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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Z. Suszynski, M. Kosikowski, R. Duer | The application of Artificial Neural Network for the assessment of
thermal properties of multi-layer semiconductor structure | Dans Proceedings of 12th International Workshop on Thermal
investigations of ICs - THERMINIC 2006, Nice : France (2006) | null | null | cond-mat.mtrl-sci | In this paper, the solution of the problem of identification of thermal
properties of investigated multi-layer structure is presented. In order of
that, artificial neural network was used to find the set of thermal properties
for which the complex contrast characteric derived fits the best to the one
evaluated basing... | [{'version': 'v1', 'created': 'Wed, 12 Sep 2007 13:30:55 GMT'}] | 2007-09-13 |
Gabriele C. Sosso (1), Giacomo Miceli (1), Sebastiano Caravati (2),
J\"org Behler (3), and Marco Bernasconi (1) ((1) Dipartimento di Scienza dei
Materiali, Universit\`a di Milano-Bicocca, Milano, Italy, (2) Computational
Science, Department of Chemistry and Applied Biosciences ETH Zurich, USI
Campus, Lugano, Sw... | A neural network interatomic potential for the phase change material
GeTe | Phys. Rev. B 85, 174103 (2012) | 10.1103/PhysRevB.85.174103 | null | cond-mat.mtrl-sci cond-mat.dis-nn | GeTe is a prototypical phase change material of high interest for
applications in optical and electronic non-volatile memories. We present an
interatomic potential for the bulk phases of GeTe, which is created using a
neural network (NN) representation of the potential-energy surface obtained
from reference calculati... | [{'version': 'v1', 'created': 'Tue, 10 Jan 2012 11:35:59 GMT'}] | 2012-08-02 |
Francesco Bonanno, Giacomo Capizzi, Grazia Lo Sciuto, Christian
Napoli, Giuseppe Pappalardo, Emiliano Tramontana | A Cascade Neural Network Architecture investigating Surface Plasmon
Polaritons propagation for thin metals in OpenMP | International conference on Artificial Intelligence and Soft
Computing (ICAISC 2014), Vol I, 22-33 (2014) | null | null | cs.NE cond-mat.mes-hall cond-mat.mtrl-sci cs.DC cs.LG | Surface plasmon polaritons (SPPs) confined along metal-dielectric interface
have attracted a relevant interest in the area of ultracompact photonic
circuits, photovoltaic devices and other applications due to their strong field
confinement and enhancement. This paper investigates a novel cascade neural
network (NN) a... | [{'version': 'v1', 'created': 'Thu, 12 Jun 2014 08:40:04 GMT'}] | 2014-06-13 |
S. Alireza Ghasemi, Albert Hofstetter, Santanu Saha, Stefan Goedecker | Interatomic potentials for ionic systems with density functional
accuracy based on charge densities obtained by a neural network | Phys. Rev. B 92, 045131 (2015) | 10.1103/PhysRevB.92.045131 | null | cond-mat.mtrl-sci physics.chem-ph | Based on an analysis of the short range chemical environment of each atom in
a system, standard machine learning based approaches to the construction of
interatomic potentials aim at determining directly the central quantity which
is the total energy. This prevents for instance an accurate description of the
energeti... | [{'version': 'v1', 'created': 'Thu, 29 Jan 2015 05:19:01 GMT'}] | 2015-08-05 |
Samad Hajinazar, Junping Shao, Aleksey N. Kolmogorov | Stratified construction of neural network based interatomic models for
multicomponent materials | Phys. Rev. B 95, 014114 (2017) | 10.1103/PhysRevB.95.014114 | null | cond-mat.mtrl-sci | Recent application of neural networks (NNs) to modeling interatomic
interactions has shown the learning machines' encouragingly accurate
performance for select elemental and multicomponent systems. In this study, we
explore the possibility of building a library of NN-based models by introducing
a hierarchical NN trai... | [{'version': 'v1', 'created': 'Tue, 27 Sep 2016 14:06:16 GMT'}, {'version': 'v2', 'created': 'Wed, 1 Feb 2017 01:15:44 GMT'}] | 2017-02-08 |
P. Anees, M. C. Valsakumar and B. K. Panigrahi | Delineating the role of ripples on thermal expansion of honeycomb
materials:graphene, 2D-h-BN and monolayer(ML)-MoS2 | Phys. Chem. Chem. Phys., 19, 10518, (2017) | 10.1039/C6CP08635G | null | cond-mat.mtrl-sci | We delineated the role of thermally excited ripples on thermal expansion
properties of 2D honeycomb materials (free-standing graphene, 2D h-BN, and
ML-MoS2), by explicitly carrying out three-dimensional (3D) and two-dimensional
(2D) molecular dynamics simulations. In 3D simulations, the in-plane lattice
parameter (a-... | [{'version': 'v1', 'created': 'Tue, 25 Oct 2016 08:43:49 GMT'}] | 2017-07-25 |
Daniel Valencia, Evan Wilson, Zhengping Jiang, Gustavo A.
