, Minghui Sun, Hong Jiang, Wenhui Duan,
Yong Xu | Improving density matrix electronic structure method by deep learning | null | null | null | physics.comp-ph cond-mat.mtrl-sci | The combination of deep learning and ab initio materials calculations is
emerging as a trending frontier of materials science research, with
deep-learning density functional theory (DFT) electronic structure being
particularly promising. In this work, we introduce a neural-network method for
modeling the DFT density ... | [{'version': 'v1', 'created': 'Tue, 25 Jun 2024 13:55:40 GMT'}] | 2024-06-26 |
Michael Moran, Vladimir V. Gusev, Michael W. Gaultois, Dmytro Antypov,
Matthew J. Rosseinsky | Establishing Deep InfoMax as an effective self-supervised learning
methodology in materials informatics | null | null | null | cs.LG cond-mat.mtrl-sci | The scarcity of property labels remains a key challenge in materials
informatics, whereas materials data without property labels are abundant in
comparison. By pretraining supervised property prediction models on
self-supervised tasks that depend only on the "intrinsic information" available
in any Crystallographic I... | [{'version': 'v1', 'created': 'Sun, 30 Jun 2024 11:33:49 GMT'}] | 2024-07-02 |
Somnath Bharech, Yangyiwei Yang, Michael Selzer, Britta Nestler,
Bai-Xiang Xu | ML-extendable framework for multiphysics-multiscale simulation workflow
and data management using Kadi4Mat | null | null | null | cond-mat.mtrl-sci | As material modeling and simulation has become vital for modern materials
science, research data with distinctive physical principles and extensive
volume are generally required for full elucidation of the material behavior
across all relevant scales. Effective workflow and data management, with
corresponding metadat... | [{'version': 'v1', 'created': 'Tue, 2 Jul 2024 11:13:41 GMT'}] | 2024-07-03 |
Seifallah Elfetni and Reza Darvishi Kamachali | PINNs-MPF: A Physics-Informed Neural Network Framework for
Multi-Phase-Field Simulation of Interface Dynamics | null | null | null | cond-mat.mtrl-sci physics.comp-ph | We present an application of Physics-Informed Neural Networks to handle
MultiPhase-Field simulations of microstructure evolution. It has been showcased
that a combination of optimization techniques extended and adapted from the
PINNs literature, and the introduction of specific techniques inspired by the
MPF Method b... | [{'version': 'v1', 'created': 'Tue, 2 Jul 2024 12:55:01 GMT'}, {'version': 'v2', 'created': 'Fri, 30 Aug 2024 18:07:34 GMT'}] | 2024-09-04 |
Ji Wei Yoon, Bangjian Zhou, J Senthilnath | SG-NNP: Species-separated Gaussian Neural Network Potential with Linear
Elemental Scaling and Optimized Dimensions for Multi-component Materials | null | null | null | cond-mat.mtrl-sci | Accurate simulations of materials at long-time and large-length scales have
increasingly been enabled by Machine-learned Interatomic Potentials (MLIPs).
There have been increasing interest on improving the robustness of such models.
To this end, we engineer a novel set of Gaussian-type descriptors that scale
linearly... | [{'version': 'v1', 'created': 'Tue, 9 Jul 2024 07:46:34 GMT'}] | 2024-07-10 |
Zhilong Song, Shuaihua Lu, Minggang Ju, Qionghua Zhou and Jinlan Wang | Is Large Language Model All You Need to Predict the Synthesizability and
Precursors of Crystal Structures? | null | null | null | cond-mat.mtrl-sci | Accessing the synthesizability of crystal structures is pivotal for advancing
the practical application of theoretical material structures designed by
machine learning or high-throughput screening. However, a significant gap
exists between the actual synthesizability and thermodynamic or kinetic
stability, which is c... | [{'version': 'v1', 'created': 'Tue, 9 Jul 2024 16:35:12 GMT'}] | 2024-07-10 |
Joseph Musielewicz, Janice Lan, Matt Uyttendaele, and John R. Kitchin | Improved Uncertainty Estimation of Graph Neural Network Potentials Using
Engineered Latent Space Distances | null | null | null | cs.LG cond-mat.mtrl-sci | Graph neural networks (GNNs) have been shown to be astonishingly capable
models for molecular property prediction, particularly as surrogates for
expensive density functional theory calculations of relaxed energy for novel
material discovery. However, one limitation of GNNs in this context is the lack
of useful uncer... | [{'version': 'v1', 'created': 'Mon, 15 Jul 2024 15:59:39 GMT'}, {'version': 'v2', 'created': 'Mon, 26 Aug 2024 17:31:16 GMT'}] | 2024-08-27 |
Erwin Cazares and Brian E. Schuster | Deep Learning for Quantitative Dynamic Fragmentation Analysis | null | null | null | cond-mat.mtrl-sci | We have developed an image-based convolutional neural network (CNN) that is
applicable for quantitative time-resolved measurements of the fragmentation
behavior of opaque brittle materials using ultra-high speed optical imaging.
