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 |
|---|---|---|---|---|---|---|---|---|
Tilman Ki{\ss}linger, Andreas Raabgrund, Begmuhammet Geldiyev,
Maximilian Ammon, Janek Rieger, Jonas Hauner, Lutz Hammer, Thomas Fauster,
and M. Alexander Schneider | CuTe chains on Cu(111) by deposition of 1/3 ML Te: atomic and electronic
structure | Phys. Rev. B 102, 155422 (2020) | 10.1103/PhysRevB.102.155422 | null | cond-mat.mtrl-sci | The surface atomic and electronic structure after deposition of 1/3 monolayer
(ML) Te on Cu(111) was determined using a combination of low-energy electron
diffraction (LEED), scanning tunneling microscopy and spectroscopy (STM/STS),
angle-resolved single and two-photon photoelectron spectroscopy (ARPES
/AR-2PPE) and ... | [{'version': 'v1', 'created': 'Wed, 30 Sep 2020 12:42:24 GMT'}] | 2020-11-02 |
Hironori Yoshioka and Tomonori Honda | Determination of the Interface between Amorphous Insulator and
Crystalline 4H-SiC in Transmission Electron Microscope Image by using
Convolutional Neural Network | AIP Advances 11, 015101 (2021) | 10.1063/5.0036982 | null | cond-mat.mtrl-sci cond-mat.dis-nn cs.LG | A rough interface seems to be one of the possible reasons for low channel
mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface
roughness, we drew a boundary line between amorphous insulator and crystalline
4H-SiC in a cross-sectional image obtained by a transmission electron
microscope (TEM),... | [{'version': 'v1', 'created': 'Wed, 14 Oct 2020 08:28:01 GMT'}] | 2021-01-08 |
Shehtab Zaman, Christopher Owen, Kenneth Chiu, Michael Lawler | Graph Neural Network for Metal Organic Framework Potential Energy
Approximation | null | null | null | cs.LG cond-mat.mtrl-sci | Metal-organic frameworks (MOFs) are nanoporous compounds composed of metal
ions and organic linkers. MOFs play an important role in industrial
applications such as gas separation, gas purification, and electrolytic
catalysis. Important MOF properties such as potential energy are currently
computed via techniques such... | [{'version': 'v1', 'created': 'Thu, 29 Oct 2020 19:47:44 GMT'}] | 2020-11-02 |
Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu | Deep Learning of Accurate Force Field of Ferroelectric HfO$_2$ | Phys. Rev. B 103, 024108 (2021) | 10.1103/PhysRevB.103.024108 | null | cond-mat.mtrl-sci | The discovery of ferroelectricity in HfO$_2$-based thin films opens up new
opportunities for using this silicon-compatible ferroelectric to realize
low-power logic circuits and high-density non-volatile memories. The functional
performances of ferroelectrics are intimately related to their dynamic
responses to extern... | [{'version': 'v1', 'created': 'Fri, 30 Oct 2020 05:21:20 GMT'}] | 2021-02-03 |
Yusuf Shaidu, Emine Kucukbenli, Ruggero Lot, Franco Pellegrini,
Efthimios Kaxiras, Stefano de Gironcoli | A Systematic Approach to Generating Accurate Neural Network Potentials:
the Case of Carbon | null | null | null | cond-mat.mtrl-sci | Availability of affordable and widely applicable interatomic potentials is
the key needed to unlock the riches of modern materials modelling. Artificial
neural network based approaches for generating potentials are promising;
however neural network training requires large amounts of data, sampled
adequately from an o... | [{'version': 'v1', 'created': 'Mon, 9 Nov 2020 17:58:48 GMT'}] | 2020-11-10 |
Yinan Wang, Diane Oyen, Weihong (Grace) Guo, Anishi Mehta, Cory Braker
Scott, Nishant Panda, M. Giselle Fern\'andez-Godino, Gowri Srinivasan,
Xiaowei Yue | StressNet: Deep Learning to Predict Stress With Fracture Propagation in
Brittle Materials | null | null | null | cs.LG cond-mat.mtrl-sci | Catastrophic failure in brittle materials is often due to the rapid growth
and coalescence of cracks aided by high internal stresses. Hence, accurate
prediction of maximum internal stress is critical to predicting time to failure
and improving the fracture resistance and reliability of materials. Existing
high-fideli... | [{'version': 'v1', 'created': 'Fri, 20 Nov 2020 05:49:12 GMT'}] | 2020-11-23 |
Leonid Mill, David Wolff, Nele Gerrits, Patrick Philipp, Lasse Kling,
Florian Vollnhals, Andrew Ignatenko, Christian Jaremenko, Yixing Huang,
Olivier De Castro, Jean-Nicolas Audinot, Inge Nelissen, Tom Wirtz, Andreas
Maier, Silke Christiansen | Synthetic Image Rendering Solves Annotation Problem in Deep Learning
Nanoparticle Segmentation | null | null | null | cs.LG cond-mat.mtrl-sci cs.CV eess.IV physics.app-ph | Nanoparticles occur in various environments as a consequence of man-made
processes, which raises concerns about their impact on the environment and
human health. To allow for proper risk assessment, a precise and statistically
relevant analysis of particle characteristics (such as e.g. size, shape and
composition) is... | [{'version': 'v1', 'created': 'Fri, 20 Nov 2020 17:05:36 GMT'}] | 2020-11-23 |
Jean-Claude Crivello, Nataliya Sokolovska, Jean-Marc Joubert | Supervised deep learning prediction of the formation enthalpy of the
full set of configurations in complex phases: the $\sigma-$phase as an
example | null | null | null | cond-mat.mtrl-sci cs.LG | Machine learning (ML) methods are becoming integral to scientific inquiry in
numerous disciplines, such as material sciences. In this manuscript, we
demonstrate how ML can be used to predict several properties in solid-state
chemistry, in particular the heat of formation of a given complex
crystallographic phase (her... | [{'version': 'v1', 'created': 'Sat, 21 Nov 2020 22:07:15 GMT'}] | 2020-11-24 |
Qiangqiang Gu, Linfeng Zhang and Ji Feng | Neural network representation of electronic structure from $ab$ $initio$
molecular dynamics | Science Bulletin 67, 29 (2022) | 10.1016/j.scib.2021.09.010 | null | cond-mat.mtrl-sci cond-mat.dis-nn | Despite their rich information content, electronic structure data amassed at
high volumes in $ab$ $initio$ molecular dynamics simulations are generally
under-utilized. We introduce a transferable high-fidelity neural network
representation of such data in the form of tight-binding Hamiltonians for
crystalline materia... | [{'version': 'v1', 'created': 'Fri, 27 Nov 2020 15:13:13 GMT'}, {'version': 'v2', 'created': 'Mon, 11 Jan 2021 14:02:19 GMT'}, {'version': 'v3', 'created': 'Mon, 21 Feb 2022 04:03:34 GMT'}] | 2022-02-22 |
Chen Qian, Yunhai Xiong and Xiang Chen | Directed Graph Attention Neural Network Utilizing 3D Coordinates for
Molecular Property Prediction | null | null | null | cs.LG cond-mat.mtrl-sci | The prosperity of computer vision (CV) and natural language procession (NLP)
in recent years has spurred the development of deep learning in many other
domains. The advancement in machine learning provides us with an alternative
option besides the computationally expensive density functional theories (DFT).
