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 |
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Christopher Sims | Edge Detection and Image Filter algorithms for Spectroscopic Analysis
with Deep Learning Applications | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Edge detection and image filters are commonly used in computer vision.
However, they have never been applied to the data analysis of angle-resolved
photoemission spectroscopy (ARPES) data before in a systematic fashion. In this
paper we will use the Sobel, Laplacian of a gaussian (LoG), Canny, Prewitt,
Roberts, and f... | [{'version': 'v1', 'created': 'Mon, 14 Mar 2022 02:31:06 GMT'}] | 2022-03-15 |
Wei Wang, Loai Danial, Yang Li, Eric Herbelin, Evgeny Pikhay, Yakov
Roizin, Barak Hoffer, Zhongrui Wang, Shahar Kvatinsky | A memristive deep belief neural network based on silicon synapses | Nature Electronics, 5, 870 (2022) | 10.1038/s41928-022-00878-9 | null | physics.app-ph cond-mat.dis-nn cond-mat.mtrl-sci cs.ET | Memristor-based neuromorphic computing could overcome the limitations of
traditional von Neumann computing architectures -- in which data are shuffled
between separate memory and processing units -- and improve the performance of
deep neural networks. However, this will require accurate synaptic-like device
performan... | [{'version': 'v1', 'created': 'Thu, 17 Mar 2022 02:54:55 GMT'}, {'version': 'v2', 'created': 'Fri, 20 May 2022 01:26:46 GMT'}, {'version': 'v3', 'created': 'Fri, 21 Jul 2023 01:26:34 GMT'}] | 2023-07-24 |
James Chapman, Tim Hsu, Xiao Chen, Tae Wook Heo, Brandon C. Wood | Quantifying Disorder One Atom at a Time Using an Interpretable Graph
Neural Network Paradigm | null | 10.1038/s41467-023-39755-0 | null | cond-mat.dis-nn cond-mat.mes-hall cond-mat.mtrl-sci | Quantifying the level of atomic disorder within materials is critical to
understanding how evolving local structural environments dictate performance
and durability. Here, we leverage graph neural networks to define a physically
interpretable metric for local disorder. This metric encodes the diversity of
the local a... | [{'version': 'v1', 'created': 'Fri, 18 Mar 2022 22:12:42 GMT'}, {'version': 'v2', 'created': 'Thu, 16 Jun 2022 19:09:47 GMT'}] | 2023-08-02 |
Marcin Abram, Keith Burghardt, Greg Ver Steeg, Aram Galstyan, Remi
Dingreville | Inferring topological transitions in pattern-forming processes with
self-supervised learning | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn cs.CV cs.LG | The identification and classification of transitions in topological and
microstructural regimes in pattern-forming processes are critical for
understanding and fabricating microstructurally precise novel materials in many
application domains. Unfortunately, relevant microstructure transitions may
depend on process pa... | [{'version': 'v1', 'created': 'Sat, 19 Mar 2022 00:47:50 GMT'}, {'version': 'v2', 'created': 'Wed, 10 Aug 2022 20:11:40 GMT'}] | 2022-08-12 |
Rongzhi Dong, Yong Zhao, Yuqi Song, Nihang Fu, Sadman Sadeed Omee,
Sourin Dey, Qinyang Li, Lai Wei, Jianjun Hu | DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from
Materials Composition | null | null | null | cond-mat.mtrl-sci | One of the long-standing problems in materials science is how to predict a
material's structure and then its properties given only its composition.
Experimental characterization of crystal structures has been widely used for
structure determination, which is however too expensive for high-throughput
screening. At the... | [{'version': 'v1', 'created': 'Sun, 27 Mar 2022 15:20:11 GMT'}] | 2022-03-29 |
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu, Nihang Fu,
Mohammed Al-Fahdi, Ming Hu, and Jianjun Hu | Physics Guided Deep Learning for Generative Design of Crystal Materials
with Symmetry Constraints | null | null | null | cond-mat.mtrl-sci cs.LG | Discovering new materials is a challenging task in materials science crucial
to the progress of human society. Conventional approaches based on experiments
and simulations are labor-intensive or costly with success heavily depending on
experts' heuristic knowledge. Here, we propose a deep learning based Physics
Guide... | [{'version': 'v1', 'created': 'Sun, 27 Mar 2022 17:21:36 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Jul 2022 15:57:26 GMT'}, {'version': 'v3', 'created': 'Tue, 13 Dec 2022 17:01:31 GMT'}] | 2022-12-14 |
Alexander Kovacs, Lukas Exl, Alexander Kornell, Johann Fischbacher,
Markus Hovorka, Markus Gusenbauer, Leoni Breth, Harald Oezelt, Masao Yano,
Noritsugu Sakuma, Akihito Kinoshita, Tetsuya Shoji, Akira Kato, Thomas
Schrefl | Exploring the hysteresis properties of nanocrystalline permanent magnets
using deep learning | null | null | null | cond-mat.mtrl-sci physics.comp-ph | We demonstrate the use of model order reduction and neural networks for
estimating the hysteresis properties of nanocrystalline permanent magnets from
microstructure. With a data-driven approach, we learn the demagnetization curve
from data-sets created by grain growth and micromagnetic simulations. We show
that the ... | [{'version': 'v1', 'created': 'Wed, 30 Mar 2022 21:04:09 GMT'}] | 2022-04-01 |
Koji Shimizu, Ying Dou, Elvis F. Arguelles, Takumi Moriya, Emi
Minamitani, and Satoshi Watanabe | Using neural network potential to study point defect properties in
multiple charge states of GaN with nitrogen vacancy | null | null | null | cond-mat.mtrl-sci | Investigation of charged defects is necessary to understand the properties of
semiconductors. While density functional theory calculations can accurately
describe the relevant physical quantities, these calculations increase the
computational loads substantially, which often limits the application of this
method to l... | [{'version': 'v1', 'created': 'Thu, 31 Mar 2022 04:33:36 GMT'}] | 2022-04-01 |
H. Pahlavani, M. Amani, M. Cruz Sald\'ivar, J. Zhou, M. J. Mirzaali,
A. A. Zadpoor | Deep learning for the rare-event rational design of 3D printed
multi-material mechanical metamaterials | null | null | null | cond-mat.mtrl-sci cs.LG | Emerging multi-material 3D printing techniques have paved the way for the
rational design of metamaterials with not only complex geometries but also
arbitrary distributions of multiple materials within those geometries. Varying
the spatial distribution of multiple materials gives rise to many interesting
and potentia... | [{'version': 'v1', 'created': 'Mon, 4 Apr 2022 18:04:23 GMT'}] | 2022-04-06 |
Qi-Jun Hong | Melting temperature prediction via first principles and deep learning | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Melting is a high temperature process that requires extensive sampling of
configuration space, thus making melting temperature prediction computationally
very expensive and challenging. Over the past few years, I have built two
methods to address this challenge, one via direct density functional theory
(DFT) molecula... | [{'version': 'v1', 'created': 'Sun, 10 Apr 2022 18:17:32 GMT'}] | 2022-04-12 |
Dillan J. Chang, Colum M. O'Leary, Cong Su, Salman Kahn, Alex Zettl,
Jim Ciston, Peter Ercius and Jianwei Miao | Deep Learning Coherent Diffractive Imaging | null | null | null | cond-mat.mtrl-sci | We report the development of deep learning coherent electron diffractive
imaging at sub-angstrom resolution using convolutional neural networks (CNNs)
trained with only simulated data. We experimentally demonstrate this method by
applying the trained CNNs to directly recover the phase images from electron
diffraction... | [{'version': 'v1', 'created': 'Mon, 18 Apr 2022 04:05:41 GMT'}] | 2022-04-19 |
Giovanni Bertoni, Enzo Rotunno, Daan Marsmans, Peter Tiemeijer, Amir
H. Tavabi, Rafal E. Dunin-Borkowski and Vincenzo Grillo | Near-real-time diagnosis of electron optical phase aberrations in
scanning transmission electron microscopy using an artificial neural network | Ultramicroscopy 2023 | 10.1016/j.ultramic.2022.113663 | null | physics.ins-det cond-mat.mtrl-sci physics.optics | The key to optimizing spatial resolution in a state-of-the-art scanning
transmission electron microscope is the ability to precisely measure and
correct for electron optical aberrations of the probe-forming lenses. Several
diagnostic methods for aberration measurement and correction with maximum
precision and accurac... | [{'version': 'v1', 'created': 'Sat, 23 Apr 2022 19:07:43 GMT'}] | 2023-01-04 |
Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Edirisuriya M. D.