This model extends previous work on the U-net model, where we trained binary, 3
and 5 cla... | [{'version': 'v1', 'created': 'Wed, 17 Jul 2024 19:35:57 GMT'}] | 2024-07-19 |
Zilong Yuan, Zechen Tang, Honggeng Tao, Xiaoxun Gong, Zezhou Chen,
Yuxiang Wang, He Li, Yang Li, Zhiming Xu, Minghui Sun, Boheng Zhao, Chong
Wang, Wenhui Duan, Yong Xu | Deep learning density functional theory Hamiltonian in real space | null | null | null | physics.comp-ph cond-mat.mtrl-sci | Deep learning electronic structures from ab initio calculations holds great
potential to revolutionize computational materials studies. While existing
methods proved success in deep-learning density functional theory (DFT)
Hamiltonian matrices, they are limited to DFT programs using localized
atomic-like bases and he... | [{'version': 'v1', 'created': 'Fri, 19 Jul 2024 15:07:22 GMT'}] | 2024-07-22 |
Nihang Fu, Sadman Sadeed Omee, Jianjun Hu | Physical Encoding Improves OOD Performance in Deep Learning Materials
Property Prediction | null | null | null | cond-mat.mtrl-sci | Deep learning (DL) models have been widely used in materials property
prediction with great success, especially for properties with large datasets.
However, the out-of-distribution (OOD) performances of such models are
questionable, especially when the training set is not large enough. Here we
showed that using physi... | [{'version': 'v1', 'created': 'Sun, 21 Jul 2024 16:40:28 GMT'}] | 2024-07-23 |
Alexander Gorfer and David Heuser and Rainer Abart and Christoph
Dellago | Thermodynamics of alkali feldspar solid solutions with varying Al-Si
order: atomistic simulations using a neural network potential | null | null | null | cond-mat.mtrl-sci physics.comp-ph physics.geo-ph | The thermodynamic mixing properties of alkali feldspar solid solutions
between the Na and K end members were computed through atomistic simulations
using a neural network potential. We performed combined molecular dynamics and
Monte Carlo simulations in the semi-grand canonical ensemble at 800 {\deg}C and
considered ... | [{'version': 'v1', 'created': 'Wed, 24 Jul 2024 17:34:03 GMT'}] | 2024-07-25 |
Suchona Akter, Yong Li, Minbum Kim, Md Omar Faruque, Zhonghua Peng,
Praveen K. Thallapally, and Mohammad R. Momeni | Fine-tuning Microporosity of Crystalline Vanadomolybdate Frameworks for
Selective Adsorptive Separation of Kr from Xe | Langmuir 2024 40 (47), 24934-24944 | 10.1021/acs.langmuir.4c02910 | null | cond-mat.mtrl-sci | Selective adsorptive capture and separation of chemically inert Kr and Xe
noble gases with very low ppmv concentrations in air and industrial off-gases
constitute an important technological challenge. Here, using a synergistic
combination of experiment and theory, the microporous crystalline
vanadomolybdates (MoVOx) ... | [{'version': 'v1', 'created': 'Sat, 27 Jul 2024 12:54:17 GMT'}] | 2025-05-08 |
Zihan Wang, Anindya Bhaduri, Hongyi Xu, Liping Wang | An Uncertainty-aware Deep Learning Framework-based Robust Design
Optimization of Metamaterial Units | null | null | null | eess.SP cond-mat.mtrl-sci cs.LG | Mechanical metamaterials represent an innovative class of artificial
structures, distinguished by their extraordinary mechanical characteristics,
which are beyond the scope of traditional natural materials. The use of deep
generative models has become increasingly popular in the design of metamaterial
units. The effe... | [{'version': 'v1', 'created': 'Fri, 19 Jul 2024 22:21:27 GMT'}] | 2024-07-31 |
Christian Venturella, Jiachen Li, Christopher Hillenbrand, Ximena
Leyva Peralta, Jessica Liu, Tianyu Zhu | Unified Deep Learning Framework for Many-Body Quantum Chemistry via
Green's Functions | null | null | null | physics.chem-ph cond-mat.mtrl-sci physics.comp-ph | Quantum many-body methods provide a systematic route to computing electronic