Kernel me... | [{'version': 'v1', 'created': 'Tue, 1 Dec 2020 11:06:40 GMT'}] | 2020-12-02 |
Jize Zhang, Bhavya Kailkhura, T. Yong-Jin Han | Leveraging Uncertainty from Deep Learning for Trustworthy Materials
Discovery Workflows | null | null | null | cond-mat.mtrl-sci cs.CV cs.LG physics.app-ph | In this paper, we leverage predictive uncertainty of deep neural networks to
answer challenging questions material scientists usually encounter in machine
learning based materials applications workflows. First, we show that by
leveraging predictive uncertainty, a user can determine the required training
data set size... | [{'version': 'v1', 'created': 'Wed, 2 Dec 2020 19:34:16 GMT'}, {'version': 'v2', 'created': 'Thu, 22 Apr 2021 23:29:30 GMT'}] | 2021-04-26 |
Debjyoti Bhattacharya and Tarak K Patra | dPOLY: Deep Learning of Polymer Phases and Phase Transition | null | 10.1021/acs.macromol.0c02655 | null | cond-mat.soft cond-mat.mtrl-sci | Machine learning (ML) and artificial intelligence (AI) have the remarkable
ability to classify, recognize, and characterize complex patterns and trends in
large data sets. Here, we adopt a subclass of machine learning methods viz.,
deep learnings and develop a general-purpose AI tool - dPOLY for analyzing
molecular d... | [{'version': 'v1', 'created': 'Sun, 6 Dec 2020 04:51:40 GMT'}] | 2021-06-09 |
Robbie Sadre, Colin Ophus, Anstasiia Butko, and Gunther H Weber | Deep Learning Segmentation of Complex Features in Atomic-Resolution
Phase Contrast Transmission Electron Microscopy Images | null | 10.1017/S1431927621000167 | null | cond-mat.mtrl-sci cs.LG | Phase contrast transmission electron microscopy (TEM) is a powerful tool for
imaging the local atomic structure of materials. TEM has been used heavily in
studies of defect structures of 2D materials such as monolayer graphene due to
its high dose efficiency. However, phase contrast imaging can produce complex
nonlin... | [{'version': 'v1', 'created': 'Wed, 9 Dec 2020 21:17:34 GMT'}] | 2021-09-01 |
Zhao Fan and Evan Ma | Predicting orientation-dependent plastic susceptibility from static
structure in amorphous solids via deep learning | Nature Communications 12, 1506 (2021) | 10.1038/s41467-021-21806-z | null | cond-mat.mtrl-sci | It has been a long-standing materials science challenge to establish
structure-property relations in amorphous solids. Here we introduce a
rotation-variant local structure representation that enables different
predictions for different loading orientations, which is found essential for
high-fidelity prediction of the... | [{'version': 'v1', 'created': 'Fri, 11 Dec 2020 00:28:51 GMT'}] | 2022-03-15 |
Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Jianjun Hu | Computational discovery of new 2D materials using deep learning
generative models | null | null | null | cond-mat.mtrl-sci cs.LG | Two dimensional (2D) materials have emerged as promising functional materials
with many applications such as semiconductors and photovoltaics because of
their unique optoelectronic properties. While several thousand 2D materials
have been screened in existing materials databases, discovering new 2D
materials remains ... | [{'version': 'v1', 'created': 'Wed, 16 Dec 2020 23:10:48 GMT'}] | 2020-12-18 |
Jiale Zhang, Danni Wei, Feng Zhang, Xi Chen, and Dawei Wang | Structural phase transition of two-dimensional monolayer SnTe from
artificial neural network | null | null | null | cond-mat.mtrl-sci physics.comp-ph | As machine learning becomes increasingly important in engineering and
science, it is inevitable that machine learning techniques will be applied to
the investigation of materials, and in particular the structural phase
transitions common in ferroelectric materials. Here, we build and train an
artificial neural networ... | [{'version': 'v1', 'created': 'Mon, 21 Dec 2020 06:33:53 GMT'}, {'version': 'v2', 'created': 'Mon, 29 Mar 2021 03:43:05 GMT'}] | 2021-03-30 |
Jeffrey M. Ede | Advances in Electron Microscopy with Deep Learning | null | 10.5281/zenodo.4399748 | null | eess.IV cond-mat.mtrl-sci cs.CV cs.LG | This doctoral thesis covers some of my advances in electron microscopy with
deep learning. Highlights include a comprehensive review of deep learning in
electron microscopy; large new electron microscopy datasets for machine
learning, dataset search engines based on variational autoencoders, and
automatic data cluste... | [{'version': 'v1', 'created': 'Mon, 4 Jan 2021 13:49:37 GMT'}, {'version': 'v2', 'created': 'Sat, 9 Jan 2021 17:30:04 GMT'}, {'version': 'v3', 'created': 'Fri, 5 Mar 2021 12:06:00 GMT'}, {'version': 'v4', 'created': 'Tue, 9 Mar 2021 14:53:24 GMT'}, {'version': 'v5', 'created': 'Thu, 11 Mar 2021 17:25:33 GMT'}] | 2021-03-12 |
Yi-Shen Lin, Ganga P. Purja Pun and Yuri Mishin | Development of a physically-informed neural network interatomic
potential for tantalum | Computational Materials Science 205, 111180 (2022) | 10.1016/j.commatsci.2021.111180 | null | cond-mat.mtrl-sci | Large-scale atomistic simulations of materials heavily rely on interatomic
potentials, which predict the system energy and atomic forces. One of the
recent developments in the field is constructing interatomic potentials by
machine-learning (ML) methods. ML potentials predict the energy and forces by
numerical interp... | [{'version': 'v1', 'created': 'Sat, 16 Jan 2021 22:49:09 GMT'}] | 2022-02-09 |
Mani Valleti, Sergei V. Kalinin, Christopher T. Nelson, Jonathan J. P.
Peters, Wen Dong, Richard Beanland, Xiaohang Zhang, Ichiro Takeuchi, Maxim
Ziatdinov | Unsupervised learning of ferroic variants from atomically resolved STEM
images | null | 10.1063/5.0105406 | null | cond-mat.mtrl-sci cond-mat.mes-hall | An approach for the analysis of atomically resolved scanning transmission
electron microscopy data with multiple ferroic variants in the presence of
imaging non-idealities and chemical variabilities based on a rotationally
invariant variational autoencoder (rVAE) is presented. We show that an optimal
local descriptor... | [{'version': 'v1', 'created': 'Mon, 18 Jan 2021 06:00:41 GMT'}, {'version': 'v2', 'created': 'Mon, 20 Jun 2022 13:37:26 GMT'}] | 2024-06-19 |
Joshua L. Vincent, Ramon Manzorro, Sreyas Mohan, Binh Tang, Dev Y.
Sheth, Eero P. Simoncelli, David S. Matteson, Carlos Fernandez-Granda, and
Peter A. Crozier | Developing and Evaluating Deep Neural Network-based Denoising for
Nanoparticle TEM Images with Ultra-low Signal-to-Noise | Microscopy and Microanalysis, vol 27, no 6, pp 1431--1447, Dec
2021 | 10.1017/S1431927621012678 | null | cond-mat.mtrl-sci eess.IV | A deep convolutional neural network has been developed to denoise
atomic-resolution TEM image datasets of nanoparticles acquired using direct
electron counting detectors, for applications where the image signal is
severely limited by shot noise. The network was applied to a model system of
CeO2-supported Pt nanoparti... | [{'version': 'v1', 'created': 'Tue, 19 Jan 2021 18:34:18 GMT'}, {'version': 'v2', 'created': 'Wed, 17 Mar 2021 19:37:16 GMT'}] | 2025-03-03 |
Doyl Dickel, Mashroor Nitol, Christopher Barrett | LAMMPS Implementation of Rapid Artificial Neural Network Derived
Interatomic Potentials | null | null | null | cond-mat.mtrl-sci | While machine learning approaches have been successfully used to represent
interatomic potentials, their speed has typically lagged behind conventional
formalisms. This is often due to the complexity of the structural fingerprints
used to describe the local atomic environment and the large cutoff radii and
neighbor l... | [{'version': 'v1', 'created': 'Thu, 4 Feb 2021 00:06:08 GMT'}, {'version': 'v2', 'created': 'Fri, 19 Feb 2021 21:55:12 GMT'}] | 2021-02-23 |
Chi Chen and Shyue Ping Ong | AtomSets -- A Hierarchical Transfer Learning Framework for Small and
Large Materials Datasets | null | 10.1038/s41524-021-00639-w | null | cond-mat.mtrl-sci | Predicting materials properties from composition or structure is of great
interest to the materials science community. Deep learning has recently
garnered considerable interest in materials predictive tasks with low model
errors when dealing with large materials data. However, deep learning models
suffer in the small... | [{'version': 'v1', 'created': 'Thu, 4 Feb 2021 04:02:23 GMT'}, {'version': 'v2', 'created': 'Fri, 5 Feb 2021 18:41:06 GMT'}] | 2021-11-01 |
Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin, Anthony M.