Siriwardane, Fanglin Chen, Jianjun Hu | Crystal Transformer: Self-learning neural language model for Generative
and Tinkering Design of Materials | null | null | null | cond-mat.mtrl-sci cs.LG | Self-supervised neural language models have recently achieved unprecedented
success, from natural language processing to learning the languages of
biological sequences and organic molecules. These models have demonstrated
superior performance in the generation, structure classification, and
functional predictions for... | [{'version': 'v1', 'created': 'Mon, 25 Apr 2022 20:20:26 GMT'}] | 2022-04-27 |
Jim James, Nathan Pruyne, Tiberiu Stan, Marcus Schwarting, Jiwon Yeom,
Seungbum Hong, Peter Voorhees, Ben Blaiszik, Ian Foster | 3D Convolutional Neural Networks for Dendrite Segmentation Using
Fine-Tuning and Hyperparameter Optimization | null | null | null | cs.CV cond-mat.mtrl-sci eess.IV | Dendritic microstructures are ubiquitous in nature and are the primary
solidification morphologies in metallic materials. Techniques such as x-ray
computed tomography (XCT) have provided new insights into dendritic phase
transformation phenomena. However, manual identification of dendritic
morphologies in microscopy ... | [{'version': 'v1', 'created': 'Mon, 2 May 2022 19:20:05 GMT'}] | 2022-05-04 |
Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, and Zachary Ulissi | FINETUNA: Fine-tuning Accelerated Molecular Simulations | null | null | null | physics.comp-ph cond-mat.mtrl-sci cs.LG | Machine learning approaches have the potential to approximate Density
Functional Theory (DFT) for atomistic simulations in a computationally
efficient manner, which could dramatically increase the impact of computational
simulations on real-world problems. However, they are limited by their accuracy
and the cost of g... | [{'version': 'v1', 'created': 'Mon, 2 May 2022 21:36:01 GMT'}, {'version': 'v2', 'created': 'Fri, 1 Jul 2022 18:59:42 GMT'}] | 2022-07-05 |
Rishikesh Magar, Yuyang Wang, and Amir Barati Farimani | Crystal Twins: Self-supervised Learning for Crystalline Material
Property Prediction | null | null | null | cs.LG cond-mat.mtrl-sci | Machine learning (ML) models have been widely successful in the prediction of
material properties. However, large labeled datasets required for training
accurate ML models are elusive and computationally expensive to generate.
Recent advances in Self-Supervised Learning (SSL) frameworks capable of
training ML models ... | [{'version': 'v1', 'created': 'Wed, 4 May 2022 05:08:46 GMT'}] | 2022-05-05 |
B. Burton, W.T. Nash, N. Birbilis | RustSEG -- Automated segmentation of corrosion using deep learning | null | null | null | cs.CV cond-mat.mtrl-sci | The inspection of infrastructure for corrosion remains a task that is
typically performed manually by qualified engineers or inspectors. This task of
inspection is laborious, slow, and often requires complex access. Recently,
deep learning based algorithms have revealed promise and performance in the
automatic detect... | [{'version': 'v1', 'created': 'Wed, 11 May 2022 11:48:02 GMT'}] | 2022-05-12 |
Mao Su, Ji-Hui Yang, Hong-Jun Xiang and Xin-Gao Gong | Efficient determination of the Hamiltonian and electronic properties
using graph neural network with complete local coordinates | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph | Despite the successes of machine learning methods in physical sciences,
prediction of the Hamiltonian, and thus electronic properties, is still
unsatisfactory. Here, based on graph neural network architecture, we present an
extendable neural network model to determine the Hamiltonian from ab initio
data, with only lo... | [{'version': 'v1', 'created': 'Wed, 11 May 2022 13:20:20 GMT'}, {'version': 'v2', 'created': 'Wed, 11 Jan 2023 17:56:34 GMT'}] | 2023-01-12 |
Darren C. Pagan, Calvin R. Pash, Austin R. Benson, Matthew P. Kasemer | Graph Neural Network Modeling of Grain-scale Anisotropic Elastic
Behavior using Simulated and Measured Microscale Data | null | null | null | cond-mat.mtrl-sci | Here we assess the applicability of graph neural networks (GNNs) for
predicting the grain-scale elastic response of polycrystalline metallic alloys.
Using GNN surrogate models, grain-averaged stresses during uniaxial elastic
tension in Low Solvus High Refractory (LSHR) Ni Superalloy and Ti 7wt%Al
(Ti-7Al), as example... | [{'version': 'v1', 'created': 'Thu, 12 May 2022 19:25:50 GMT'}, {'version': 'v2', 'created': 'Tue, 30 Aug 2022 18:24:36 GMT'}] | 2022-09-01 |
Emanuele Costa, Giuseppe Scriva, Rosario Fazio, Sebastiano Pilati | Deep learning density functionals for gradient descent optimization | Phys. Rev. E 106, 045309 (2022) | 10.1103/PhysRevE.106.045309 | null | physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.other cond-mat.quant-gas | Machine-learned regression models represent a promising tool to implement
accurate and computationally affordable energy-density functionals to solve
quantum many-body problems via density functional theory. However, while they
can easily be trained to accurately map ground-state density profiles to the
corresponding... | [{'version': 'v1', 'created': 'Tue, 17 May 2022 13:57:08 GMT'}, {'version': 'v2', 'created': 'Mon, 7 Nov 2022 12:14:42 GMT'}] | 2022-11-08 |
Md Esharuzzaman Emu | Predictions of Electromotive Force of Magnetic Shape Memory Alloy (MSMA)
Using Constitutive Model and Generalized Regression Neural Network | null | 10.1088/1361-665X/acb2a1 | null | cond-mat.mtrl-sci stat.ML | Ferromagnetic shape memory alloys (MSMAs), such as Ni-Mn-Ga single crystals,
can exhibit the shape memory effect due to an applied magnetic field at room
temperature. Under a variable magnetic field and a constant bias stress
loading, MSMAs have been used for actuation applications. This work introduced
a new feature... | [{'version': 'v1', 'created': 'Wed, 8 Jun 2022 06:38:33 GMT'}, {'version': 'v2', 'created': 'Wed, 19 Oct 2022 03:50:36 GMT'}, {'version': 'v3', 'created': 'Fri, 11 Nov 2022 21:37:38 GMT'}] | 2023-01-20 |
Kihyun Lee, Jinsub Park, Soyeon Choi, Yangjin Lee, Sol Lee, Joowon
Jung, Jong-Young Lee, Farman Ullah, Zeeshan Tahir, Yong Soo Kim, Gwan-Hyoung
Lee, and Kwanpyo Kim | STEM image analysis based on deep learning: identification of vacancy
defects and polymorphs of ${MoS_2}$ | Nano Letters, 2022 | 10.1021/acs.nanolett.2c00550 | null | cond-mat.mes-hall cond-mat.mtrl-sci cs.CV | Scanning transmission electron microscopy (STEM) is an indispensable tool for
atomic-resolution structural analysis for a wide range of materials. The
conventional analysis of STEM images is an extensive hands-on process, which
limits efficient handling of high-throughput data. Here we apply a fully
convolutional net... | [{'version': 'v1', 'created': 'Thu, 9 Jun 2022 04:43:56 GMT'}] | 2022-06-10 |
Ronaldo Giro, Hsianghan Hsu, Akihiro Kishimoto, Toshiyuki Hama,
Rodrigo F. Neumann, Binquan Luan, Seiji Takeda, Lisa Hamada and Mathias B.
Steiner | AI powered, automated discovery of polymer membranes for carbon capture | null | null | null | cond-mat.mtrl-sci | The generation of molecules with Artificial Intelligence (AI) is poised to
revolutionize materials discovery. Potential applications range from
development of potent drugs to efficient carbon capture and separation
technologies. However, existing computational frameworks lack automated
training data creation and phys... | [{'version': 'v1', 'created': 'Wed, 29 Jun 2022 13:31:24 GMT'}, {'version': 'v2', 'created': 'Thu, 28 Jul 2022 17:14:06 GMT'}] | 2022-07-29 |
Claus O.W. Trost (1), Stanislav Zak (1), Sebastian Schaffer (2 and 3),
Christian Saringer (4), Lukas Exl (2 and 3) and Megan J. Cordill (1 and 4)
((1) Erich Schmid Institute of Materials Science, Austrian Academy of
Sciences, Leoben, Austria., (2) Wolfgang Pauli Institute c/o Faculty of
Mathematics, University ... | Bridging Fidelities to Predict Nanoindentation Tip Radii Using
Interpretable Deep Learning Models | null | 10.1007/s11837-022-05233-z | null | cond-mat.mtrl-sci | As the need for miniaturized structural and functional materials has
increased,the need for precise materials characterizaton has also expanded.
Nanoindentation is a popular method that can be used to measure material
mechanical behavior which enables high-throughput experiments and, in some
cases, can also provide i... | [{'version': 'v1', 'created': 'Fri, 1 Jul 2022 07:26:54 GMT'}] | 2022-07-04 |
Nikil Ravi, Pranshu Chaturvedi, E. A. Huerta, Zhengchun Liu, Ryan
Chard, Aristana Scourtas, K.J. Schmidt, Kyle Chard, Ben Blaiszik and Ian
Foster | FAIR principles for AI models with a practical application for
accelerated high energy diffraction microscopy | Scientific Data 9, 657 (2022) | 10.1038/s41597-022-01712-9 | null | cs.AI cond-mat.mtrl-sci cs.LG | A concise and measurable set of FAIR (Findable, Accessible, Interoperable and
Reusable) principles for scientific data is transforming the state-of-practice
for data management and stewardship, supporting and enabling discovery and
innovation. Learning from this initiative, and acknowledging the impact of
artificial ... | [{'version': 'v1', 'created': 'Fri, 1 Jul 2022 18:11:12 GMT'}, {'version': 'v2', 'created': 'Thu, 14 Jul 2022 18:11:27 GMT'}, {'version': 'v3', 'created': 'Wed, 21 Dec 2022 17:37:27 GMT'}] | 2023-08-21 |
Siyu Isaac Parker Tian, Zekun Ren, Selvaraj Venkataraj, Yuanhang
Cheng, Daniil Bash, Felipe Oviedo, J. Senthilnath, Vijila Chellappan, Yee-Fun
Lim, Armin G. Aberle, Benjamin P MacLeod, Fraser G. L. Parlane, Curtis P.