properties of molecules and materials, but high computational costs restrict
their use in large-scale applications. Due to the complexity in many-electron
wavefunctions, machine learning models capable of capturing fundamental
many-body phys... | [{'version': 'v1', 'created': 'Mon, 29 Jul 2024 19:20:52 GMT'}] | 2024-07-31 |
Isaiah A. Moses, Wesley F. Reinhart | Transfer Learning for Multi-material Classification of Transition Metal
Dichalcogenides with Atomic Force Microscopy | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Deep learning models are widely used for the data-driven design of materials
based on atomic force microscopy (AFM) and other scanning probe microscopy.
These tools enhance efficiency in inverse design and characterization of
materials. However, limited and imbalanced experimental materials data
typically available i... | [{'version': 'v1', 'created': 'Tue, 30 Jul 2024 17:06:42 GMT'}, {'version': 'v2', 'created': 'Tue, 10 Dec 2024 22:27:58 GMT'}] | 2024-12-12 |
Shunya Minami, Yoshihiro Hayashi, Stephen Wu, Kenji Fukumizu, Hiroki
Sugisawa, Masashi Ishii, Isao Kuwajima, Kazuya Shiratori, Ryo Yoshida | Scaling Law of Sim2Real Transfer Learning in Expanding Computational
Materials Databases for Real-World Predictions | null | null | null | cond-mat.mtrl-sci cs.LG | To address the challenge of limited experimental materials data, extensive
physical property databases are being developed based on high-throughput
computational experiments, such as molecular dynamics simulations. Previous
studies have shown that fine-tuning a predictor pretrained on a computational
database to a re... | [{'version': 'v1', 'created': 'Wed, 7 Aug 2024 18:47:58 GMT'}] | 2024-08-09 |
Ali Riza Durmaz, Akhil Thomas, Lokesh Mishra, Rachana Niranjan Murthy,
Thomas Straub | MaterioMiner -- An ontology-based text mining dataset for extraction of
process-structure-property entities | null | null | null | cs.CL cond-mat.mtrl-sci | While large language models learn sound statistical representations of the
language and information therein, ontologies are symbolic knowledge
representations that can complement the former ideally. Research at this
critical intersection relies on datasets that intertwine ontologies and text
corpora to enable trainin... | [{'version': 'v1', 'created': 'Mon, 5 Aug 2024 21:42:59 GMT'}] | 2024-08-12 |
A. K. Shargh, C. D. Stiles, J. A. El-Awady | Deep Learning Accelerated Phase Prediction of Refractory Multi-Principal
Element Alloys | null | null | null | cond-mat.mtrl-sci | The tunability of the mechanical properties of refractory
multi-principal-element alloys (RMPEAs) make them attractive for numerous
high-temperature applications. It is well-established that the phase stability
of RMPEAs control their mechanical properties. In this study, we develop a deep
learning framework that is ... | [{'version': 'v1', 'created': 'Mon, 12 Aug 2024 15:42:52 GMT'}] | 2024-08-13 |
Yan Chen, Xueru Wang, Xiaobin Deng, Yilun Liu, Xi Chen, Yunwei Zhang,
Lei Wang, Hang Xiao | MatterGPT: A Generative Transformer for Multi-Property Inverse Design of
Solid-State Materials | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Inverse design of solid-state materials with desired properties represents a
formidable challenge in materials science. Although recent generative models
have demonstrated potential, their adoption has been hindered by limitations
such as inefficiency, architectural constraints and restricted open-source
availability... | [{'version': 'v1', 'created': 'Wed, 14 Aug 2024 15:12:05 GMT'}] | 2024-08-15 |
Qinyang Li, Nicholas Miklaucic, Jianjun Hu | Out-of-distribution materials property prediction using adversarial
learning based fine-tuning | null | null | null | cond-mat.mtrl-sci cs.LG | The accurate prediction of material properties is crucial in a wide range of