DeGennaro, Andi M. Barbour | Noise Reduction in X-ray Photon Correlation Spectroscopy with
Convolutional Neural Networks Encoder-Decoder Models | null | 10.1038/s41598-021-93747-y | null | cond-mat.mtrl-sci cs.LG | Like other experimental techniques, X-ray Photon Correlation Spectroscopy is
subject to various kinds of noise. Random and correlated fluctuations and
heterogeneities can be present in a two-time correlation function and obscure
the information about the intrinsic dynamics of a sample. Simultaneously
addressing the d... | [{'version': 'v1', 'created': 'Sun, 7 Feb 2021 18:38:59 GMT'}, {'version': 'v2', 'created': 'Thu, 5 Aug 2021 18:22:34 GMT'}] | 2021-08-09 |
Hossein Mirhosseini, Hossein Tahmasbi, Sai Ram Kuchana, S. Alireza
Ghasemi, Thomas D. K\"uhne | An automated approach for developing neural network interatomic
potentials with FLAME | null | null | null | cond-mat.mtrl-sci | The performance of machine learning interatomic potentials relies on the
quality of the training dataset. In this work, we present an approach for
generating diverse and representative training data points which initiates with
\it{ab initio} calculations for bulk structures. The data generation and
potential construc... | [{'version': 'v1', 'created': 'Mon, 8 Feb 2021 09:48:27 GMT'}] | 2021-02-09 |
Boyu Zhang, Mushen Zhou, Jianzhong Wu, Fuchang Gao | Predicting Material Properties Using a 3D Graph Neural Network with
Invariant Local Descriptors | null | null | null | cond-mat.mtrl-sci cs.AI | Accurate prediction of physical properties is critical for discovering and
designing novel materials. Machine learning technologies have attracted
significant attention in the materials science community for their potential
for large-scale screening. Graph Convolution Neural Network (GCNN) is one of
the most successf... | [{'version': 'v1', 'created': 'Tue, 16 Feb 2021 19:56:54 GMT'}, {'version': 'v2', 'created': 'Mon, 22 Nov 2021 22:08:52 GMT'}] | 2021-11-24 |
Yue Li, Xuyang Zhou, Timoteo Colnaghi, Ye Wei, Andreas Marek,
Hongxiang Li, Stefan Bauer, Markus Rampp, Leigh Stephenson | Convolutional neural network-assisted recognition of nanoscale L12
ordered structures in face-centred cubic alloys | NPJ Computational Materials 7, 8 (2021) | 10.1038/s41524-020-00472-7 | null | cond-mat.mtrl-sci physics.data-an | Nanoscale L12-type ordered structures are widely used in face-centred cubic
(FCC) alloys to exploit their hardening capacity and thereby improve mechanical
properties. These fine-scale particles are typically fully coherent with matrix
with the same atomic configuration disregarding chemical species, which makes
them... | [{'version': 'v1', 'created': 'Tue, 16 Feb 2021 09:41:50 GMT'}] | 2021-02-23 |
Debjyoti Bhattacharya and Tarak K Patra | Deep Learning Order Parameter for Polymer Phase Transition | null | null | null | cond-mat.mtrl-sci | We report a deep learning (DL) framework viz. deep autoencoder that
autonomously discovers an appropriate order parameter from molecular dynamics
(MD) simulation data to characterize the coil to globule phase transition of a
polymer. The deep autoencoder encodes the 3N dimensional MD trajectory of a
polymer in a one-... | [{'version': 'v1', 'created': 'Wed, 24 Feb 2021 01:04:15 GMT'}] | 2021-02-25 |
Andreas Erlebach, Petr Nachtigall, and Luk\'a\v{s} Grajciar | Accurate large-scale simulations of siliceous zeolites by neural network
potentials | npj Comput Mater 8, 174 (2022) | 10.1038/s41524-022-00865-w | null | cond-mat.mtrl-sci | The computational discovery and design of zeolites is a crucial part of the
chemical industry. Finding highly accurate while computationally feasible
protocol for identification of hypothetical zeolites that could be targeted
experimentally is a great challenge. To tackle the challenge, we trained neural
network pote... | [{'version': 'v1', 'created': 'Wed, 24 Feb 2021 16:44:18 GMT'}, {'version': 'v2', 'created': 'Mon, 10 Jan 2022 14:35:01 GMT'}, {'version': 'v3', 'created': 'Fri, 19 Aug 2022 14:12:44 GMT'}] | 2022-08-22 |
Wesley F. Reinhart | Unsupervised learning of atomic environments from simple features | null | 10.1016/j.commatsci.2021.110511 | null | cond-mat.mtrl-sci | I present a strategy for unsupervised manifold learning on local atomic
environments in molecular simulations based on simple rotation- and
permutation-invariant three-body features. These features are highly
descriptive, generalize to multiple chemical species, and are
human-interpretable. The low-dimensional embedd... | [{'version': 'v1', 'created': 'Sun, 28 Feb 2021 11:37:27 GMT'}, {'version': 'v2', 'created': 'Sat, 10 Apr 2021 13:46:17 GMT'}] | 2023-01-03 |
Andreas Leitherer, Angelo Ziletti, and Luca M. Ghiringhelli | Robust recognition and exploratory analysis of crystal structures via
Bayesian deep learning | Leitherer, A., Ziletti, A. & Ghiringhelli, L.M. Robust recognition
and exploratory analysis of crystal structures via Bayesian deep learning.
Nat. Commun. 12, 6234 (2021) | 10.1038/s41467-021-26511-5 | null | cond-mat.mtrl-sci | Due to their ability to recognize complex patterns, neural networks can drive
a paradigm shift in the analysis of materials science data. Here, we introduce
ARISE, a crystal-structure identification method based on Bayesian deep
learning. As a major step forward, ARISE is robust to structural noise and can
treat more... | [{'version': 'v1', 'created': 'Wed, 17 Mar 2021 17:04:13 GMT'}, {'version': 'v2', 'created': 'Wed, 21 Apr 2021 17:33:31 GMT'}, {'version': 'v3', 'created': 'Tue, 14 Sep 2021 19:37:09 GMT'}, {'version': 'v4', 'created': 'Fri, 1 Oct 2021 14:24:19 GMT'}, {'version': 'v5', 'created': 'Mon, 8 Nov 2021 15:03:00 GMT'}] | 2021-11-09 |
Noopur Jamnikar, Sen Liu, Craig Brice, and Xiaoli Zhang | Comprehensive process-molten pool relations modeling using CNN for
wire-feed laser additive manufacturing | null | null | null | cond-mat.mtrl-sci cs.LG eess.SP stat.ML | Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to
its high level of automation, high deposition rates, and good quality of
printed parts. In-process monitoring and feedback controls that would reduce
the uncertainty in the quality of the material are in the early stages of
development. Mac... | [{'version': 'v1', 'created': 'Mon, 22 Mar 2021 05:27:20 GMT'}] | 2021-03-23 |
C.H.Wong, S.M. Ng, C.W.Leung, A.F.Zatsepin | The effectiveness of data augmentation in porous substrate, nanowire,
fiber and tip images at the level of deep learning intelligence | null | null | null | cond-mat.mtrl-sci | To prepare for identifying the composition of nanowire-fiber mixtures in
Scanning Electron Microscope (SEM) images, we optimize the performance of image
classification between nanowires, fibers and tips due to their geometric
similarities. The SEM images are analyzed by deep learning techniques where the
validation a... | [{'version': 'v1', 'created': 'Thu, 11 Mar 2021 10:05:13 GMT'}] | 2021-03-24 |
Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire, Daniel Sparkman,
George Em Karniadakis | A physics-informed neural network for quantifying the microstructure
properties of polycrystalline Nickel using ultrasound data | null | 10.1109/MSP.2021.3118904 | null | cond-mat.mtrl-sci physics.comp-ph | We employ physics-informed neural networks (PINNs) to quantify the
microstructure of a polycrystalline Nickel by computing the spatial variation
of compliance coefficients (compressibility, stiffness and rigidity) of the
material. The PINN is supervised with realistic ultrasonic surface acoustic
wavefield data acquir... | [{'version': 'v1', 'created': 'Thu, 25 Mar 2021 19:47:17 GMT'}, {'version': 'v2', 'created': 'Tue, 5 Oct 2021 17:27:05 GMT'}] | 2022-01-12 |
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Qingsong Tu,
Gerbrand Ceder | A probabilistic deep learning approach to automate the interpretation of
multi-phase diffraction spectra | null | 10.1021/acs.chemmater.1c01071 | null | cond-mat.mtrl-sci cs.LG | Autonomous synthesis and characterization of inorganic materials requires the
automatic and accurate analysis of X-ray diffraction spectra. For this task, we
designed a probabilistic deep learning algorithm to identify complex
multi-phase mixtures. At the core of this algorithm lies an ensemble
convolutional neural n... | [{'version': 'v1', 'created': 'Tue, 30 Mar 2021 20:13:01 GMT'}] | 2021-05-27 |
Lars Banko, Phillip M. Maffettone, Dennis Naujoks, Daniel Olds, Alfred
Ludwig | Deep learning for visualization and novelty detection in large X-ray
diffraction datasets | null | null | null | cond-mat.mtrl-sci physics.data-an | We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data
analysis on both simulated and experimental thin-film data. We show that
crystal structure representations learned by a VAE reveal latent information,
such as the structural similarity of textured diffraction patterns. While other
artificial inte... | [{'version': 'v1', 'created': 'Fri, 9 Apr 2021 14:31:22 GMT'}] | 2021-04-12 |
Wouter Klessens, Ivan Vasconcelos, Yang Jiao | AI-driven Bayesian inference of statistical microstructure descriptors
from finite-frequency waves | null | null | null | physics.geo-ph cond-mat.mtrl-sci eess.IV | The ability to image materials at the microscale from long-wavelength wave
data is a major challenge to the geophysical, engineering and medical fields.