Berlinguette, Qianxiao Li, Tonio Buonassisi, Zhe Liu | Tackling Data Scarcity with Transfer Learning: A Case Study of Thickness
Characterization from Optical Spectra of Perovskite Thin Films | null | null | null | cs.LG cond-mat.mtrl-sci eess.IV physics.optics | Transfer learning increasingly becomes an important tool in handling data
scarcity often encountered in machine learning. In the application of
high-throughput thickness as a downstream process of the high-throughput
optimization of optoelectronic thin films with autonomous workflows, data
scarcity occurs especially ... | [{'version': 'v1', 'created': 'Tue, 14 Jun 2022 16:26:15 GMT'}, {'version': 'v2', 'created': 'Tue, 20 Dec 2022 08:51:48 GMT'}] | 2022-12-21 |
Alexey N. Korovin, Innokentiy S. Humonen, Artem I. Samtsevich, Roman
A. Eremin, Artem I. Vasilyev, Vladimir D. Lazarev, Semen A. Budennyy | Boosting Heterogeneous Catalyst Discovery by Structurally Constrained
Deep Learning Models | Materials Today Chemistry 2023, 30, 101541 | 10.1016/j.mtchem.2023.101541 | null | cond-mat.mtrl-sci cs.LG | The discovery of new catalysts is one of the significant topics of
computational chemistry as it has the potential to accelerate the adoption of
renewable energy sources. Recently developed deep learning approaches such as
graph neural networks (GNNs) open new opportunity to significantly extend scope
for modelling n... | [{'version': 'v1', 'created': 'Mon, 11 Jul 2022 17:01:28 GMT'}, {'version': 'v2', 'created': 'Mon, 5 Sep 2022 13:49:45 GMT'}, {'version': 'v3', 'created': 'Sun, 2 Oct 2022 15:30:48 GMT'}] | 2023-04-24 |
Sadhana Singh, Avinash G. Khanderao, Mukul Gupta, Ilya Sergeev, H. C.
Wille, Kai Schlage, Marcus Herlitschke, Dileep Kumar | Origin of exchange bias in [Co/Pt]ML/Fe multilayer with orthogonal
magnetic anisotropies | Phys. Rev. B 108, 075414 (2023) | 10.1103/PhysRevB.108.075414 | null | cond-mat.mtrl-sci | Magnetization reversal of soft ferromagnetic Fe layer, coupled to [Co/Pt]ML
multilayer [ML] with perpendicular magnetic anisotropy (PMA), has been studied
in-situ with an aim to understand the origin of exchange bias (EB) in
orthogonal magnetic anisotropic systems. The interface remanant state of the ML
is modified b... | [{'version': 'v1', 'created': 'Fri, 15 Jul 2022 09:48:17 GMT'}, {'version': 'v2', 'created': 'Tue, 19 Jul 2022 14:23:44 GMT'}] | 2023-09-06 |
Sachin Gautham and Tarak Patra | Deep Learning Potential of Mean Force between Polymer Grafted
Nanoparticles | null | null | null | cond-mat.mtrl-sci cond-mat.soft | Grafting polymer chains on nanoparticles surfaces is a well-known route to
control their self assembly and distribution in a polymer matrix. A wide
variety of self assembled structures are achieved by changing the grafting
patterns on an individual nanoparticle surface. However, accurate estimation of
the effective p... | [{'version': 'v1', 'created': 'Mon, 18 Jul 2022 15:26:53 GMT'}] | 2022-07-19 |
Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna, Mathew
Cherukara, Valeria Molinero, Subramanian KRS Sankaranarayanan | CEGANN: Crystal Edge Graph Attention Neural Network for multiscale
classification of materials environment | null | null | null | cond-mat.mtrl-sci | Machine learning models and applications in materials design and discovery
typically involve the use of feature representations or "descriptors" followed
by a learning algorithm that maps them to a user-desired property of interest.
Most popular mathematical formulation-based descriptors are not unique across
atomic ... | [{'version': 'v1', 'created': 'Wed, 20 Jul 2022 19:42:46 GMT'}, {'version': 'v2', 'created': 'Tue, 15 Nov 2022 05:10:45 GMT'}] | 2022-11-16 |
Jingrui Wei, Ben Blaiszik, Aristana Scourtas, Dane Morgan, and Paul M.
Voyles | Benchmark tests of atom segmentation deep learning models with a
consistent dataset | Microsc. Microanal. (2022) | 10.1093/micmic/ozac043 | null | cond-mat.mtrl-sci cond-mat.dis-nn | The information content of atomic resolution scanning transmission electron
microscopy (STEM) images can often be reduced to a handful of parameters
describing each atomic column, chief amongst which is the column position.
Neural networks (NNs) are a high performance, computationally efficient method
to automaticall... | [{'version': 'v1', 'created': 'Wed, 20 Jul 2022 19:51:39 GMT'}] | 2023-02-22 |
Ramya Gurunathan and Kamal Choudhary and Francesca Tavazza | Rapid Prediction of Phonon Structure and Properties using an Atomistic
Line Graph Neural Network (ALIGNN) | null | null | null | cond-mat.mtrl-sci | The phonon density-of-states (DOS) summarizes the lattice vibrational modes
supported by a structure, and gives access to rich information about the
material's stability, thermodynamic constants, and thermal transport
coefficients. Here, we present an atomistic line graph neural network (ALIGNN)
model for the predict... | [{'version': 'v1', 'created': 'Mon, 25 Jul 2022 20:13:49 GMT'}] | 2022-07-27 |
Andy Bridger, William I. F. David, Thomas J. Wood, Mohsen Danaie,
Keith T. Butler | Versatile Domain Mapping Of Scanning Electron Nanobeam Diffraction
Datasets Utilising Variational AutoEncoders and Decoder-Assisted Latent-Space
Clustering | null | null | null | cond-mat.mtrl-sci | Advancements in fast electron detectors have enabled the statistically
significant sampling of crystal structures on the nanometre scale by means of
Scanning Electron Nanobeam Diffraction (SEND). Characterisation of structural
similarity across this length scale is key to bridging the gap between local
atomic structu... | [{'version': 'v1', 'created': 'Wed, 27 Jul 2022 09:16:01 GMT'}] | 2022-07-28 |
\c{S}ener \"Oz\"onder and H. K\"ubra K\"u\c{c}\"ukkartal | Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian
Optimization with Neural Network Kernel | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn physics.comp-ph stat.ML | Density functional theory (DFT) is a powerful computational method used to
obtain physical and chemical properties of materials. In the materials
discovery framework, it is often necessary to virtually screen a large and
high-dimensional chemical space to find materials with desired properties.
However, grid searchin... | [{'version': 'v1', 'created': 'Tue, 16 Aug 2022 09:02:16 GMT'}, {'version': 'v2', 'created': 'Sat, 3 Aug 2024 20:39:34 GMT'}] | 2024-08-06 |
Kyeongpung Lee, Yutack Park, and Seungwu Han | $\textit{Ab initio}$ construction of full phase diagram of MgO-CaO
eutectic system using neural network interatomic potentials | null | null | null | physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci | While several studies confirmed that machine-learned potentials (MLPs) can
provide accurate free energies for determining phase stabilities, the abilities
of MLPs for efficiently constructing a full phase diagram of multi-component
systems are yet to be established. In this work, by employing neural network
interatom... | [{'version': 'v1', 'created': 'Thu, 25 Aug 2022 04:25:46 GMT'}] | 2022-08-26 |
Zeyu Liu, Meng Jiang, Tengfei Luo | Leveraging Low-Fidelity Data to Improve Machine Learning of Sparse
High-Fidelity Thermal Conductivity Data via Transfer Learning | null | null | null | cond-mat.mtrl-sci physics.app-ph | Lattice thermal conductivity (TC) of semiconductors is crucial for various
applications, ranging from microelectronics to thermoelectrics. Data-driven
approach can potentially establish the critical composition-property
relationship needed for fast screening of candidates with desirable TC, but the
small number of av... | [{'version': 'v1', 'created': 'Sun, 28 Aug 2022 21:45:54 GMT'}] | 2022-08-30 |
Mohammad S. Khorrami, Jaber R. Mianroodi, Nima H. Siboni, Pawan Goyal,
Bob Svendsen, Peter Benner, Dierk Raabe | An artificial neural network for surrogate modeling of stress fields in
viscoplastic polycrystalline materials | null | null | null | cond-mat.mtrl-sci | The purpose of this work is the development of an artificial neural network
(ANN) for surrogate modeling of the mechanical response of viscoplastic grain
microstructures. To this end, a U-Net-based convolutional neural network (CNN)
is trained to account for the history dependence of the material behavior. The
traini... | [{'version': 'v1', 'created': 'Mon, 29 Aug 2022 10:47:55 GMT'}] | 2022-08-30 |
Roberto Perera and Vinamra Agrawal | Dynamic and adaptive mesh-based graph neural network framework for
simulating displacement and crack fields in phase field models | null | null | null | cond-mat.mtrl-sci | Fracture is one of the main causes of failure in engineering structures.