scientific and engineering disciplines. Machine learning (ML) has advanced the
state of the art in this field, enabling scientists to discover novel materials
and design materials with specific desired properties. However, one major
challeng... | [{'version': 'v1', 'created': 'Sat, 17 Aug 2024 21:22:21 GMT'}] | 2024-08-20 |
Salvatore Romano, Pablo Montero de Hijes, Matthias Meier, Georg
Kresse, Cesare Franchini, Christoph Dellago | Structure and dynamics of the magnetite(001)/water interface from
molecular dynamics simulations based on a neural network potential | null | null | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | The magnetite/water interface is commonly found in nature and plays a crucial
role in various technological applications. However, our understanding of its
structural and dynamical properties at the molecular scale remains still
limited. In this study, we develop an efficient Behler-Parrinello neural
network potentia... | [{'version': 'v1', 'created': 'Wed, 21 Aug 2024 11:33:24 GMT'}, {'version': 'v2', 'created': 'Fri, 6 Sep 2024 09:22:33 GMT'}] | 2024-09-09 |
Xiangxiang Shen, Zheng Wan, Lingfeng Wen, Licheng Sun, Ou Yang Ming
Jie, JiJUn Cheng, Xuan Tang, Xian Wei | PDDFormer: Pairwise Distance Distribution Graph Transformer for Crystal
Material Property Prediction | null | null | null | cond-mat.mtrl-sci cs.AI | The crystal structure can be simplified as a periodic point set repeating
across the entire three-dimensional space along an underlying lattice.
Traditionally, methods for representing crystals rely on descriptors like
lattice parameters, symmetry, and space groups to characterize the structure.
However, in reality, ... | [{'version': 'v1', 'created': 'Fri, 23 Aug 2024 11:05:48 GMT'}, {'version': 'v2', 'created': 'Mon, 26 Aug 2024 02:42:23 GMT'}, {'version': 'v3', 'created': 'Sun, 22 Sep 2024 13:35:30 GMT'}, {'version': 'v4', 'created': 'Sun, 24 Nov 2024 08:10:52 GMT'}] | 2024-11-26 |
Saurabh Tiwari, Prathamesh Satpute, Supriyo Ghosh | Time series forecasting of multiphase microstructure evolution using
deep learning | Computational Materials Science 247, 113518, 2025 | 10.1016/j.commatsci.2024.113518 | null | cond-mat.mtrl-sci | Microstructure evolution, which plays a critical role in determining
materials properties, is commonly simulated by the high-fidelity but
computationally expensive phase-field method. To address this, we approximate
microstructure evolution as a time series forecasting problem within the domain
of deep learning. Our ... | [{'version': 'v1', 'created': 'Thu, 22 Aug 2024 06:14:06 GMT'}, {'version': 'v2', 'created': 'Thu, 21 Nov 2024 11:32:58 GMT'}] | 2024-11-22 |
Harikrishnan Vijayakumaran, Jonathan B. Russ, Glaucio H. Paulino,
Miguel A. Bessa | Consistent machine learning for topology optimization with
microstructure-dependent neural network material models | null | null | null | cond-mat.mtrl-sci cs.LG cs.NA math.NA | Additive manufacturing methods together with topology optimization have
enabled the creation of multiscale structures with controlled spatially-varying
material microstructure. However, topology optimization or inverse design of
such structures in the presence of nonlinearities remains a challenge due to
the expense ... | [{'version': 'v1', 'created': 'Sun, 25 Aug 2024 14:17:43 GMT'}, {'version': 'v2', 'created': 'Tue, 27 Aug 2024 14:24:52 GMT'}] | 2024-08-28 |
Fanjie Xu, Wentao Guo, Feng Wang, Lin Yao, Hongshuai Wang, Fujie Tang,
Zhifeng Gao, Linfeng Zhang, Weinan E, Zhong-Qun Tian, Jun Cheng | Towards a Unified Benchmark and Framework for Deep Learning-Based
Prediction of Nuclear Magnetic Resonance Chemical Shifts | null | null | null | physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.chem-ph | The study of structure-spectrum relationships is essential for spectral
interpretation, impacting structural elucidation and material design.