Here, we present a framework to constrain microstructure geometry and
properties from long-scale waves. To realistically quantify microstructures we
use two-point st... | [{'version': 'v1', 'created': 'Fri, 16 Apr 2021 13:43:52 GMT'}] | 2021-04-19 |
Zi-Shan Liao, Hong-Hao Zhang, Zhongbo Yan | Nonlinear Hall effect in two-dimensional class AI metals | Phys. Rev. B 103, 235151 (2021) | 10.1103/PhysRevB.103.235151 | null | cond-mat.mtrl-sci quant-ph | In a time-reversal invariant system, while the anomalous Hall effect
identically vanishes in the linear response regime due to the constraint of
time-reversal symmetry on the distribution of Berry curvature, a nonlinear Hall
effect can emerge in the second-order response regime if the inversion symmetry
is broken to ... | [{'version': 'v1', 'created': 'Sat, 17 Apr 2021 07:50:59 GMT'}] | 2021-06-30 |
Yunxing Zuo, Mingde Qin, Chi Chen, Weike Ye, Xiangguo Li, Jian Luo,
Shyue Ping Ong | Accelerating Materials Discovery with Bayesian Optimization and Graph
Deep Learning | null | null | null | cond-mat.mtrl-sci | Machine learning (ML) models utilizing structure-based features provide an
efficient means for accurate property predictions across diverse chemical
spaces. However, obtaining equilibrium crystal structures typically requires
expensive density functional theory (DFT) calculations, which limits ML-based
exploration to... | [{'version': 'v1', 'created': 'Tue, 20 Apr 2021 20:37:00 GMT'}] | 2021-04-22 |
Yongtao Liu, Roger Proksch, Chun Yin Wong, Maxim Ziatdinov, and Sergei
V. Kalinin | Disentangling ferroelectric wall dynamics and identification of pinning
mechanisms via deep learning | null | null | null | cond-mat.dis-nn cond-mat.mtrl-sci | Field-induced domain wall dynamics in ferroelectric materials underpins
multiple applications ranging from actuators to information technology devices
and necessitates a quantitative description of the associated mechanisms
including giant electromechanical couplings, controlled non-linearities, or low
coercive volta... | [{'version': 'v1', 'created': 'Sat, 15 May 2021 03:39:19 GMT'}] | 2021-05-18 |
Maxim Ziatdinov, Ayana Ghosh, Tommy Wong, and Sergei V. Kalinin | AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy
Data in (Scanning) Transmission Electron Microscopy and Beyond | Nat Mach Intell 4, 1101-1112 (2022) | 10.1038/s42256-022-00555-8 | null | physics.data-an cond-mat.dis-nn cond-mat.mtrl-sci cs.LG | AtomAI is an open-source software package bridging instrument-specific Python
libraries, deep learning, and simulation tools into a single ecosystem. AtomAI
allows direct applications of the deep convolutional neural networks for atomic
and mesoscopic image segmentation converting image and spectroscopy data into
cla... | [{'version': 'v1', 'created': 'Sun, 16 May 2021 17:44:59 GMT'}] | 2022-12-29 |
Zhe Wang and Claude Guet | Deep learning in physics: a study of dielectric quasi-cubic particles in
a uniform electric field | null | null | null | physics.class-ph cond-mat.mtrl-sci cs.LG physics.comp-ph | Solving physics problems for which we know the equations, boundary conditions
and symmetries can be done by deep learning. The constraints can be either
imposed as terms in a loss function or used to formulate a neural ansatz. In
the present case study, we calculate the induced field inside and outside a
dielectric c... | [{'version': 'v1', 'created': 'Tue, 11 May 2021 10:40:03 GMT'}] | 2021-05-21 |
Brendan P. Croom, Michael Berkson, Robert K. Mueller, Michael Presley,
Steven Storck | Deep learning prediction of stress fields in additively manufactured
metals with intricate defect networks | null | null | null | cond-mat.mtrl-sci | In context of the universal presence of defects in additively manufactured
(AM) metals, efficient computational tools are required to rapidly screen AM
microstructures for mechanical integrity. To this end, a deep learning approach
is used to predict the elastic stress fields in images of defect-containing
metal micr... | [{'version': 'v1', 'created': 'Fri, 21 May 2021 20:44:44 GMT'}] | 2021-05-25 |
Maxim Ziatdinov, Muammer Yusuf Yaman, Yongtao Liu, David Ginger, and
Sergei V. Kalinin | Semi-supervised learning of images with strong rotational disorder:
assembling nanoparticle libraries | null | null | null | cs.LG cond-mat.dis-nn cond-mat.mtrl-sci physics.data-an | The proliferation of optical, electron, and scanning probe microscopies gives
rise to large volumes of imaging data of objects as diversified as cells,
bacteria, pollen, to nanoparticles and atoms and molecules. In most cases, the
experimental data streams contain images having arbitrary rotations and
translations wi... | [{'version': 'v1', 'created': 'Mon, 24 May 2021 18:01:57 GMT'}] | 2021-05-26 |
Gihan Panapitiya, Michael Girard, Aaron Hollas, Vijay Murugesan, Wei
Wang, Emily Saldanha | Predicting Aqueous Solubility of Organic Molecules Using Deep Learning
Models with Varied Molecular Representations | null | 10.1021/acsomega.2c00642 | null | cond-mat.mtrl-sci cs.LG | Determining the aqueous solubility of molecules is a vital step in many
pharmaceutical, environmental, and energy storage applications. Despite efforts
made over decades, there are still challenges associated with developing a
solubility prediction model with satisfactory accuracy for many of these
applications. The ... | [{'version': 'v1', 'created': 'Wed, 26 May 2021 15:54:54 GMT'}, {'version': 'v2', 'created': 'Thu, 27 May 2021 01:03:43 GMT'}] | 2022-09-05 |
Kamal Choudhary, Brian DeCost | Atomistic Line Graph Neural Network for Improved Materials Property
Predictions | null | 10.1038/s41524-021-00650-1 | null | cond-mat.mtrl-sci | Graph neural networks (GNN) have been shown to provide substantial
performance improvements for atomistic material representation and modeling
compared with descriptor-based machine learning models. While most existing GNN
models for atomistic predictions are based on atomic distance information, they
do not explicit... | [{'version': 'v1', 'created': 'Thu, 3 Jun 2021 13:26:06 GMT'}, {'version': 'v2', 'created': 'Tue, 7 Sep 2021 13:53:44 GMT'}, {'version': 'v3', 'created': 'Thu, 7 Apr 2022 00:13:11 GMT'}] | 2022-04-08 |
Peyman Saidi, Hadi Pirgazi, Mehdi Sanjari, Saeed Tamimi, Mohsen
Mohammadi, Laurent K. Beland, Mark R. Daymond, Isaac Tamblyn | Deep Learning and Crystal Plasticity: A Preconditioning Approach for
Accurate Orientation Evolution Prediction | null | 10.1016/j.cma.2021.114392 | null | cond-mat.mtrl-sci | Efficient and precise prediction of plasticity by data-driven models relies
on appropriate data preparation and a well-designed model. Here we introduce an
unsupervised machine learning-based data preparation method to maximize the
trainability of crystal orientation evolution data during deformation. For
Taylor mode... | [{'version': 'v1', 'created': 'Thu, 24 Jun 2021 02:32:46 GMT'}] | 2021-12-22 |
Yoshinori Shiihara, Ryosuke Kanazawa, Daisuke Matsunaka, Ivan
Lobzenko, Tomohito Tsuru, Masanori Kohyama, Hideki Mori | Artificial neural network molecular mechanics of iron grain boundaries | null | null | null | cond-mat.mtrl-sci | This study reports grain boundary (GB) energy calculations for 46
symmetric-tilt GBs in alpha-iron using molecular mechanics based on an
artificial neural network (ANN) potential and compares the results with
calculations based on the density functional theory (DFT), the embedded atom
method (EAM), and the modified E... | [{'version': 'v1', 'created': 'Thu, 24 Jun 2021 03:05:43 GMT'}] | 2021-06-25 |
So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen
Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke
Asano, Tasuku Onodera, Takafumi Ishii, Takao Kudo, Hideki Ono, Ryohto Sawada,
Ryuichiro Ishitani, Marc Ong, Taiki Yamaguchi, Toshiki Kataoka, Akihide
Hayashi, Nontawat... | Towards Universal Neural Network Potential for Material Discovery
Applicable to Arbitrary Combination of 45 Elements | null | 10.1038/s41467-022-30687-9 | null | cond-mat.mtrl-sci physics.comp-ph | Computational material discovery is under intense study owing to its ability
to explore the vast space of chemical systems. Neural network potentials (NNPs)
have been shown to be particularly effective in conducting atomistic
simulations for such purposes. However, existing NNPs are generally designed
for narrow targ... | [{'version': 'v1', 'created': 'Mon, 28 Jun 2021 11:32:13 GMT'}, {'version': 'v2', 'created': 'Fri, 1 Apr 2022 15:48:16 GMT'}] | 2022-07-06 |
Bruce Lim, Ewen Bellec, Maxime Dupraz, Steven Leake, Andrea Resta,
Alessandro Coati, Michael Sprung, Ehud Almog, Eugen Rabkin, Tobias Sch\"ulli
and Marie-Ingrid Richard | A convolutional neural network for defect classification in Bragg
coherent X-ray diffraction | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Coherent diffraction imaging enables the imaging of individual defects, such
as dislocations or stacking faults, in materials.These defects and their
surrounding elastic strain fields have a critical influence on the macroscopic