Phase field methods coupled with adaptive mesh refinement (AMR) techniques have
been widely used to model crack propagation due to their ease of implementation
and scalability. However, phase field methods can still be computationally
demanding ... | [{'version': 'v1', 'created': 'Tue, 30 Aug 2022 16:12:16 GMT'}, {'version': 'v2', 'created': 'Thu, 1 Sep 2022 14:00:43 GMT'}, {'version': 'v3', 'created': 'Tue, 11 Jul 2023 16:54:12 GMT'}] | 2023-07-12 |
Kamal Choudhary, Brian DeCost, Lily Major, Keith Butler, Jeyan
Thiyagalingam, Francesca Tavazza | Unified Graph Neural Network Force-field for the Periodic Table | null | 10.1039/D2DD00096B | null | cond-mat.mtrl-sci | Classical force fields (FF) based on machine learning (ML) methods show great
potential for large scale simulations of materials. MLFFs have hitherto largely
been designed and fitted for specific systems and are not usually transferable
to chemistries beyond the specific training set. We develop a unified
atomisitic ... | [{'version': 'v1', 'created': 'Mon, 12 Sep 2022 19:13:27 GMT'}, {'version': 'v2', 'created': 'Fri, 16 Sep 2022 11:21:11 GMT'}] | 2023-03-06 |
Minyi Dai, Mehmet F. Demirel, Xuanhan Liu, Yingyu Liang, Jia-Mian Hu | Graph Neural Network for Predicting the Effective Properties of
Polycrystalline Materials: A Comprehensive Analysis | null | null | null | cond-mat.mtrl-sci | We develop a polycrystal graph neural network (PGNN) model for predicting the
effective properties of polycrystalline materials, using the Li7La3Zr2O12
ceramic as an example. A large-scale dataset with >5000 different
three-dimensional polycrystalline microstructures of finite-width grain
boundary is generated by Vor... | [{'version': 'v1', 'created': 'Mon, 12 Sep 2022 20:12:19 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2023 05:39:03 GMT'}] | 2023-06-09 |
Paul Laiu, Ying Yang, Massimiliano Lupo Pasini, Jong Youl Choi,
Dongwon Shin | A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid
Solutions | null | null | null | cond-mat.mtrl-sci | We present a data-centric deep learning (DL) approach using neural networks
(NNs) to predict the thermodynamics of ternary solid solutions. We explore how
NNs can be trained with a dataset of Gibbs free energies computed from a
CALPHAD database to predict ternary systems as a function of composition and
temperature. ... | [{'version': 'v1', 'created': 'Mon, 12 Sep 2022 20:59:10 GMT'}] | 2022-09-14 |
Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu | Probabilistic Generative Transformer Language models for Generative
Design of Molecules | null | null | null | cond-mat.mtrl-sci cs.LG physics.chem-ph | Self-supervised neural language models have recently found wide applications
in generative design of organic molecules and protein sequences as well as
representation learning for downstream structure classification and functional
prediction. However, most of the existing deep learning models for molecule
design usua... | [{'version': 'v1', 'created': 'Tue, 20 Sep 2022 01:51:57 GMT'}] | 2022-09-21 |
Jaber R. Mianroodi, Nima H. Siboni, Dierk Raabe | Computational Discovery of Energy-Efficient Heat Treatment for
Microstructure Design using Deep Reinforcement Learning | null | null | null | cond-mat.mtrl-sci cs.LG | Deep Reinforcement Learning (DRL) is employed to develop autonomously
optimized and custom-designed heat-treatment processes that are both,
microstructure-sensitive and energy efficient. Different from conventional
supervised machine learning, DRL does not rely on static neural network
training from data alone, but a... | [{'version': 'v1', 'created': 'Thu, 22 Sep 2022 18:07:16 GMT'}] | 2022-09-26 |
Christopher Kuenneth and Rampi Ramprasad | polyBERT: A chemical language model to enable fully machine-driven
ultrafast polymer informatics | null | 10.1038/s41467-023-39868-6 | null | cond-mat.mtrl-sci cs.AI cs.LG | Polymers are a vital part of everyday life. Their chemical universe is so
large that it presents unprecedented opportunities as well as significant
challenges to identify suitable application-specific candidates. We present a
complete end-to-end machine-driven polymer informatics pipeline that can search
this space f... | [{'version': 'v1', 'created': 'Thu, 29 Sep 2022 14:09:54 GMT'}] | 2023-07-20 |
Animesh Ghose, Mikhail Segal, Fanchen Meng, Zhu Liang, Mark S.
Hybertsen, Xiaohui Qu, Eli Stavitski, Shinjae Yoo, Deyu Lu, Matthew R.
Carbone | Uncertainty-aware predictions of molecular X-ray absorption spectra
using neural network ensembles | null | null | null | cond-mat.mtrl-sci | As machine learning (ML) methods continue to be applied to a broad scope of
problems in the physical sciences, uncertainty quantification is becoming
correspondingly more important for their robust application. Uncertainty aware
machine learning methods have been used in select applications, but largely for
scalar pr... | [{'version': 'v1', 'created': 'Sat, 1 Oct 2022 18:11:26 GMT'}, {'version': 'v2', 'created': 'Fri, 28 Oct 2022 19:17:51 GMT'}, {'version': 'v3', 'created': 'Sun, 22 Jan 2023 02:54:31 GMT'}] | 2023-01-24 |
Abdourahman Khaireh-Walieh, Alexandre Arnoult, S\'ebastien Plissard,
Peter R. Wiecha | Monitoring MBE substrate deoxidation via RHEED image-sequence analysis
by deep learning | Crystal Growth and Design, 23(2) 892-898 (2023) | 10.1021/acs.cgd.2c01132 | null | cond-mat.mes-hall cond-mat.mtrl-sci cs.LG | Reflection high-energy electron diffraction (RHEED) is a powerful tool in
molecular beam epitaxy (MBE), but RHEED images are often difficult to
interpret, requiring experienced operators. We present an approach for
automated surveillance of GaAs substrate deoxidation in MBE reactors using deep
learning based RHEED im... | [{'version': 'v1', 'created': 'Fri, 7 Oct 2022 10:01:06 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Dec 2022 14:14:55 GMT'}] | 2023-06-09 |
Himanshu and Tarak K Patra | When does deep learning fail and how to tackle it? A critical analysis
on polymer sequence-property surrogate models | null | null | null | cond-mat.mtrl-sci cs.LG | Deep learning models are gaining popularity and potency in predicting polymer
properties. These models can be built using pre-existing data and are useful
for the rapid prediction of polymer properties. However, the performance of a
deep learning model is intricately connected to its topology and the volume of
traini... | [{'version': 'v1', 'created': 'Wed, 12 Oct 2022 23:04:10 GMT'}] | 2022-10-14 |
Shuyan Zhang, Jie Gong, Sharon Chu, Daniel Xiao, B. Reeja Jayan, Alan
J. H. McGaughey | Pair distribution function analysis for oxide defect identification
through feature extraction and supervised learning | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Feature extraction and a neural network model are applied to predict the
defect types and concentrations in experimental TiO$_2$ samples. A dataset of
TiO$_2$ structures with vacancies and interstitials of oxygen and titanium is
built and the structures are relaxed using energy minimization. The features of
the calcu... | [{'version': 'v1', 'created': 'Thu, 13 Oct 2022 21:34:56 GMT'}] | 2022-10-17 |
Ivan Novikov, Olga Kovalyova, Alexander Shapeev and Max Hodapp | AI-accelerated Materials Informatics Method for the Discovery of Ductile
Alloys | Journal of Materials Research (2022) | 10.1557/s43578-022-00783-z | null | cond-mat.mtrl-sci | In computational materials science, a common means for predicting macroscopic
(e.g., mechanical) properties of an alloy is to define a model using
combinations of descriptors that depend on some material properties (elastic
constants, misfit volumes, etc.), representative for the macroscopic behavior.