Predicting spectra from molecular structures is challenging due to their
complex relationships. Herein, we introduce NMRNet, a deep learning framework
using the SE(3) Transform... | [{'version': 'v1', 'created': 'Wed, 28 Aug 2024 10:11:00 GMT'}] | 2024-08-29 |
Xiuying Zhang, Linqiang Xu, Jing Lu, Zhaofu Zhang, and Lei Shen | Physics-integrated Neural Network for Quantum Transport Prediction of
Field-effect Transistor | null | null | null | cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph | Quantum-mechanics-based transport simulation is of importance for the design
of ultra-short channel field-effect transistors (FETs) with its capability of
understanding the physical mechanism, while facing the primary challenge of the
high computational intensity. Traditional machine learning is expected to
accelerat... | [{'version': 'v1', 'created': 'Fri, 30 Aug 2024 05:38:12 GMT'}] | 2024-09-02 |
Alexander New, Nam Q. Le, Michael J. Pekala, Christopher D. Stiles | Self-supervised learning for crystal property prediction via denoising | null | null | null | cs.LG cond-mat.mtrl-sci | Accurate prediction of the properties of crystalline materials is crucial for
targeted discovery, and this prediction is increasingly done with data-driven
models. However, for many properties of interest, the number of materials for
which a specific property has been determined is much smaller than the number
of kno... | [{'version': 'v1', 'created': 'Fri, 30 Aug 2024 12:53:40 GMT'}] | 2024-09-02 |
Tsz Wai Ko and Shyue Ping Ong | Data-Efficient Construction of High-Fidelity Graph Deep Learning
Interatomic Potentials | null | null | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | Machine learning potentials (MLPs) have become an indispensable tool in
large-scale atomistic simulations because of their ability to reproduce ab
initio potential energy surfaces (PESs) very accurately at a fraction of
computational cost. For computational efficiency, the training data for most
MLPs today are comput... | [{'version': 'v1', 'created': 'Mon, 2 Sep 2024 05:57:32 GMT'}] | 2024-09-04 |
Zirui Zhao, Xiaoke Wang, Si Wu, Pengfei Zhou, Qian Zhao, Guanping Xu,
Kaitong Sun, Hai-Feng Li | Deep learning-driven evaluation and prediction of ion-doped NASICON
materials for enhanced solid-state battery performance | AAPPS Bulletin, 2024, 34(1): 26 | 10.1007/s43673-024-00131-9 | null | cond-mat.mtrl-sci | We developed a convolutional neural network (CNN) model capable of predicting
the performance of various ion-doped NASICON compounds by leveraging extensive
datasets from prior experimental investigation.The model demonstrated high
accuracy and efficiency in predicting ionic conductivity and electrochemical
propertie... | [{'version': 'v1', 'created': 'Mon, 2 Sep 2024 02:20:44 GMT'}, {'version': 'v2', 'created': 'Mon, 9 Sep 2024 02:46:15 GMT'}] | 2025-01-13 |
Koki Ueno, Satoru Ohuchi, Kazuhide Ichikawa, Kei Amii, Kensuke
Wakasugi | SpinMultiNet: Neural Network Potential Incorporating Spin Degrees of
Freedom with Multi-Task Learning | null | null | null | cond-mat.mtrl-sci cs.LG | Neural Network Potentials (NNPs) have attracted significant attention as a
method for accelerating density functional theory (DFT) calculations. However,