Valencia-Zapata, Gerhard Klimeck and Michael Povolotskyi | Grain Boundary Resistance in Copper Interconnects from an Atomistic
Model to a Neural Network | Phys. Rev. Applied 9, 044005 (2018) | 10.1103/PhysRevApplied.9.044005 | null | cond-mat.mtrl-sci | Orientation effects on the resistivity of copper grain boundaries are studied
systematically with two different atomistic tight binding methods. A
methodology is developed to model the resistivity of grain boundaries using the
Embedded Atom Model, tight binding methods and non-equilibrum Green's functions
(NEGF). The... | [{'version': 'v1', 'created': 'Tue, 17 Jan 2017 23:24:10 GMT'}, {'version': 'v2', 'created': 'Sat, 4 Feb 2017 04:16:11 GMT'}, {'version': 'v3', 'created': 'Sun, 8 Oct 2017 20:03:43 GMT'}] | 2018-04-11 |
Kyle Mills, Michael Spanner, and Isaac Tamblyn | Deep learning and the Schr\"odinger equation | Phys. Rev. A 96, 042113 (2017) | 10.1103/PhysRevA.96.042113 | null | cond-mat.mtrl-sci cs.LG physics.chem-ph | We have trained a deep (convolutional) neural network to predict the
ground-state energy of an electron in four classes of confining two-dimensional
electrostatic potentials. On randomly generated potentials, for which there is
no analytic form for either the potential or the ground-state energy, the
neural network m... | [{'version': 'v1', 'created': 'Sun, 5 Feb 2017 02:58:58 GMT'}, {'version': 'v2', 'created': 'Thu, 8 Jun 2017 20:39:27 GMT'}, {'version': 'v3', 'created': 'Fri, 3 Nov 2017 13:10:51 GMT'}] | 2017-11-06 |
Seyed Majid Azimi, Dominik Britz, Michael Engstler, Mario Fritz, Frank
M\"ucklich | Advanced Steel Microstructural Classification by Deep Learning Methods | null | 10.1038/s41598-018-20037-5 | null | cs.CV cond-mat.mtrl-sci | The inner structure of a material is called microstructure. It stores the
genesis of a material and determines all its physical and chemical properties.
While microstructural characterization is widely spread and well known, the
microstructural classification is mostly done manually by human experts, which
gives rise... | [{'version': 'v1', 'created': 'Tue, 20 Jun 2017 14:29:42 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Feb 2018 14:30:16 GMT'}] | 2018-02-16 |
Weizong Xu and James M. LeBeau | A Deep Convolutional Neural Network to Analyze Position Averaged
Convergent Beam Electron Diffraction Patterns | null | 10.1016/j.ultramic.2018.03.004 | null | physics.data-an cond-mat.mtrl-sci | We establish a series of deep convolutional neural networks to automatically
analyze position averaged convergent beam electron diffraction patterns. The
networks first calibrate the zero-order disk size, center position, and
rotation without the need for pretreating the data. With the aligned data,
additional networ... | [{'version': 'v1', 'created': 'Thu, 3 Aug 2017 14:38:30 GMT'}] | 2018-06-05 |
A. Ziletti, D. Kumar, M. Scheffler, L. M. Ghiringhelli | Insightful classification of crystal structures using deep learning | Nature Communications 9, 2775 (2018) | 10.1038/s41467-018-05169-6 | null | cond-mat.mtrl-sci cond-mat.dis-nn | Computational methods that automatically extract knowledge from data are
critical for enabling data-driven materials science. A reliable identification
of lattice symmetry is a crucial first step for materials characterization and
analytics. Current methods require a user-specified threshold, and are unable
to detect... | [{'version': 'v1', 'created': 'Thu, 7 Sep 2017 15:09:27 GMT'}, {'version': 'v2', 'created': 'Wed, 30 May 2018 06:11:23 GMT'}] | 2018-07-19 |