The material pr... | [{'version': 'v1', 'created': 'Fri, 14 Oct 2022 10:12:24 GMT'}] | 2022-10-17 |
Dongchen Huang, Junde Liu, Tian Qian, and Yi-feng Yang | Spectroscopic data de-noising via training-set-free deep learning method | Sci. China-Phys. Mech. Astron. 66, 267011 (2023) | 10.1007/s11433-022-2075-x | null | cond-mat.mtrl-sci cs.LG physics.data-an | De-noising plays a crucial role in the post-processing of spectra. Machine
learning-based methods show good performance in extracting intrinsic
information from noisy data, but often require a high-quality training set that
is typically inaccessible in real experimental measurements. Here, using
spectra in angle-reso... | [{'version': 'v1', 'created': 'Wed, 19 Oct 2022 12:04:35 GMT'}, {'version': 'v2', 'created': 'Mon, 15 May 2023 12:07:55 GMT'}] | 2023-05-16 |
Suresh Bishnoi, Skyler Badge, Jayadeva and N. M. Anoop Krishnan | Predicting Oxide Glass Properties with Low Complexity Neural Network and
Physical and Chemical Descriptors | null | 10.1016/j.jnoncrysol.2023.122488 | null | cond-mat.mtrl-sci cs.LG | Due to their disordered structure, glasses present a unique challenge in
predicting the composition-property relationships. Recently, several attempts
have been made to predict the glass properties using machine learning
techniques. However, these techniques have the limitations, namely, (i)
predictions are limited t... | [{'version': 'v1', 'created': 'Wed, 19 Oct 2022 12:23:30 GMT'}] | 2023-08-09 |
Elton Pan, Christopher Karpovich and Elsa Olivetti | Deep Reinforcement Learning for Inverse Inorganic Materials Design | null | null | null | cond-mat.mtrl-sci cs.LG | A major obstacle to the realization of novel inorganic materials with
desirable properties is the inability to perform efficient optimization across
both materials properties and synthesis of those materials. In this work, we
propose a reinforcement learning (RL) approach to inverse inorganic materials
design, which ... | [{'version': 'v1', 'created': 'Fri, 21 Oct 2022 13:06:19 GMT'}] | 2022-10-24 |
Xiaoxun Gong, He Li, Nianlong Zou, Runzhang Xu, Wenhui Duan, Yong Xu | General framework for E(3)-equivariant neural network representation of
density functional theory Hamiltonian | Nat. Commun. 14, 2848 (2023) | 10.1038/s41467-023-38468-8 | null | physics.comp-ph cond-mat.mtrl-sci | Combination of deep learning and ab initio calculation has shown great
promise in revolutionizing future scientific research, but how to design neural
network models incorporating a priori knowledge and symmetry requirements is a
key challenging subject. Here we propose an E(3)-equivariant deep-learning
framework to ... | [{'version': 'v1', 'created': 'Tue, 25 Oct 2022 12:16:26 GMT'}] | 2023-06-12 |
Santiago Miret, Kin Long Kelvin Lee, Carmelo Gonzales, Marcel Nassar,
Matthew Spellings | The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in
Materials Science | Transactions on Machine Learning Research (2023) | null | 2835-8856 | cs.LG cond-mat.mtrl-sci cs.AI | We present the Open MatSci ML Toolkit: a flexible, self-contained, and
scalable Python-based framework to apply deep learning models and methods on
scientific data with a specific focus on materials science and the OpenCatalyst
Dataset. Our toolkit provides: 1. A scalable machine learning workflow for
materials scien... | [{'version': 'v1', 'created': 'Mon, 31 Oct 2022 17:11:36 GMT'}] | 2023-09-01 |
Biswadev Roy, A. Karoui, B. Vlahovic, and M.H. Wu | Supervised learning applied to high-dimensional millimeter wave
transient absorption data for age prediction of perovskite thin-film | null | null | null | cond-mat.mtrl-sci physics.data-an | We have analyzed a limited sample set of 120 GHz, and 150 GHz time-resolved
millimeter wave (mmW) photoconductive decay (mmPCD) signals of 300 nm thick
air-stable encapsulated perovskite film (methyl-ammonium lead halide) excited
using a pulsed 532-nm laser with fluence 10.6 micro-Joules per cm-2. We
correlated 12 pa... | [{'version': 'v1', 'created': 'Fri, 4 Nov 2022 13:15:55 GMT'}] | 2022-11-07 |
Phong C.H. Nguyen, Yen-Thi Nguyen, Pradeep K. Seshadri, Joseph B.
Choi, H.S. Udaykumar, and Stephen Baek | A physics-aware deep learning model for energy localization in
multiscale shock-to-detonation simulations of heterogeneous energetic
materials | Pyrotech. 2023, e202200268 | 10.1002/prep.202200268 | null | cond-mat.mtrl-sci cs.LG | Predictive simulations of the shock-to-detonation transition (SDT) in
heterogeneous energetic materials (EM) are vital to the design and control of
their energy release and sensitivity. Due to the complexity of the
thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid
mesoscale energy localiza... | [{'version': 'v1', 'created': 'Tue, 8 Nov 2022 21:16:00 GMT'}, {'version': 'v2', 'created': 'Wed, 22 Mar 2023 01:53:41 GMT'}] | 2023-05-12 |
Marios Mattheakis, Gabriel R. Schleder, Daniel T. Larson, Efthimios
Kaxiras | First principles physics-informed neural network for quantum
wavefunctions and eigenvalue surfaces | null | null | null | cs.LG cond-mat.mtrl-sci physics.comp-ph | Physics-informed neural networks have been widely applied to learn general
parametric solutions of differential equations. Here, we propose a neural
network to discover parametric eigenvalue and eigenfunction surfaces of quantum
systems. We apply our method to solve the hydrogen molecular ion. This is an
ab-initio de... | [{'version': 'v1', 'created': 'Tue, 8 Nov 2022 23:22:42 GMT'}, {'version': 'v2', 'created': 'Sun, 13 Nov 2022 19:42:06 GMT'}, {'version': 'v3', 'created': 'Sun, 20 Nov 2022 01:41:11 GMT'}] | 2022-11-22 |
Yu-Chuan Hsu, Markus J. Buehler | DyFraNet: Forecasting and Backcasting Dynamic Fracture Mechanics in
Space and Time Using a 2D-to-3D Deep Neural Network | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall | The dynamics of materials failure is one of the most critical phenomena in a
range of scientific and engineering fields, from healthcare to structural
materials to transportation. In this paper we propose a specially designed deep
neural network, DyFraNet, which can predict dynamic fracture behaviors by
identifying a... | [{'version': 'v1', 'created': 'Tue, 15 Nov 2022 20:19:32 GMT'}] | 2022-11-17 |
Albert Zhu, Simon Batzner, Albert Musaelian, Boris Kozinsky | Fast Uncertainty Estimates in Deep Learning Interatomic Potentials | null | 10.1063/5.0136574 | null | physics.comp-ph cond-mat.mtrl-sci cs.LG physics.chem-ph | Deep learning has emerged as a promising paradigm to give access to highly
accurate predictions of molecular and materials properties. A common
short-coming shared by current approaches, however, is that neural networks
only give point estimates of their predictions and do not come with predictive
uncertainties assoc... | [{'version': 'v1', 'created': 'Thu, 17 Nov 2022 20:13:39 GMT'}] | 2023-05-10 |
Hongyu Yu, Boyu Liu, Yang Zhong, Liangliang Hong, Junyi Ji, Changsong
Xu, Xingao Gong, Hongjun Xiang | General time-reversal equivariant neural network potential for magnetic
materials | Physical Review B 2024 | 10.1103/PhysRevB.110.104427 | Phys. Rev. B 110,104427 | cond-mat.mtrl-sci cs.LG physics.comp-ph | This study introduces time-reversal E(3)-equivariant neural network and
SpinGNN++ framework for constructing a comprehensive interatomic potential for
magnetic systems, encompassing spin-orbit coupling and noncollinear magnetic
moments. SpinGNN++ integrates multitask spin equivariant neural network with
explicit spin... | [{'version': 'v1', 'created': 'Mon, 21 Nov 2022 12:25:58 GMT'}, {'version': 'v2', 'created': 'Mon, 19 Dec 2022 07:20:51 GMT'}, {'version': 'v3', 'created': 'Mon, 8 Jan 2024 12:45:12 GMT'}] | 2025-03-14 |
Xiangrui Yang | Leveraging Orbital Information and Atomic Feature in Deep Learning Model | null | null | null | cond-mat.mtrl-sci cs.AI cs.LG | Predicting material properties base on micro structure of materials has long
been a challenging problem. Recently many deep learning methods have been
developed for material property prediction. In this study, we propose a crystal
representation learning framework, Orbital CrystalNet, OCrystalNet, which
consists of t... | [{'version': 'v1', 'created': 'Sat, 29 Oct 2022 06:22:29 GMT'}] | 2022-11-22 |
Roberto Perera, Vinamra Agrawal | A generalized machine learning framework for brittle crack problems
using transfer learning and graph neural networks | null | null | null | cond-mat.mtrl-sci cs.LG | Despite their recent success, machine learning (ML) models such as graph
neural networks (GNNs), suffer from drawbacks such as the need for large
training datasets and poor performance for unseen cases. In this work, we use
transfer learning (TL) approaches to circumvent the need for retraining with
large datasets. W... | [{'version': 'v1', 'created': 'Tue, 22 Nov 2022 18:16:16 GMT'}] | 2022-11-23 |
Hongyu Yu, Liangliang Hong, Shiyou Chen, Xingao Gong, Hongjun Xiang | Capturing long-range interaction with reciprocal space neural network | null | null | null | cond-mat.mtrl-sci cs.LG physics.chem-ph physics.comp-ph | Machine Learning (ML) interatomic models and potentials have been widely
employed in simulations of materials. Long-range interactions often dominate in
some ionic systems whose dynamics behavior is significantly influenced.