conventional NNP models typically do not incorporate spin degrees of freedom,
limiting their applicability to systems where spin states critically influence
materia... | [{'version': 'v1', 'created': 'Thu, 5 Sep 2024 05:13:28 GMT'}, {'version': 'v2', 'created': 'Sun, 8 Sep 2024 23:58:44 GMT'}] | 2024-09-10 |
Wei Lu and Rachel K. Luu and Markus J. Buehler | Fine-tuning large language models for domain adaptation: Exploration of
training strategies, scaling, model merging and synergistic capabilities | null | null | null | cs.CL cond-mat.mtrl-sci cs.AI | The advancement of Large Language Models (LLMs) for domain applications in
fields such as materials science and engineering depends on the development of
fine-tuning strategies that adapt models for specialized, technical
capabilities. In this work, we explore the effects of Continued Pretraining
(CPT), Supervised Fi... | [{'version': 'v1', 'created': 'Thu, 5 Sep 2024 11:49:53 GMT'}] | 2024-09-06 |
Abdelwahab Kawafi, Lars K\"urten, Levke Ortlieb, Yushi Yang, Abraham
Mauleon Amieva, James E. Hallett and C.Patrick Royall | Colloidoscope: Detecting Dense Colloids in 3d with Deep Learning | null | null | null | cond-mat.soft cond-mat.mtrl-sci cond-mat.stat-mech | Colloidoscope is a deep learning pipeline employing a 3D residual Unet
architecture, designed to enhance the tracking of dense colloidal suspensions
through confocal microscopy. This methodology uses a simulated training dataset
that reflects a wide array of real-world imaging conditions, specifically
targeting high ... | [{'version': 'v1', 'created': 'Fri, 6 Sep 2024 20:21:33 GMT'}] | 2024-09-10 |
Ayush Jain, Rishi Gurnani, Arunkumar Rajan, H. Jerry Qi, Rampi
Ramprasad | A Physics-Enforced Neural Network to Predict Polymer Melt Viscosity | null | 10.1038/s41524-025-01532-6 | null | cs.CE cond-mat.mtrl-sci | Achieving superior polymeric components through additive manufacturing (AM)
relies on precise control of rheology. One key rheological property
particularly relevant to AM is melt viscosity ($\eta$). Melt viscosity is
influenced by polymer chemistry, molecular weight ($M_w$), polydispersity,
induced shear rate ($\dot... | [{'version': 'v1', 'created': 'Sun, 8 Sep 2024 22:52:24 GMT'}] | 2025-04-25 |
Nicholas Beaver, Aniruddha Dive, Marina Wong, Keita Shimanuki, Ananya
Patil, Anthony Ferrell, Mohsen B. Kivy | Rapid Assessment of Stable Crystal Structures in Single Phase High
Entropy Alloys Via Graph Neural Network Based Surrogate Modelling | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn | In an effort to develop a rapid, reliable, and cost-effective method for
predicting the structure of single-phase high entropy alloys, a Graph Neural
Network (ALIGNN-FF) based approach was introduced. This method was successfully
tested on 132 different high entropy alloys, and the results were analyzed and
compared ... | [{'version': 'v1', 'created': 'Wed, 11 Sep 2024 23:34:48 GMT'}] | 2024-09-13 |
Jun Li, Wenqi Fang, Shangjian Jin, Tengdong Zhang, Yanling Wu, Xiaodan
Xu, Yong Liu and Dao-Xin Yao | A deep learning approach to search for superconductors from electronic
bands | null | null | null | cond-mat.supr-con cond-mat.mtrl-sci | Energy band theory is a foundational framework in condensed matter physics.