M. Carrillo, J. A. Gonz\'alez, S. Hern\'andez-Ortiz, C. E. L\'opez, A.
Raya | Bloch oscillations in graphene from an artificial neural network study | null | null | null | cond-mat.mtrl-sci cond-mat.mes-hall | We develop an artificial neural network (ANN) approach to classify simulated
signals corrsponding to the semi-classical description of Bloch oscillations in
pristine graphene. After the ANN is properly trained, we consider the inverse
problem of Bloch oscillations (BO),namely, a new signal is classified according
to ... | [{'version': 'v1', 'created': 'Wed, 4 Oct 2017 17:46:07 GMT'}] | 2017-10-05 |
Adrien Bouhon and Annica M. Black-Schaffer | Bulk topology of line-nodal structures protected by space group
symmetries in class AI | null | null | null | cond-mat.mtrl-sci | We give an exhaustive characterization of the topology of band structures in
class AI, using nonsymmorphic space group 33 ($Pna2_1$) as a representative
example where a great variety of symmetry protected line-nodal structures can
be formed. We start with the topological classification of all line-nodal
structures gi... | [{'version': 'v1', 'created': 'Fri, 13 Oct 2017 11:10:07 GMT'}] | 2017-10-16 |
Maxwell L. Hutchinson, Erin Antono, Brenna M. Gibbons, Sean Paradiso,
Julia Ling, Bryce Meredig | Overcoming data scarcity with transfer learning | null | null | null | cs.LG cond-mat.mtrl-sci stat.ML | Despite increasing focus on data publication and discovery in materials
science and related fields, the global view of materials data is highly sparse.
This sparsity encourages training models on the union of multiple datasets, but
simple unions can prove problematic as (ostensibly) equivalent properties may
be measu... | [{'version': 'v1', 'created': 'Thu, 2 Nov 2017 12:54:51 GMT'}] | 2017-11-15 |
Eric Gossett, Cormac Toher, Corey Oses, Olexandr Isayev, Fleur
Legrain, Frisco Rose, Eva Zurek, Jes\'us Carrete, Natalio Mingo, Alexander
Tropsha, Stefano Curtarolo | AFLOW-ML: A RESTful API for machine-learning predictions of materials
properties | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Machine learning approaches, enabled by the emergence of comprehensive
databases of materials properties, are becoming a fruitful direction for
materials analysis. As a result, a plethora of models have been constructed and
trained on existing data to predict properties of new systems. These powerful
methods allow re... | [{'version': 'v1', 'created': 'Wed, 29 Nov 2017 09:35:46 GMT'}] | 2017-11-30 |
Ruijin Cang, Hechao Li, Hope Yao, Yang Jiao, Yi Ren | Improving Direct Physical Properties Prediction of Heterogeneous
Materials from Imaging Data via Convolutional Neural Network and a
Morphology-Aware Generative Model | null | null | null | physics.comp-ph cond-mat.mtrl-sci | Direct prediction of material properties from microstructures through
statistical models has shown to be a potential approach to accelerating
computational material design with large design spaces. However, statistical
modeling of highly nonlinear mappings defined on high-dimensional
microstructure spaces is known to... | [{'version': 'v1', 'created': 'Thu, 7 Dec 2017 06:49:29 GMT'}] | 2017-12-12 |
Kristof T. Sch\"utt, Huziel E. Sauceda, Pieter-Jan Kindermans,