However, the long-range effect such as Coulomb and Van der Wales potential is
not considered ... | [{'version': 'v1', 'created': 'Wed, 30 Nov 2022 02:10:48 GMT'}] | 2022-12-01 |
Andrew J. Lew, Kai Jin, Markus J. Buehler | Architected Materials for Mechanical Compression: Design via Simulation,
Deep Learning, and Experimentation | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall | Architected materials can achieve enhanced properties compared to their plain
counterparts. Specific architecting serves as a powerful design lever to
achieve targeted behavior without changing the base material. Thus, the
connection between architected structure and resultant properties remains an
open field of grea... | [{'version': 'v1', 'created': 'Mon, 5 Dec 2022 22:58:01 GMT'}, {'version': 'v2', 'created': 'Mon, 13 Feb 2023 13:07:25 GMT'}] | 2023-02-14 |
James P. Horwath, Xiao-Min Lin, Hongrui He, Qingteng Zhang, Eric M.
Dufresne, Miaoqi Chu, Subramanian K. R. S. Sankaranarayanan, Wei Chen, Suresh
Narayanan, Mathew J. Cherukara | Elucidation of Relaxation Dynamics Beyond Equilibrium Through
AI-informed X-ray Photon Correlation Spectroscopy | null | null | null | cond-mat.mtrl-sci cond-mat.mes-hall | Understanding and interpreting dynamics of functional materials \textit{in
situ} is a grand challenge in physics and materials science due to the
difficulty of experimentally probing materials at varied length and time
scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited
for characterizing mat... | [{'version': 'v1', 'created': 'Wed, 7 Dec 2022 22:36:53 GMT'}] | 2022-12-14 |
Luis M. Antunes, Keith T. Butler, Ricardo Grau-Crespo | Predicting Thermoelectric Transport Properties from Composition with
Attention-based Deep Learning | null | null | null | cond-mat.mtrl-sci | Thermoelectric materials can be used to construct devices which recycle waste
heat into electricity. However, the best known thermoelectrics are based on
rare, expensive or even toxic elements, which limits their widespread adoption.
To enable deployment on global scales, new classes of effective thermoelectrics
are ... | [{'version': 'v1', 'created': 'Tue, 13 Dec 2022 09:26:24 GMT'}] | 2022-12-14 |
Hyun Park, Ruijie Zhu, E. A. Huerta, Santanu Chaudhuri, Emad
Tajkhorshid, Donny Cooper | End-to-end AI framework for interpretable prediction of molecular and
crystal properties | Mach. Learn.: Sci. Technol. 4 (2023) 025036 | 10.1088/2632-2153/acd434 | null | cond-mat.mtrl-sci cs.AI cs.LG | We introduce an end-to-end computational framework that allows for
hyperparameter optimization using the DeepHyper library, accelerated model
training, and interpretable AI inference. The framework is based on
state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN,
MPNN-transformer, and TorchMD-NET. We emp... | [{'version': 'v1', 'created': 'Wed, 21 Dec 2022 19:27:51 GMT'}, {'version': 'v2', 'created': 'Mon, 14 Aug 2023 22:45:57 GMT'}] | 2023-08-16 |
Ashwini Gupta, Anindya Bhaduri, Lori Graham-Brady | Accelerated multiscale mechanics modeling in a deep learning framework | null | 10.1016/j.mechmat.2023.104709 | null | cond-mat.mtrl-sci | Microstructural heterogeneity affects the macro-scale behavior of materials.
Conversely, load distribution at the macro-scale changes the microstructural
response. These up-scaling and down-scaling relations are often modeled using
multiscale finite element (FE) approaches such as FE-squared ($FE^2$). However,
$FE^2$... | [{'version': 'v1', 'created': 'Fri, 30 Dec 2022 08:57:43 GMT'}] | 2023-06-13 |
Pan Zhang, Cheng Shang, Zhipan Liu, Ji-Hui Yang, Xin-Gao Gong | Origin of performance degradation in high-delithiation Li$_x$CoO$_2$:
insights from direct atomic simulations using global neural network
potentials | null | null | null | physics.comp-ph cond-mat.mtrl-sci | Li$_x$CoO$_2$ based batteries have serious capacity degradation and safety
issues when cycling at high-delithiation states but full and consistent
mechanisms are still poorly understood. Herein, a global neural network
potential (GNNP) is developed to provide direct theoretical understandings by
performing long-time ... | [{'version': 'v1', 'created': 'Fri, 30 Dec 2022 12:59:02 GMT'}] | 2023-01-02 |
Armand Barbot, Riccardo Gatti | Unsupervised learning for structure detection in plastically deformed
crystals | Computational Materials Science, 2023, 230, pp.112459 | 10.1016/j.commatsci.2023.112459 | null | cond-mat.mtrl-sci cs.LG | Detecting structures at the particle scale within plastically deformed
crystalline materials allows a better understanding of the occurring phenomena.
While previous approaches mostly relied on applying hand-chosen criteria on
different local parameters, these approaches could only detect already known
structures.We ... | [{'version': 'v1', 'created': 'Thu, 22 Dec 2022 14:17:32 GMT'}, {'version': 'v2', 'created': 'Tue, 14 May 2024 07:12:35 GMT'}] | 2024-05-15 |
M. Pietrow, A. Miaskowski | Artificial neural network as an effective tool to calculate parameters
of positron annihilation lifetime spectra | J. Appl. Phys. 134, 114902 (2023) | 10.1063/5.0155987 | null | physics.atom-ph cond-mat.mtrl-sci physics.chem-ph physics.data-an | The paper presents the application of the multi-layer perceptron regressor
model for predicting the parameters of positron annihilation lifetime spectra
using the example of alkanes in the solid phase. A good agreement of
calculation results was found when comparing with the commonly used methods.
The presented metho... | [{'version': 'v1', 'created': 'Wed, 4 Jan 2023 10:18:58 GMT'}] | 2023-09-20 |
Fatemeh Hafezianzade, Morad Biagooi, SeyedEhsan Nedaaee Oskoee | Physics informed neural network for charged particles surrounded by
conductive boundaries | null | null | null | physics.comp-ph cond-mat.mtrl-sci math-ph math.MP | In this paper, we developed a new PINN-based model to predict the potential
of point-charged particles surrounded by conductive walls. As a result of the
proposed physics-informed neural network model, the mean square error and R2
score are less than 7% and more than 90% for the corresponding example
simulation, resp... | [{'version': 'v1', 'created': 'Thu, 5 Jan 2023 17:52:36 GMT'}] | 2023-01-06 |
Saugat Kandel, Tao Zhou, Anakha V Babu, Zichao Di, Xinxin Li, Xuedan
Ma, Martin Holt, Antonino Miceli, Charudatta Phatak, and Mathew Cherukara | Demonstration of an AI-driven workflow for autonomous high-resolution
scanning microscopy | null | null | null | physics.app-ph cond-mat.mtrl-sci physics.ins-det | With the continuing advances in scientific instrumentation, scanning
microscopes are now able to image physical systems with up to sub-atomic-level
spatial resolutions and sub-picosecond time resolutions. Commensurately, they
are generating ever-increasing volumes of data, storing and analysis of which
is becoming an... | [{'version': 'v1', 'created': 'Thu, 12 Jan 2023 20:40:43 GMT'}] | 2023-01-16 |
Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane, Jianjun Hu | Discovery of 2D materials using Transformer Network based Generative
Design | null | null | null | cond-mat.mtrl-sci cs.LG | Two-dimensional (2D) materials have wide applications in superconductors,
quantum, and topological materials. However, their rational design is not well
established, and currently less than 6,000 experimentally synthesized 2D
materials have been reported. Recently, deep learning, data-mining, and density
functional t... | [{'version': 'v1', 'created': 'Sat, 14 Jan 2023 05:59:38 GMT'}] | 2023-01-18 |
Xiongzhi Zeng, Yi Fan, Jie Liu, Zhenyu Li, Jinlong Yang | Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient
Quantum Simulation of Chemical Systems | null | null | null | quant-ph cond-mat.mtrl-sci physics.chem-ph | The variational quantum eigensolver is a promising way to solve the
Schr\"odinger equation on a noisy intermediate-scale quantum (NISQ) computer,
while its success relies on a well-designed wavefunction ansatz. Compared to
physically motivated ansatzes, hardware heuristic ansatzes usually lead to a
shallower circuit,... | [{'version': 'v1', 'created': 'Wed, 18 Jan 2023 14:00:26 GMT'}, {'version': 'v2', 'created': 'Thu, 19 Jan 2023 11:42:14 GMT'}] | 2023-01-20 |
Francesco Guidarelli Mattioli, Francesco Sciortino, John Russo | A neural network potential with self-trained atomic fingerprints: a test
with the mW water potential | null | 10.1063/5.0139245 | null | cond-mat.soft cond-mat.dis-nn cond-mat.mtrl-sci | We present a neural network (NN) potential based on a new set of atomic
fingerprints built upon two- and three-body contributions that probe distances