properties and functionality of materials. However, their identification in
Bragg coheren... | [{'version': 'v1', 'created': 'Wed, 30 Jun 2021 16:15:29 GMT'}] | 2021-07-01 |
Gerardo Valadez Huerta, Yusuke Nanba, Iori Kurata, Kosuke Nakago, So
Takamoto, Chikashi Shinagawa, Michihisa Koyama | Calculations of Real-System Nanoparticles Using Universal Neural Network
Potential PFP | null | null | null | cond-mat.mtrl-sci | It is essential to explore the stability and activity of real-system
nanoparticles theoretically. While applications of theoretical methods for this
purpose can be found in literature, the expensive computational costs of
conventional theoretical methods hinder their massive applications to practical
materials design... | [{'version': 'v1', 'created': 'Fri, 2 Jul 2021 10:51:24 GMT'}] | 2021-07-05 |
Carlos J. G. Rojas, Marco L. Bitterncourt, Jos\'e L. Boldrini | Parameter identification for a damage model using a physics informed
neural network | null | null | null | cond-mat.mtrl-sci physics.comp-ph | This work applies concepts of artificial neural networks to identify the
parameters of a mathematical model based on phase fields for damage and
fracture. Damage mechanics is the part of the continuum mechanics that models
the effects of the micro-defect formation using state variables at the
macroscopic level. The e... | [{'version': 'v1', 'created': 'Fri, 25 Jun 2021 13:04:00 GMT'}] | 2021-07-21 |
Chenxi Sui, Yao-Yu Li, Xiuqiang Li, Genesis Higueros, Keyu Wang,
Wanrong Xie, Po-Chun Hsu | Bio-inspired vascularized electrodes for high-performance fast-charging
batteries designed by deep learning | null | null | null | cond-mat.mtrl-sci | Slow ionic transport and high voltage drop (IR drop) of homogeneous porous
electrodes are the critical causes of severe performance degradation of
lithium-ion (Li-ion) batteries under high charging rates. Herein, we
demonstrate that a bio-inspired vascularized porous electrode can
simultaneously solve these two probl... | [{'version': 'v1', 'created': 'Thu, 29 Jul 2021 02:30:19 GMT'}] | 2021-07-30 |
Aur\`ele Goetz, Ali Riza Durmaz, Martin M\"uller, Akhil Thomas,
Dominik Britz, Pierre Kerfriden and Chris Eberl | Addressing materials' microstructure diversity using transfer learning | null | null | null | cond-mat.mtrl-sci cs.LG | Materials' microstructures are signatures of their alloying composition and
processing history. Therefore, microstructures exist in a wide variety. As
materials become increasingly complex to comply with engineering demands,
advanced computer vision (CV) approaches such as deep learning (DL) inevitably
gain relevance... | [{'version': 'v1', 'created': 'Thu, 29 Jul 2021 09:13:11 GMT'}] | 2021-07-30 |
Ryo Tamura, Momo Matsuda, Jianbo Lin, Yasunori Futamura, Tetsuya
Sakurai, Tsuyoshi Miyazaki | Unsupervised learning-based structural analysis: Search for a
characteristic low-dimensional space by local structures in atomistic
simulations | null | 10.1103/PhysRevB.105.075107 | null | cond-mat.mtrl-sci | Owing to the advances in computational techniques and the increase in
computational power, atomistic simulations of materials can simulate large
systems with higher accuracy. Complex phenomena can be observed in such
state-of-the-art atomistic simulations. However, it has become increasingly
difficult to understand w... | [{'version': 'v1', 'created': 'Thu, 29 Jul 2021 20:19:42 GMT'}] | 2022-02-16 |
Johannes Allotey, Keith T. Butler and Jeyan Thiyagalingam | Entropy-based Active Learning of Graph Neural Network Surrogate Models
for Materials Properties | null | 10.1063/5.0065694 | null | cond-mat.mtrl-sci | Graph neural networks, trained on experimental or calculated data are
becoming an increasingly important tool in computational materials science.
Networks, once trained, are able to make highly accurate predictions at a
fraction of the cost of experiments or first-principles calculations of
comparable accuracy. Howev... | [{'version': 'v1', 'created': 'Wed, 4 Aug 2021 14:22:57 GMT'}, {'version': 'v2', 'created': 'Fri, 13 Aug 2021 10:49:29 GMT'}] | 2024-06-19 |
Leonid Kahle and Federico Zipoli | On the Quality of Uncertainty Estimates from Neural Network Potential
Ensembles | Phys. Rev. E 105 (2022), 015311 | 10.1103/PhysRevE.105.015311 | null | cond-mat.mtrl-sci cond-mat.dis-nn | Neural network potentials (NNPs) combine the computational efficiency of
classical interatomic potentials with the high accuracy and flexibility of the
ab initio methods used to create the training set, but can also result in
unphysical predictions when employed outside their training set distribution.
Estimating the... | [{'version': 'v1', 'created': 'Thu, 12 Aug 2021 13:36:51 GMT'}, {'version': 'v2', 'created': 'Fri, 21 Jan 2022 17:17:12 GMT'}] | 2022-01-24 |
Suheng Xu, Alexander S. McLeod, Xinzhong Chen, Daniel J. Rizzo, Bjarke
S. Jessen, Ziheng Yao, Zhiyuan Sun, Sara Shabani, Abhay N. Pasupathy, Andrew
J. Millis, Cory R. Dean, James C. Hone, Mengkun Liu, D. N. Basov | Deep learning analysis of polaritonic waves images | ACS Nano 15, 11, 18182-18191(2020) | 10.1021/acsnano.1c07011 | null | cond-mat.mtrl-sci physics.data-an physics.optics | Deep learning (DL) is an emerging analysis tool across sciences and
engineering. Encouraged by the successes of DL in revealing quantitative trends
in massive imaging data, we applied this approach to nano-scale deeply
sub-diffractional images of propagating polaritonic waves in complex materials.
We developed a prac... | [{'version': 'v1', 'created': 'Wed, 11 Aug 2021 02:33:41 GMT'}, {'version': 'v2', 'created': 'Wed, 10 Jul 2024 05:26:52 GMT'}] | 2024-07-11 |
Mingren Shen, Guanzhao Li, Dongxia Wu, Yudai Yaguchi, Jack C. Haley,
Kevin G. Field, and Dane Morgan | A Deep Learning Based Automatic Defect Analysis Framework for In-situ
TEM Ion Irradiations | null | 10.1016/j.commatsci.2021.110560 | null | cs.CV cond-mat.mtrl-sci | Videos captured using Transmission Electron Microscopy (TEM) can encode
details regarding the morphological and temporal evolution of a material by
taking snapshots of the microstructure sequentially. However, manual analysis
of such video is tedious, error-prone, unreliable, and prohibitively
time-consuming if one w... | [{'version': 'v1', 'created': 'Thu, 19 Aug 2021 19:15:44 GMT'}] | 2021-08-23 |
Mingren Shen, Guanzhao Li, Dongxia Wu, Yuhan Liu, Jacob Greaves, Wei
Hao, Nathaniel J. Krakauer, Leah Krudy, Jacob Perez, Varun Sreenivasan, Bryan
Sanchez, Oigimer Torres, Wei Li, Kevin Field, and Dane Morgan | Multi defect detection and analysis of electron microscopy images with
deep learning | null | 10.1016/j.commatsci.2021.110576 | null | cs.CV cond-mat.mtrl-sci | Electron microscopy is widely used to explore defects in crystal structures,
but human detecting of defects is often time-consuming, error-prone, and
unreliable, and is not scalable to large numbers of images or real-time
analysis. In this work, we discuss the application of machine learning
approaches to find the lo... | [{'version': 'v1', 'created': 'Thu, 19 Aug 2021 19:16:24 GMT'}] | 2021-08-23 |
Di Chen, Yiwei Bai, Sebastian Ament, Wenting Zhao, Dan Guevarra, Lan
Zhou, Bart Selman, R. Bruce van Dover, John M. Gregoire, Carla P. Gomes | Automating Crystal-Structure Phase Mapping: Combining Deep Learning with
Constraint Reasoning | null | null | null | cs.LG cond-mat.mtrl-sci cs.AI | Crystal-structure phase mapping is a core, long-standing challenge in
materials science that requires identifying crystal structures, or mixtures
thereof, in synthesized materials. Materials science experts excel at solving
simple systems but cannot solve complex systems, creating a major bottleneck in
high-throughpu... | [{'version': 'v1', 'created': 'Sat, 21 Aug 2021 15:01:38 GMT'}] | 2021-08-24 |
Arindam Debnath, Adam M. Krajewski, Hui Sun, Shuang Lin, Marcia Ahn,
Wenjie Li, Shanshank Priya, Jogender Singh, Shunli Shang, Allison M. Beese,
Zi-Kui Liu, Wesley F. Reinhart | Generative deep learning as a tool for inverse design of high-entropy
refractory alloys | null | 10.20517/jmi.2021.05 | null | cond-mat.mtrl-sci | Generative deep learning is powering a wave of new innovations in materials
design. In this article, we discuss the basic operating principles of these
methods and their advantages over rational design through the lens of a case
study on refractory high-entropy alloys for ultra-high-temperature
applications. We prese... | [{'version': 'v1', 'created': 'Thu, 26 Aug 2021 19:59:45 GMT'}, {'version': 'v2', 'created': 'Tue, 31 Aug 2021 18:25:06 GMT'}] | 2023-01-03 |
Junqi Yin and Zongrui Pei and Michael Gao | Neural network based order parameter for phase transitions and its
applications in high-entropy alloys | null | null | null | cond-mat.mtrl-sci cs.LG | Phase transition is one of the most important phenomena in nature and plays a
central role in materials design. All phase transitions are characterized by
suitable order parameters, including the order-disorder phase transition.