Alexandre Tkatchenko, Klaus-Robert M\"uller | SchNet - a deep learning architecture for molecules and materials | null | 10.1063/1.5019779 | null | physics.chem-ph cond-mat.mtrl-sci | Deep learning has led to a paradigm shift in artificial intelligence,
including web, text and image search, speech recognition, as well as
bioinformatics, with growing impact in chemical physics. Machine learning in
general and deep learning in particular is ideally suited for representing
quantum-mechanical interact... | [{'version': 'v1', 'created': 'Sun, 17 Dec 2017 13:55:03 GMT'}, {'version': 'v2', 'created': 'Wed, 7 Mar 2018 12:42:19 GMT'}, {'version': 'v3', 'created': 'Thu, 22 Mar 2018 11:12:43 GMT'}] | 2018-04-18 |
Maxim Ziatdinov, Ondrej Dyck, Artem Maksov, Xufan Li, Xiahan Sang, Kai
Xiao, Raymond R. Unocic, Rama Vasudevan, Stephen Jesse, Sergei V. Kalinin | Deep Learning of Atomically Resolved Scanning Transmission Electron
Microscopy Images: Chemical Identification and Tracking Local Transformations | ACS Nano, 2017, 11 (12), pp 12742-12752 | 10.1021/acsnano.7b07504 | null | cond-mat.mtrl-sci | Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size a... | [{'version': 'v1', 'created': 'Wed, 17 Jan 2018 20:45:52 GMT'}] | 2018-01-19 |
Jacob Madsen, Pei Liu, Jens Kling, Jakob Birkedal Wagner, Thomas
Willum Hansen, Ole Winther, Jakob Schi{\o}tz | A deep learning approach to identify local structures in
atomic-resolution transmission electron microscopy images | Adv. Theory Simul. 1, 1800037 (2018) | 10.1002/adts.201800037 | null | cond-mat.mtrl-sci | Recording atomic-resolution transmission electron microscopy (TEM) images is
becoming increasingly routine. A new bottleneck is then analyzing this
information, which often involves time-consuming manual structural
identification. We have developed a deep learning-based algorithm for
recognition of the local structur... | [{'version': 'v1', 'created': 'Thu, 8 Feb 2018 18:57:20 GMT'}, {'version': 'v2', 'created': 'Fri, 9 Feb 2018 10:43:02 GMT'}] | 2018-09-13 |
Rama K. Vasudevan, Nouamane Laanait, Erik M. Ferragut, Kai Wang, David
B. Geohegan, Kai Xiao, Maxim A. Ziatdinov, Stephen Jesse, Ondrej E. Dyck,
Sergei V. Kalinin | Mapping mesoscopic phase evolution during e-beam induced transformations
via deep learning of atomically resolved images | null | null | null | cond-mat.mtrl-sci | Understanding transformations under electron beam irradiation requires
mapping the structural phases and their evolution in real time. To date, this
has mostly been a manual endeavor comprising of difficult frame-by-frame
analysis that is simultaneously tedious and prone to error. Here, we turn
towards the use of dee... | [{'version': 'v1', 'created': 'Wed, 28 Feb 2018 16:27:35 GMT'}] | 2018-03-01 |
B.D. Conduit, N.G. Jones, H.J. Stone, G.J. Conduit | Probabilistic design of a molybdenum-base alloy using a neural network | Scripta Materialia 146, 82 (2018) | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph | An artificial intelligence tool is exploited to discover and characterize a
new molybdenum-base alloy that is the most likely to simultaneously satisfy
targets of cost, phase stability, precipitate content, yield stress, and
hardness. Experimental testing demonstrates that the proposed alloy fulfils the
computational... | [{'version': 'v1', 'created': 'Fri, 2 Mar 2018 15:11:49 GMT'}] | 2018-03-05 |
Youngjun Cho, Nadia Bianchi-Berthouze, Nicolai Marquardt and Simon J.