and local orientational order respectively. Compared to existing NN potentials,
the atomic fingerprints depend on a small set of tuneable parameters which are
trained ... | [{'version': 'v1', 'created': 'Fri, 27 Jan 2023 09:28:08 GMT'}] | 2023-03-22 |
Soumya Sanyal, Arun Kumar Sagotra, Narendra Kumar, Sharad Rathi,
Mohana Krishna, Nagesh Somayajula, Duraivelan Palanisamy, Ram R. Ratnakar,
Suchismita Sanyal, Partha Talukdar, Umesh Waghmare and Janakiraman
Balachandran | Potential energy surface prediction of Alumina polymorphs using graph
neural network | null | null | null | cond-mat.mtrl-sci | The process of design and discovery of new materials can be significantly
expedited and simplified if we can learn effectively from available data. Deep
learning (DL) approaches have recently received a lot of interest for their
ability to speed up the design of novel materials by predicting material
properties with ... | [{'version': 'v1', 'created': 'Sat, 28 Jan 2023 02:27:47 GMT'}] | 2023-01-31 |
Bulat N. Galimzyanov, Maria A. Doronina, Anatolii V. Mokshin | Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an
Artificial Neural Network | Materials 2023, 16(3), 1127 | 10.3390/ma16031127 | null | cond-mat.mtrl-sci | The Arrhenius crossover temperature, $T_{A}$, corresponds to a thermodynamic
state wherein the atomistic dynamics of a liquid becomes heterogeneous and
cooperative; and the activation barrier of diffusion dynamics becomes
temperature-dependent at temperatures below $T_{A}$. The theoretical estimation
of this temperat... | [{'version': 'v1', 'created': 'Sat, 28 Jan 2023 18:11:55 GMT'}] | 2023-01-31 |
Fabrice Roncoroni, Ana Sanz-Matias, Siddharth Sundararaman, David
Prendergast | Unsupervised learning of representative local atomic arrangements in
molecular dynamics data | null | 10.1039/D3CP00525A | null | cond-mat.mtrl-sci physics.chem-ph physics.comp-ph | Molecular dynamics (MD) simulations present a data-mining challenge, given
that they can generate a considerable amount of data but often rely on limited
or biased human interpretation to examine their information content. By not
asking the right questions of MD data we may miss critical information hidden
within it.... | [{'version': 'v1', 'created': 'Thu, 2 Feb 2023 23:37:19 GMT'}] | 2023-05-09 |
Chuannan Li, Hanpu Liang, Xie Zhang, Zijing Lin, Su-Huai Wei | Graph deep learning accelerated efficient crystal structure search and
feature extraction | null | null | null | cond-mat.mtrl-sci | Structural search and feature extraction are a central subject in modern
materials design, the efficiency of which is currently limited, but can be
potentially boosted by machine learning (ML). Here, we develop an ML-based
prediction-analysis framework, which includes a symmetry-based combinatorial
crystal optimizati... | [{'version': 'v1', 'created': 'Tue, 7 Feb 2023 09:14:52 GMT'}] | 2023-02-08 |
Brenden W. Hamilton, Pilsun Yoo, Michael N. Sakano, Md Mahbubul Islam,
Alejandro Strachan | High Pressure and Temperature Neural Network Reactive Force Field for
Energetic Materials | null | 10.1063/5.0146055 | null | cond-mat.mtrl-sci physics.chem-ph | Reactive force fields for molecular dynamics have enabled a wide range of
studies in numerous material classes. These force fields are computationally
inexpensive as compared to electronic structure calculations and allow for
simulations of millions of atoms. However, the accuracy of traditional force
fields is limit... | [{'version': 'v1', 'created': 'Thu, 9 Feb 2023 19:26:33 GMT'}] | 2023-04-26 |
Zechen Tang, He Li, Peize Lin, Xiaoxun Gong, Gan Jin, Lixin He, Hong
Jiang, Xinguo Ren, Wenhui Duan, Yong Xu | Efficient hybrid density functional calculation by deep learning | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Hybrid density functional calculation is indispensable to accurate
description of electronic structure, whereas the formidable computational cost
restricts its broad application. Here we develop a deep equivariant neural
network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian
from self-consiste... | [{'version': 'v1', 'created': 'Thu, 16 Feb 2023 11:08:35 GMT'}] | 2023-02-17 |
Sayani Majumdar and Ioannis Zeimpekis | Back-end and Flexible Substrate Compatible Analog Ferroelectric Field
Effect Transistors for Accurate Online Training in Deep Neural Network
Accelerators | null | null | null | cond-mat.mtrl-sci physics.app-ph | Online training of deep neural networks (DNN) can be significantly
accelerated by performing in-situ vector matrix multiplication in a crossbar
array of analog memories. However, training accuracies often suffer due to
device non-idealities such as nonlinearity, asymmetry, limited bit precision
and dynamic weight upd... | [{'version': 'v1', 'created': 'Thu, 23 Feb 2023 13:45:14 GMT'}] | 2023-02-24 |
Matthew Helmi Leth Larsen (1), William Bang Lomholdt (2), Cuauhtemoc
Nu\~nez Valencia (1), Thomas W. Hansen (2), Jakob Schi{\o}tz (1) ((1)
Department of Physics Technical University of Denmark, (2) National Center
for Nano Fabrication and Characterization Technical University of Denmark) | Quantifying Noise Limitations of Neural Network Segmentations in
High-Resolution Transmission Electron Microscopy | Ultramicroscopy 253, (2023) 113803 | 10.1016/j.ultramic.2023.113803 | null | cond-mat.mtrl-sci | Motivated by the need for low electron dose transmission electron microscopy
imaging, we report the optimal frame dose (i.e. $e^-/A^{2}$) range for object
detection and segmentation tasks with neural networks. The MSD-net architecture
shows promising abilities over the industry standard U-net architecture in
generali... | [{'version': 'v1', 'created': 'Fri, 24 Feb 2023 13:46:05 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2023 14:32:24 GMT'}] | 2023-09-26 |
Bowen Deng, Peichen Zhong, KyuJung Jun, Janosh Riebesell, Kevin Han,
Christopher J. Bartel, Gerbrand Ceder | CHGNet: Pretrained universal neural network potential for
charge-informed atomistic modeling | null | null | null | cond-mat.mtrl-sci cs.LG | The simulation of large-scale systems with complex electron interactions
remains one of the greatest challenges for the atomistic modeling of materials.
Although classical force fields often fail to describe the coupling between
electronic states and ionic rearrangements, the more accurate
\textit{ab-initio} molecula... | [{'version': 'v1', 'created': 'Tue, 28 Feb 2023 01:30:06 GMT'}, {'version': 'v2', 'created': 'Tue, 20 Jun 2023 21:27:36 GMT'}] | 2023-06-22 |
Qichen Xu, I. P. Miranda, Manuel Pereiro, Filipp N. Rybakov, Danny
Thonig, Erik Sj\"oqvist, Pavel Bessarab, Anders Bergman, Olle Eriksson, Pawel
Herman, Anna Delin | Metaheuristic conditional neural network for harvesting skyrmionic
metastable states | null | 10.1103/PhysRevResearch.5.043199 | null | physics.comp-ph cond-mat.mtrl-sci cs.AI cs.LG | We present a metaheuristic conditional neural-network-based method aimed at
identifying physically interesting metastable states in a potential energy
surface of high rugosity. To demonstrate how this method works, we identify and
analyze spin textures with topological charge $Q$ ranging from 1 to $-13$
(where antisk... | [{'version': 'v1', 'created': 'Mon, 6 Mar 2023 04:04:19 GMT'}, {'version': 'v2', 'created': 'Mon, 29 May 2023 14:13:15 GMT'}] | 2023-12-05 |
Noah Hoffmann (1), Jonathan Schmidt (2,1), Silvana Botti (2), Miguel
A. L. Marques (1) ((1) Institut f\"ur Physik, Martin-Luther-Universit\"at
Halle-Wittenberg, D-06099 Halle, Germany, (2) Institut f\"ur
Festk\"orpertheorie und -optik, Friedrich-Schiller-Universit\"at Jena,
Max-Wien-Platz 1, 07743 Jena, Germany... | Transfer learning on large datasets for the accurate prediction of
material properties | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn | Graph neural networks trained on large crystal structure databases are
extremely effective in replacing ab initio calculations in the discovery and
characterization of materials. However, crystal structure datasets comprising
millions of materials exist only for the Perdew-Burke-Ernzerhof (PBE)
functional. In this wo... | [{'version': 'v1', 'created': 'Mon, 6 Mar 2023 09:56:06 GMT'}] | 2023-03-07 |
Namkyeong Lee, Heewoong Noh, Sungwon Kim, Dongmin Hyun, Gyoung S. Na,
Chanyoung Park | Predicting Density of States via Multi-modal Transformer | null | null | null | cs.LG cond-mat.mtrl-sci physics.comp-ph | The density of states (DOS) is a spectral property of materials, which
provides fundamental insights on various characteristics of materials. In this
paper, we propose a model to predict the DOS by reflecting the nature of DOS:
DOS determines the general distribution of states as a function of energy.