However, finding a representative order parameter for complex systems is
nontrivial, such... | [{'version': 'v1', 'created': 'Sun, 12 Sep 2021 19:54:36 GMT'}] | 2021-09-14 |
Seunghyun Moon, Ruimin Ma, Ross Attardo, Charles Tomonto, Mark Nordin,
Paul Wheelock, Michael Glavicic, Maxwell Layman, Richard Billo, Tengfei Luo | Impact of Surface and Pore Characteristics on Fatigue Life of Laser
Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models | null | null | null | cond-mat.mtrl-sci cs.LG physics.app-ph | In this study, the effects of surface roughness and pore characteristics on
fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were
investigated. The 197 fatigue bars were printed using the same laser power but
with varied scanning speeds. These actions led to variations in the geometries
of microscale p... | [{'version': 'v1', 'created': 'Sat, 28 Aug 2021 02:51:04 GMT'}] | 2021-09-21 |
Yudong Yao, Henry Chan, Subramanian Sankaranarayanan, Prasanna
Balaprakash, Ross J. Harder, and Mathew J. Cherukara | AutoPhaseNN: Unsupervised Physics-aware Deep Learning of 3D Nanoscale
Bragg Coherent Diffraction Imaging | null | null | null | physics.app-ph cond-mat.mtrl-sci cs.AI cs.CV | The problem of phase retrieval, or the algorithmic recovery of lost phase
information from measured intensity alone, underlies various imaging methods
from astronomy to nanoscale imaging. Traditional methods of phase retrieval are
iterative in nature, and are therefore computationally expensive and time
consuming. Mo... | [{'version': 'v1', 'created': 'Tue, 28 Sep 2021 21:16:34 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Apr 2022 15:11:33 GMT'}] | 2022-04-05 |
Tanishq Gupta, Mohd Zaki, N. M. Anoop Krishnan, Mausam | MatSciBERT: A Materials Domain Language Model for Text Mining and
Information Extraction | null | null | null | cs.CL cond-mat.mtrl-sci | An overwhelmingly large amount of knowledge in the materials domain is
generated and stored as text published in peer-reviewed scientific literature.
Recent developments in natural language processing, such as bidirectional
encoder representations from transformers (BERT) models, provide promising
tools to extract in... | [{'version': 'v1', 'created': 'Thu, 30 Sep 2021 17:35:02 GMT'}] | 2021-10-01 |
Hongyu Yu, Changsong Xu, Feng Lou, L. Bellaiche, Zhenpeng Hu, Xingao
Gong, Hongjun Xiang | Complex Spin Hamiltonian Represented by Artificial Neural Network | Phys. Rev. B 105, (2022) | 10.1103/PhysRevB.105.174422 | null | cond-mat.mtrl-sci cs.LG | The effective spin Hamiltonian method is widely adopted to simulate and
understand the behavior of magnetism. However, the magnetic interactions of
some systems, such as itinerant magnets, are too complex to be described by any
explicit function, which prevents an accurate description of magnetism in such
systems. He... | [{'version': 'v1', 'created': 'Sat, 2 Oct 2021 04:38:28 GMT'}] | 2022-05-20 |
Ravinder Bhattoo, Sayan Ranu, N. M. Anoop Krishnan | Lagrangian Neural Network with Differentiable Symmetries and Relational
Inductive Bias | null | null | null | cs.LG cond-mat.mtrl-sci cs.AI math.DS | Realistic models of physical world rely on differentiable symmetries that, in
turn, correspond to conservation laws. Recent works on Lagrangian and
Hamiltonian neural networks show that the underlying symmetries of a system can
be easily learned by a neural network when provided with an appropriate
inductive bias. Ho... | [{'version': 'v1', 'created': 'Thu, 7 Oct 2021 08:49:57 GMT'}, {'version': 'v2', 'created': 'Tue, 12 Oct 2021 04:41:08 GMT'}] | 2021-10-13 |
Aldair E. Gongora, Siddharth Mysore, Beichen Li, Wan Shou, Wojciech
Matusik, Elise F. Morgan, Keith A. Brown, Emily Whiting | Designing Composites with Target Effective Young's Modulus using
Reinforcement Learning | null | null | null | cond-mat.mtrl-sci cs.GR cs.LG | Advancements in additive manufacturing have enabled design and fabrication of
materials and structures not previously realizable. In particular, the design
space of composite materials and structures has vastly expanded, and the
resulting size and complexity has challenged traditional design methodologies,
such as br... | [{'version': 'v1', 'created': 'Thu, 7 Oct 2021 05:44:48 GMT'}] | 2021-10-12 |
Ryan Jacobs, Mingren Shen, Yuhan Liu, Wei Hao, Xiaoshan Li, Ruoyu He,
Jacob RC Greaves, Donglin Wang, Zeming Xie, Zitong Huang, Chao Wang, Kevin G.
Field, Dane Morgan | Performance, Successes and Limitations of Deep Learning Semantic
Segmentation of Multiple Defects in Transmission Electron Micrographs | null | null | null | cs.CV cond-mat.mtrl-sci | In this work, we perform semantic segmentation of multiple defect types in
electron microscopy images of irradiated FeCrAl alloys using a deep learning
Mask Regional Convolutional Neural Network (Mask R-CNN) model. We conduct an
in-depth analysis of key model performance statistics, with a focus on
quantities such as... | [{'version': 'v1', 'created': 'Fri, 15 Oct 2021 17:57:59 GMT'}] | 2021-10-18 |
Qi-Jun Hong | A melting temperature database and a neural network model for melting
temperature prediction | null | null | null | cond-mat.mtrl-sci | I build a melting temperature database that contains approximately 10,000
materials. Based on the database, I build a machine learning model that
predicts melting temperature in seconds. The model features graph neural
network and residual neural network architecture. The root-mean-square errors
of melting temperatur... | [{'version': 'v1', 'created': 'Wed, 20 Oct 2021 19:42:49 GMT'}, {'version': 'v2', 'created': 'Sat, 30 Oct 2021 00:00:48 GMT'}] | 2021-11-02 |
Baoqin Fu and Yandong Sun and Linfeng Zhang and Han Wang and Ben Xu | Deep Learning Inter-atomic Potential for Thermal and Phonon Behaviour of
Silicon Carbide with Quantum Accuracy | null | null | null | cond-mat.mtrl-sci | Silicon carbide (SiC) is an essential material for next generation
semiconductors and components for nuclear plants. It's applications are
strongly dependent on its thermal conductivity, which is highly sensitive to
microstructures. Molecular dynamics (MD) simulation is the most used methods to
address thermal transp... | [{'version': 'v1', 'created': 'Thu, 21 Oct 2021 01:00:49 GMT'}] | 2021-10-22 |
Vu Ngoc Tuoc, Nga T. T. Nguyen, Vinit Sharma, Tran Doan Huan | Probabilistic deep learning approach for targeted hybrid
organic-inorganic perovskites | Phys. Rev. Materials 5, 125402 (2021) | 10.1103/PhysRevMaterials.5.125402 | null | cond-mat.mtrl-sci | We develop a probabilistic machine learning model and use it to screen for
new hybrid organic-inorganic perovskites (HOIPs) with targeted electronic band
gap. The data set used for this work is highly diverse, containing multiple
atomic structures for each of 192 chemically distinct HOIP formulas. Therefore,
any prop... | [{'version': 'v1', 'created': 'Mon, 25 Oct 2021 13:54:16 GMT'}, {'version': 'v2', 'created': 'Tue, 7 Dec 2021 16:18:14 GMT'}] | 2021-12-08 |
Ru Yang, Yang Li, Danielle Zeng, Ping Guo | Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End
Displacement and Strain Measurement | Journal of Materials Processing Technology (2021): 117474 | 10.1016/j.jmatprotec.2021.117474 | null | eess.IV cond-mat.mtrl-sci cs.CV | Digital image correlation (DIC) has become an industry standard to retrieve
accurate displacement and strain measurement in tensile testing and other
material characterization. Though traditional DIC offers a high precision
estimation of deformation for general tensile testing cases, the prediction
becomes unstable a... | [{'version': 'v1', 'created': 'Tue, 26 Oct 2021 14:13:57 GMT'}, {'version': 'v2', 'created': 'Thu, 6 Jan 2022 20:23:24 GMT'}] | 2022-01-10 |
Kamal Choudhary, Brian DeCost, Chi Chen, Anubhav Jain, Francesca
Tavazza, Ryan Cohn, Cheol WooPark, Alok Choudhary, Ankit Agrawal, Simon J. L.