In this work, we employ a deep learning method, BNAS, to find a direct
correlation between electronic band structure and superconducting transition
temperature. Our findings suggest that electronic band structures can act as
primary indicator... | [{'version': 'v1', 'created': 'Thu, 12 Sep 2024 03:02:59 GMT'}] | 2024-09-13 |
Xiao-Qi Han, Zhenfeng Ouyang, Peng-Jie Guo, Hao Sun, Ze-Feng Gao and
Zhong-Yi Lu | InvDesFlow: An AI-driven materials inverse design workflow to explore
possible high-temperature superconductors | Chin. Phys. Lett. 2025,42(4): 047301 | 10.1088/0256-307X/42/4/047301 | null | cond-mat.supr-con cond-mat.mtrl-sci cs.AI physics.comp-ph | The discovery of new superconducting materials, particularly those exhibiting
high critical temperature ($T_c$), has been a vibrant area of study within the
field of condensed matter physics. Conventional approaches primarily rely on
physical intuition to search for potential superconductors within the existing
datab... | [{'version': 'v1', 'created': 'Thu, 12 Sep 2024 14:16:56 GMT'}, {'version': 'v2', 'created': 'Mon, 2 Dec 2024 14:29:14 GMT'}, {'version': 'v3', 'created': 'Tue, 13 May 2025 08:22:00 GMT'}] | 2025-05-14 |
Israrul H. Hashmi, Himanshu, Rahul Karmakar and Tarak K Patra | Extrapolative ML Models for Copolymers | null | null | null | cond-mat.soft cond-mat.mtrl-sci cs.LG | Machine learning models have been progressively used for predicting materials
properties. These models can be built using pre-existing data and are useful
for rapidly screening the physicochemical space of a material, which is
astronomically large. However, ML models are inherently interpolative, and
their efficacy f... | [{'version': 'v1', 'created': 'Sun, 15 Sep 2024 11:02:01 GMT'}] | 2024-09-17 |
Shaswat Mohanty, Yifan Wang, Wei Cai | Generalizability of Graph Neural Network Force Fields for Predicting
Solid-State Properties | null | null | null | cs.LG cond-mat.mtrl-sci cs.NA math.NA | Machine-learned force fields (MLFFs) promise to offer a computationally
efficient alternative to ab initio simulations for complex molecular systems.
However, ensuring their generalizability beyond training data is crucial for
their wide application in studying solid materials. This work investigates the
ability of a... | [{'version': 'v1', 'created': 'Mon, 16 Sep 2024 02:14:26 GMT'}, {'version': 'v2', 'created': 'Sat, 21 Dec 2024 16:21:51 GMT'}] | 2024-12-24 |
Amir Omranpour and J\"org Behler | A High-Dimensional Neural Network Potential for Co$_3$O$_4$ | null | null | null | cond-mat.mtrl-sci | The Co$_3$O$_4$ spinel is an important material in oxidation catalysis. Its
properties under catalytic conditions, i.e., at finite temperatures, can be
studied by molecular dynamics simulations, which critically depend on an
accurate description of the atomic interactions. Due to the high complexity of
Co$_3$O$_4$, w... | [{'version': 'v1', 'created': 'Tue, 17 Sep 2024 10:02:27 GMT'}] | 2024-09-18 |
Luke P. J. Gilligan, Matteo Cobelli, Hasan M. Sayeed, Taylor D. Sparks
and Stefano Sanvito | Sampling Latent Material-Property Information From LLM-Derived Embedding
Representations | null | null | null | cs.CL cond-mat.mtrl-sci | Vector embeddings derived from large language models (LLMs) show promise in
capturing latent information from the literature. Interestingly, these can be
integrated into material embeddings, potentially useful for data-driven
predictions of materials properties. We investigate the extent to which
LLM-derived vectors ... | [{'version': 'v1', 'created': 'Wed, 18 Sep 2024 13:22:04 GMT'}] | 2024-09-19 |
Jaime A. Berkovich and Markus J. Buehler | LifeGPT: Topology-Agnostic Generative Pretrained Transformer Model for
Cellular Automata | null | null | null | cs.AI cond-mat.mtrl-sci cond-mat.stat-mech math.DS | Conway's Game of Life (Life), a well known algorithm within the broader class
of cellular automata (CA), exhibits complex emergent dynamics, with extreme
sensitivity to initial conditions. Modeling and predicting such intricate
behavior without explicit knowledge of the system's underlying topology
presents a signifi... | [{'version': 'v1', 'created': 'Tue, 3 Sep 2024 11:43:16 GMT'}, {'version': 'v2', 'created': 'Thu, 17 Oct 2024 16:55:02 GMT'}] | 2024-10-18 |
Teng Long, Yixuan Zhang, Hongbin Zhang | Generative deep learning for the inverse design of materials | null | null | null | cond-mat.mtrl-sci physics.comp-ph | In addition to the forward inference of materials properties using machine
learning, generative deep learning techniques applied on materials science
allow the inverse design of materials, i.e., assessing the
composition-processing-(micro-)structure-property relationships in a reversed
way. In this review, we focus o... | [{'version': 'v1', 'created': 'Fri, 27 Sep 2024 20:10:19 GMT'}] | 2024-10-01 |
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