Specifically, w... | [{'version': 'v1', 'created': 'Mon, 13 Mar 2023 10:57:35 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Apr 2023 04:16:58 GMT'}] | 2023-04-11 |
Michael Kilgour, Jutta Rogal, Mark Tuckerman | Geometric Deep Learning for Molecular Crystal Structure Prediction | null | 10.1021/acs.jctc.3c00031 | null | cond-mat.mtrl-sci cs.LG physics.chem-ph physics.comp-ph | We develop and test new machine learning strategies for accelerating
molecular crystal structure ranking and crystal property prediction using tools
from geometric deep learning on molecular graphs. Leveraging developments in
graph-based learning and the availability of large molecular crystal datasets,
we train mode... | [{'version': 'v1', 'created': 'Fri, 17 Mar 2023 17:27:47 GMT'}] | 2024-07-29 |
Vadim Korolev and Pavel Protsenko | Toward Accurate Interpretable Predictions of Materials Properties within
Transformer Language Models | null | 10.1016/j.patter.2023.100803 | null | cond-mat.mtrl-sci physics.comp-ph | Property prediction accuracy has long been a key parameter of machine
learning in materials informatics. Accordingly, advanced models showing
state-of-the-art performance turn into highly parameterized black boxes missing
interpretability. Here, we present an elegant way to make their reasoning
transparent. Human-rea... | [{'version': 'v1', 'created': 'Tue, 21 Mar 2023 20:33:12 GMT'}] | 2023-08-03 |
Daniel R. Cassar | GlassNet: a multitask deep neural network for predicting many glass
properties | Ceramics International 49 (2023) 36013-36024 | 10.1016/j.ceramint.2023.08.281 | null | cond-mat.soft cond-mat.mtrl-sci | A multitask deep neural network model was trained on more than 218k different
glass compositions. This model, called GlassNet, can predict 85 different
properties (such as optical, electrical, dielectric, mechanical, and thermal
properties, as well as density, viscosity/relaxation, crystallization, surface
tension, a... | [{'version': 'v1', 'created': 'Mon, 27 Mar 2023 18:36:41 GMT'}, {'version': 'v2', 'created': 'Fri, 25 Aug 2023 18:20:44 GMT'}, {'version': 'v3', 'created': 'Mon, 20 Nov 2023 18:55:16 GMT'}] | 2023-11-21 |
Shun Muroga, Yasuaki Miki, and Kenji Hata | A Comprehensive and Versatile Multimodal Deep Learning Approach for
Predicting Diverse Properties of Advanced Materials | Adv. Sci.,10, 24, 2302508 (2023) | 10.1002/advs.202302508 | null | cond-mat.soft cond-mat.mtrl-sci cs.AI cs.LG | We present a multimodal deep learning (MDL) framework for predicting physical
properties of a 10-dimensional acrylic polymer composite material by merging
physical attributes and chemical data. Our MDL model comprises four modules,
including three generative deep learning models for material structure
characterizatio... | [{'version': 'v1', 'created': 'Wed, 29 Mar 2023 02:42:17 GMT'}] | 2023-11-28 |
Qing-Jie Li, Mahmut Nedim Cinbiz, Yin Zhang, Qi He, Geoffrey
Beausoleil II, Ju Li | Robust Deep Learning Framework for Constitutive-Relation Modeling | null | null | null | cond-mat.mtrl-sci | Modeling the full-range deformation behaviors of materials under complex
loading and materials conditions is a significant challenge for constitutive
relations (CRs) modeling. We propose a general encoder-decoder deep learning
framework that can model high-dimensional stress-strain data and complex
loading histories ... | [{'version': 'v1', 'created': 'Sun, 2 Apr 2023 20:13:25 GMT'}] | 2023-04-04 |
Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben
Blaiszik, Ayana Ghosh, Maxim A. Ziatdinov, A. Al-Najjar, Christina Doty,
Sarah Akers, Nageswara S. Rao, Joshua C. Agar, Steven R. Spurgeon | Deep Learning for Automated Experimentation in Scanning Transmission
Electron Microscopy | null | 10.1038/s41524-023-01142-0 | null | cond-mat.mtrl-sci cs.LG | Machine learning (ML) has become critical for post-acquisition data analysis
in (scanning) transmission electron microscopy, (S)TEM, imaging and
spectroscopy. An emerging trend is the transition to real-time analysis and
closed-loop microscope operation. The effective use of ML in electron
microscopy now requires the... | [{'version': 'v1', 'created': 'Tue, 4 Apr 2023 18:01:56 GMT'}] | 2023-11-10 |
Bin Xing, Timothy J. Rupert, Xiaoqing Pan, Penghui Cao | Neural Network Kinetics for Exploring Diffusion Multiplicity and
Chemical Ordering in Compositionally Complex Materials | Nat Commun 15, 3879 (2024) | 10.1038/s41467-024-47927-9 | null | cond-mat.dis-nn cond-mat.mtrl-sci | Diffusion involving atom transport from one location to another governs many
important processes and behaviors such as precipitation and phase nucleation.
Local chemical complexity in compositionally complex alloys poses challenges
for modeling atomic diffusion and the resulting formation of chemically ordered
struct... | [{'version': 'v1', 'created': 'Thu, 6 Apr 2023 09:35:31 GMT'}, {'version': 'v2', 'created': 'Sat, 6 Apr 2024 22:02:02 GMT'}] | 2024-05-10 |
Peichen Zhong, Bowen Deng, Tanjin He, Zhengyan Lun, Gerbrand Ceder | Deep learning of experimental electrochemistry for battery cathodes
across diverse compositions | null | 10.1016/j.joule.2024.03.010 | null | cond-mat.mtrl-sci | Artificial intelligence (AI) has emerged as a tool for discovering and
optimizing novel battery materials. However, the adoption of AI in battery
cathode representation and discovery is still limited due to the complexity of
optimizing multiple performance properties and the scarcity of high-fidelity
data. In this st... | [{'version': 'v1', 'created': 'Tue, 11 Apr 2023 05:09:48 GMT'}, {'version': 'v2', 'created': 'Thu, 21 Sep 2023 07:01:47 GMT'}, {'version': 'v3', 'created': 'Tue, 2 Apr 2024 21:08:37 GMT'}] | 2024-05-14 |
Teng Yang, Zefeng Cai, Zhengtao Huang, Wenlong Tang, Ruosong Shi, Andy
Godfrey, Hanxing Liu, Yuanhua Lin, Ce-Wen Nan, Meng Ye, LinFeng Zhang, Han
Wang, Ben Xu | Deep Learning Illuminates Spin and Lattice Interaction in Magnetic
Materials | null | null | null | cond-mat.mtrl-sci | Atomistic simulations hold significant value in clarifying crucial phenomena
such as phase transitions and energy transport in materials science. Their
success stems from the presence of potential energy functions capable of
accurately depicting the relationship between system energy and lattice
changes. In magnetic ... | [{'version': 'v1', 'created': 'Wed, 19 Apr 2023 12:24:56 GMT'}, {'version': 'v2', 'created': 'Wed, 14 Jun 2023 02:38:48 GMT'}, {'version': 'v3', 'created': 'Fri, 18 Aug 2023 05:47:37 GMT'}] | 2023-08-21 |
Qidong Lin and Bin Jiang | First-Principles Modeling of Equilibration Dynamics of Hyperthermal
Products of Surface Reactions Using Scalable Neural Network Potential | null | null | null | cond-mat.mtrl-sci cond-mat.other | Equilibration dynamics of hot oxygen atoms following O2 dissociation on
Pd(100) and Pd(111) surfaces are investigated by molecular dynamics simulations
based on a scalable neural network potential enabling first-principles
description of O2 and O interacting with variable Pd supercells. We find that
to accurately des... | [{'version': 'v1', 'created': 'Fri, 21 Apr 2023 08:42:34 GMT'}] | 2023-04-24 |
Rachel K. Luu, Marcin Wysokowski, Markus J. Buehler | Generative Discovery of Novel Chemical Designs using Diffusion Modeling
and Transformer Deep Neural Networks with Application to Deep Eutectic
Solvents | null | 10.1063/5.0155890 | null | cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech | We report a series of deep learning models to solve complex forward and
inverse design problems in molecular modeling and design. Using both diffusion
models inspired by nonequilibrium thermodynamics and attention-based
transformer architectures, we demonstrate a flexible framework to capture
complex chemical structu... | [{'version': 'v1', 'created': 'Mon, 24 Apr 2023 19:17:38 GMT'}] | 2023-06-21 |
Junrong Lin, Mahmudul Hasan, Pinar Acar, Jose Blanchet and Vahid
Tarokh | Neural Network Accelerated Process Design of Polycrystalline
Microstructures | null | null | null | cs.CE cond-mat.mtrl-sci cs.LG | Computational experiments are exploited in finding a well-designed processing
path to optimize material structures for desired properties. This requires
understanding the interplay between the processing-(micro)structure-property
linkages using a multi-scale approach that connects the macro-scale (process
parameters)... | [{'version': 'v1', 'created': 'Tue, 11 Apr 2023 20:35:29 GMT'}, {'version': 'v2', 'created': 'Wed, 3 May 2023 04:07:49 GMT'}] | 2023-05-04 |
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