Billinge, Elizabeth Holm, Shyue Ping Ong and Chris Wolverton | Recent Advances and Applications of Deep Learning Methods in Materials
Science | null | 10.1038/s41524-022-00734-6 | null | cond-mat.mtrl-sci physics.comp-ph | Deep learning (DL) is one of the fastest growing topics in materials data
science, with rapidly emerging applications spanning atomistic, image-based,
spectral, and textual data modalities. DL allows analysis of unstructured data
and automated identification of features. Recent development of large materials
database... | [{'version': 'v1', 'created': 'Thu, 28 Oct 2021 00:09:04 GMT'}] | 2022-05-09 |
Anindya Bhaduri, Ashwini Gupta, Lori Graham-Brady | Stress field prediction in fiber-reinforced composite materials using a
deep learning approach | null | 10.1016/j.compositesb.2022.109879 | null | cond-mat.mtrl-sci cs.LG | Computational stress analysis is an important step in the design of material
systems. Finite element method (FEM) is a standard approach of performing
stress analysis of complex material systems. A way to accelerate stress
analysis is to replace FEM with a data-driven machine learning based stress
analysis approach. ... | [{'version': 'v1', 'created': 'Mon, 1 Nov 2021 01:52:27 GMT'}] | 2023-01-02 |
Van-Quyen Nguyen, Viet-Cuong Nguyen, Tien-Cuong Nguyen, Tien-Lam Pham | Pairwise interactions for Potential energy surfaces and Atomic forces
with Deep Neural network | null | null | null | cond-mat.mtrl-sci | Molecular dynamics (MD) simulation, which is considered an important tool for
studying physical and chemical processes at the atomic scale, requires accurate
calculations of energies and forces. Although reliable energies and forces can
be obtained by electronic structure calculations such as those based on density
f... | [{'version': 'v1', 'created': 'Wed, 10 Nov 2021 09:51:16 GMT'}, {'version': 'v2', 'created': 'Fri, 3 Dec 2021 08:58:58 GMT'}] | 2021-12-06 |
Andrea Pedrielli, Paolo E. Trevisanutto, Lorenzo Monacelli, Giovanni
Garberoglio, Nicola M. Pugno, Simone Taioli | Understanding Anharmonic Effects on Hydrogen Desorption Characteristics
of Mg$_n$H$_{2n}$ Nanoclusters by ab initio trained Deep Neural Network | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech cs.LG | Magnesium hydride (MgH$_2$) has been widely studied for effective hydrogen
storage. However, its bulk desorption temperature (553 K) is deemed too high
for practical applications. Besides doping, a strategy to decrease such
reaction energy for releasing hydrogen is the use of MgH$_2$-based
nanoparticles (NPs). Here, ... | [{'version': 'v1', 'created': 'Sat, 27 Nov 2021 18:33:58 GMT'}] | 2021-11-30 |
T Martinez Ostormujof (LEM3), Rrp Purushottam Raj Purohit (LEM3), S
Breumier (LEM3, IRT M2P), Nathalie Gey (LEM3), M Salib, L Germain (LEM3) | Deep Learning for automated phase segmentation in EBSD maps. A case
study in Dual Phase steel microstructures | null | null | null | cond-mat.mtrl-sci eess.IV | Electron Backscattering Diffraction (EBSD) provides important information to
discriminate phase transformation products in steels. This task is
conventionally performed by an expert, who carries a high degree of
subjectivity and requires time and effort. In this paper, we question if
Convolutional Neural Networks (CN... | [{'version': 'v1', 'created': 'Fri, 26 Nov 2021 10:05:48 GMT'}] | 2021-12-07 |
Nathan C. Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W.
Coley, Vijay Gadepally | Scalable Geometric Deep Learning on Molecular Graphs | null | null | null | cs.LG cond-mat.mtrl-sci physics.chem-ph | Deep learning in molecular and materials sciences is limited by the lack of
integration between applied science, artificial intelligence, and
high-performance computing. Bottlenecks with respect to the amount of training
data, the size and complexity of model architectures, and the scale of the
compute infrastructure... | [{'version': 'v1', 'created': 'Mon, 6 Dec 2021 21:29:38 GMT'}] | 2021-12-08 |
Yong Zhao, Edirisuriya MD Siriwardane, Jianjun Hu | Physics guided deep learning generative models for crystal materials
discovery | AAAI Fall Symposium Series (FSS) 2021 | null | null | cond-mat.mtrl-sci cs.LG | Deep learning based generative models such as deepfake have been able to
generate amazing images and videos. However, these models may need significant
transformation when applied to generate crystal materials structures in which
the building blocks, the physical atoms are very different from the pixels.
Naively tran... | [{'version': 'v1', 'created': 'Tue, 7 Dec 2021 06:54:48 GMT'}] | 2021-12-15 |
Masud Alam and Liverios Lymperakis | Artificial neural network interatomic potential for dislocation and
fracture properties of Molybdenum | null | null | null | cond-mat.mtrl-sci | A high dimensional artificial neural network interatomic potential for Mo is
developed. To train and validate the potential density functional theory
calculations on structures and properties that correlate to fracture, such as
elastic constants, surface energies, generalized stacking fault energies, and
surface deco... | [{'version': 'v1', 'created': 'Thu, 9 Dec 2021 00:45:23 GMT'}] | 2021-12-10 |
Alexander Ryabov, Petr Zhilyaev | Application of neural network for exchange-correlation functional
interpolation | null | null | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | Density functional theory (DFT) is one of the primary approaches to get a
solution to the many-body Schrodinger equation. The essential part of the DFT
theory is the exchange-correlation (XC) functional, which can not be obtained
in analytical form. Accordingly, the accuracy improvement of the DFT is mainly
based on ... | [{'version': 'v1', 'created': 'Thu, 9 Dec 2021 13:08:09 GMT'}] | 2021-12-10 |
Nathan C. Frey, Siddharth Samsi, Bharath Ramsundar, Connor W. Coley,
Vijay Gadepally | Bringing Atomistic Deep Learning to Prime Time | null | null | null | cs.LG cond-mat.mtrl-sci physics.chem-ph | Artificial intelligence has not yet revolutionized the design of materials
and molecules. In this perspective, we identify four barriers preventing the
integration of atomistic deep learning, molecular science, and high-performance
computing. We outline focused research efforts to address the opportunities
presented ... | [{'version': 'v1', 'created': 'Thu, 9 Dec 2021 15:16:46 GMT'}] | 2021-12-10 |
Daniel Gleaves, Edirisuriya M. Dilanga Siriwardane, Yong Zhao, Nihang
Fu, Jianjun Hu | Semi-supervised teacher-student deep neural network for materials
discovery | null | null | null | cond-mat.mtrl-sci cs.LG | Data driven generative machine learning models have recently emerged as one
of the most promising approaches for new materials discovery. While the
generator models can generate millions of candidates, it is critical to train
fast and accurate machine learning models to filter out stable, synthesizable
materials with... | [{'version': 'v1', 'created': 'Sun, 12 Dec 2021 04:00:21 GMT'}] | 2021-12-14 |
Jonathan M. Goodwill, Nitin Prasad, Brian D. Hoskins, Matthew W.
Daniels, Advait Madhavan, Lei Wan, Tiffany S. Santos, Michael Tran, Jordan A.
Katine, Patrick M. Braganca, Mark D. Stiles, and Jabez J. McClelland | Implementation of a Binary Neural Network on a Passive Array of Magnetic
Tunnel Junctions | Physical Review Applied, 18(1) 014039 (2022) | 10.1103/PhysRevApplied.18.014039 | null | cs.ET cond-mat.dis-nn cond-mat.mtrl-sci cs.LG physics.app-ph | The increasing scale of neural networks and their growing application space
have produced demand for more energy- and memory-efficient
artificial-intelligence-specific hardware. Avenues to mitigate the main issue,
the von Neumann bottleneck, include in-memory and near-memory architectures, as
well as algorithmic appr... | [{'version': 'v1', 'created': 'Thu, 16 Dec 2021 19:11:29 GMT'}, {'version': 'v2', 'created': 'Fri, 6 May 2022 12:48:41 GMT'}] | 2022-07-20 |
Kamal Choudhary, Taner Yildirim, Daniel Siderius, Aaron Gilad Kusne,
Austin McDannald, Diana Ortiz-Montalvo | Graph Neural Network Predictions of Metal Organic Framework CO2
Adsorption Properties | null | 10.1016/j.commatsci.2022.111388 | null | cond-mat.mtrl-sci physics.chem-ph physics.comp-ph | The increasing CO2 level is a critical concern and suitable materials are
needed to capture such gases from the environment. While experimental and
conventional computational methods are useful in finding such materials, they
are usually slow and there is a need to expedite such processes. We use
Atomistic Line Graph... | [{'version': 'v1', 'created': 'Sun, 19 Dec 2021 19:02:25 GMT'}] | 2023-01-16 |
Simiao Ren, Ashwin Mahendra, Omar Khatib, Yang Deng, Willie J. Padilla
and Jordan M. Malof | Inverse deep learning methods and benchmarks for artificial
electromagnetic material design | null | null | null | cs.LG cond-mat.mtrl-sci | Deep learning (DL) inverse techniques have increased the speed of artificial
electromagnetic material (AEM) design and improved the quality of resulting
devices. Many DL inverse techniques have succeeded on a number of AEM design
tasks, but to compare, contrast, and evaluate assorted techniques it is
critical to clar... | [{'version': 'v1', 'created': 'Sun, 19 Dec 2021 20:44:53 GMT'}] | 2021-12-21 |
Adu Offei-Danso, Ali Hassanali, Alex Rodriguez | High Dimensional Fluctuations in Liquid Water: Combining Chemical
Intuition with Unsupervised Learning | null | null | null | cond-mat.soft cond-mat.mtrl-sci | The microscopic description of the local structure of water remains an open
challenge. Here, we adopt an agnostic approach to understanding water's
hydrogen bond network using data harvested from molecular dynamics simulations
of an empirical water model. A battery of state-of-the-art unsupervised
data-science techni... | [{'version': 'v1', 'created': 'Wed, 22 Dec 2021 14:25:31 GMT'}, {'version': 'v2', 'created': 'Mon, 4 Apr 2022 16:03:23 GMT'}] | 2022-04-05 |
Pin Chen, Jianwen Chen, Hui Yan, Qing Mo, Zexin Xu, Jinyu Liu, Wenqing
Zhang, Yuedong Yang, Yutong Lu | Leveraging Large-scale Computational Database and Deep Learning for
Accurate Prediction of Material Properties | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Accurately predicting the physical and chemical properties of materials
remains one of the most challenging tasks in material design, and one effective
strategy is to construct a reliable data set and use it for training a machine
learning model. In this study, we constructed a large-scale material genome
database (M... | [{'version': 'v1', 'created': 'Wed, 29 Dec 2021 07:32:01 GMT'}] | 2021-12-30 |
Ankit Shrivastava, Jingxiao Liu, Kaushik Dayal, Hae Young Noh | Predicting Peak Stresses In Microstructured Materials Using
Convolutional Encoder-Decoder Learning | null | 10.1177/10812865211055504 | null | math.AP cond-mat.mtrl-sci cs.LG | This work presents a machine learning approach to predict peak-stress
clusters in heterogeneous polycrystalline materials. Prior work on using
machine learning in the context of mechanics has largely focused on predicting
the effective response and overall structure of stress fields. However, their
ability to predict... | [{'version': 'v1', 'created': 'Mon, 3 Jan 2022 15:51:52 GMT'}] | 2024-05-10 |
Martin Kuban and Santiago Rigamonti and Markus Scheidgen and Claudia
Draxl | Density-of-states similarity descriptor for unsupervised learning from
materials data | null | null | null | cond-mat.mtrl-sci | We develop a materials descriptor based on the electronic density of states
and investigate the similarity of materials based on it. As an application
example, we study the Computational 2D Materials Database that hosts thousands
of two-dimensional materials with their properties calculated by
density-functional theo... | [{'version': 'v1', 'created': 'Thu, 6 Jan 2022 18:52:52 GMT'}] | 2022-01-07 |
Chenru Duan, Daniel B. K. Chu, Aditya Nandy, and Heather J. Kulik | Two Wrongs Can Make a Right: A Transfer Learning Approach for Chemical
Discovery with Chemical Accuracy | null | null | null | physics.chem-ph cond-mat.mtrl-sci cs.LG | Appropriately identifying and treating molecules and materials with
significant multi-reference (MR) character is crucial for achieving high data
fidelity in virtual high throughput screening (VHTS). Nevertheless, most VHTS
is carried out with approximate density functional theory (DFT) using a single
functional. Des... | [{'version': 'v1', 'created': 'Tue, 11 Jan 2022 23:45:52 GMT'}] | 2022-01-13 |
Arda Genc, Libor Kovarik, Hamish L. Fraser | A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in
Electron Tomography of Catalytic Materials | null | null | null | cond-mat.mtrl-sci cs.LG | Heterogeneous catalysts possess complex surface and bulk structures,
relatively poor intrinsic contrast, and often a sparse distribution of the
catalytic nanoparticles (NPs), posing a significant challenge for image
segmentation, including the current state-of-the-art deep learning methods. To
tackle this problem, we... | [{'version': 'v1', 'created': 'Tue, 18 Jan 2022 22:45:19 GMT'}] | 2022-01-20 |
Haoyue Guo, Qian Wang, Alexander Urban, Nongnuch Artrith | AI-Aided Mapping of the Structure-Composition-Conductivity Relationships
of Glass-Ceramic Lithium Thiophosphate Electrolytes | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn | Lithium thiophosphates (LPS) with the composition
(Li$_2$S)$_x$(P$_2$S$_5$)$_{1-x}$ are among the most promising prospective
electrolyte materials for solid-state batteries (SSBs), owing to their
superionic conductivity at room temperature ($>10^{-3}$ S cm$^{-1}$), soft
mechanical properties, and low grain boundary r... | [{'version': 'v1', 'created': 'Wed, 26 Jan 2022 22:01:09 GMT'}] | 2022-01-28 |
Joydeep Munshi, Alexander Rakowski, Benjamin H Savitzky, Steven E
Zeltmann, Jim Ciston, Matthew Henderson, Shreyas Cholia, Andrew M Minor,
Maria KY Chan, and Colin Ophus | Disentangling multiple scattering with deep learning: application to
strain mapping from electron diffraction patterns | null | null | null | cond-mat.mtrl-sci cs.CV physics.app-ph | Implementation of a fast, robust, and fully-automated pipeline for crystal
structure determination and underlying strain mapping for crystalline materials
is important for many technological applications. Scanning electron
nanodiffraction offers a procedure for identifying and collecting strain maps
with good accurac... | [{'version': 'v1', 'created': 'Tue, 1 Feb 2022 03:53:39 GMT'}] | 2022-02-02 |
Chi Chen and Shyue Ping Ong | A Universal Graph Deep Learning Interatomic Potential for the Periodic
Table | null | 10.1038/s43588-022-00349-3 | null | cond-mat.mtrl-sci physics.chem-ph | Interatomic potentials (IAPs), which describe the potential energy surface of
atoms, are a fundamental input for atomistic simulations. However, existing
IAPs are either fitted to narrow chemistries or too inaccurate for general
applications. Here, we report a universal IAP for materials based on graph
neural network... | [{'version': 'v1', 'created': 'Sat, 5 Feb 2022 01:26:38 GMT'}, {'version': 'v2', 'created': 'Sun, 14 Aug 2022 22:46:23 GMT'}] | 2022-12-06 |
Rama K. Vasudevan, Erick Orozco, Sergei V. Kalinin | Discovering mechanisms for materials microstructure optimization via
reinforcement learning of a generative model | null | null | null | cond-mat.mtrl-sci cond-mat.mes-hall | The design of materials structure for optimizing functional properties and
potentially, the discovery of novel behaviors is a keystone problem in
materials science. In many cases microstructural models underpinning materials
functionality are available and well understood. However, optimization of
average properties ... | [{'version': 'v1', 'created': 'Tue, 22 Feb 2022 15:44:51 GMT'}] | 2022-02-23 |
Thomas Friedrich, Chu-Ping Yu, Jo Verbeeck, Sandra Van Aert | Phase Object Reconstruction for 4D-STEM using Deep Learning | null | 10.1093/micmic/ozac002 | null | cond-mat.mtrl-sci eess.IV | In this study we explore the possibility to use deep learning for the
reconstruction of phase images from 4D scanning transmission electron
microscopy (4D-STEM) data. The process can be divided into two main steps.
First, the complex electron wave function is recovered for a convergent beam
electron diffraction patte... | [{'version': 'v1', 'created': 'Fri, 25 Feb 2022 10:59:56 GMT'}, {'version': 'v2', 'created': 'Tue, 30 Aug 2022 21:11:30 GMT'}] | 2023-02-15 |
Qiyu Zeng, Bo Chen, Xiaoxiang Yu, Shen Zhang, Dongdong Kang, Han Wang,
and Jiayu Dai | Towards Large-Scale and Spatio-temporally Resolved Diagnosis of
Electronic Density of States by Deep Learning | Phys. Rev. B 105: 174109 (2022) | 10.1103/PhysRevB.105.174109 | null | physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci physics.atm-clus | Modern laboratory techniques like ultrafast laser excitation and shock
compression can bring matter into highly nonequilibrium states with complex
structural transformation, metallization and dissociation dynamics. To
understand and model the dramatic change of both electronic structures and ion
dynamics during such ... | [{'version': 'v1', 'created': 'Wed, 9 Mar 2022 02:21:41 GMT'}, {'version': 'v2', 'created': 'Thu, 12 May 2022 00:19:52 GMT'}] | 2022-05-24 |
Saba Kharabadze, Aidan Thorn, Ekaterina A. Koulakova, and Aleksey N.
Kolmogorov | Prediction of stable Li-Sn compounds: boosting ab initio searches with
neural network potentials | npj Computational Materials volume 8, Article number: 136 (2022) | 10.1038/s41524-022-00825-4 | null | cond-mat.mtrl-sci physics.comp-ph | The Li-Sn binary system has been the focus of extensive research because it
features Li-rich alloys with potential applications as battery anodes. Our
present re-examination of the binary system with a combination of machine
learning and ab initio methods has allowed us to screen a vast configuration
space and uncove... | [{'version': 'v1', 'created': 'Fri, 11 Mar 2022 23:34:23 GMT'}] | 2022-08-09 |
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