Julier | Deep Thermal Imaging: Proximate Material Type Recognition in the Wild
through Deep Learning of Spatial Surface Temperature Patterns | null | 10.1145/3173574.3173576 | null | cs.CV cond-mat.mtrl-sci cs.HC cs.LG | We introduce Deep Thermal Imaging, a new approach for close-range automatic
recognition of materials to enhance the understanding of people and ubiquitous
technologies of their proximal environment. Our approach uses a low-cost mobile
thermal camera integrated into a smartphone to capture thermal textures. A deep
neu... | [{'version': 'v1', 'created': 'Tue, 6 Mar 2018 17:29:08 GMT'}] | 2018-03-28 |
B.D. Conduit, N.G. Jones, H.J. Stone, and G.J. Conduit | Design of a nickel-base superalloy using a neural network | Materials & Design 131, 358 (2017) | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph | A new computational tool has been developed to model, discover, and optimize
new alloys that simultaneously satisfy up to eleven physical criteria. An
artificial neural network is trained from pre-existing materials data that
enables the prediction of individual material properties both as a function of
composition a... | [{'version': 'v1', 'created': 'Thu, 8 Mar 2018 11:04:58 GMT'}] | 2018-03-09 |
Artem Maksov, Ondrej Dyck, Kai Wang, Kai Xiao, David B. Geohegan,
Bobby G. Sumpter, Rama K. Vasudevan, Stephen Jesse, Sergei V. Kalinin, Maxim
Ziatdinov | Deep Learning Analysis of Defect and Phase Evolution During Electron
Beam Induced Transformations in WS2 | npj Computational Materials 5, Article number: 12 (2019) | 10.1038/s41524-019-0152-9 | null | cond-mat.mtrl-sci | Understanding elementary mechanisms behind solid-state phase transformations
and reactions is the key to optimizing desired functional properties of many
technologically relevant materials. Recent advances in scanning transmission
electron microscopy (STEM) allow the real-time visualization of solid-state
transformat... | [{'version': 'v1', 'created': 'Wed, 14 Mar 2018 16:16:21 GMT'}, {'version': 'v2', 'created': 'Thu, 15 Mar 2018 05:29:36 GMT'}, {'version': 'v3', 'created': 'Thu, 16 Aug 2018 06:54:26 GMT'}] | 2019-02-05 |
Xiaolin Li, Yichi Zhang, He Zhao, Craig Burkhart, L Catherine Brinson,
Wei Chen | A Transfer Learning Approach for Microstructure Reconstruction and
Structure-property Predictions | null | null | null | cond-mat.mtrl-sci cs.CE physics.comp-ph | Stochastic microstructure reconstruction has become an indispensable part of
computational materials science, but ongoing developments are specific to
particular material systems. In this paper, we address this generality problem
by presenting a transfer learning-based approach for microstructure
reconstruction and s... | [{'version': 'v1', 'created': 'Tue, 8 May 2018 00:01:48 GMT'}] | 2018-05-09 |
Y. Liu, Q. M. Sun, Dr. W. H. Lu, Dr. H. L. Wang, Y. Sun, Z. T. Wang,
X. Lu, Prof. K. Y. Zeng | General Resolution Enhancement Method in Atomic Force Microscopy (AFM)
Using Deep Learning | null | null | null | physics.data-an cond-mat.mtrl-sci | This paper develops a resolution enhancement method for post-processing the
images from Atomic Force Microscopy (AFM). This method is based on deep
learning neural networks in the AFM topography measurements. In this study, a
very deep convolution neural network is developed to derive the high-resolution
topography i... | [{'version': 'v1', 'created': 'Tue, 11 Sep 2018 07:09:14 GMT'}] | 2018-09-12 |
Yuan Dong, Chuhan Wu, Chi Zhang, Yingda Liu, Jianlin Cheng and Jian
Lin | Deep Learning Bandgaps of Topologically Doped Graphene | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Manipulation of material properties via precise doping affords enormous
tunable phenomena to explore. Recent advance shows that in the atomic and nano
scales topological states of dopants play crucial roles in determining their
properties. However, such determination is largely unknown due to the
incredible size of t... | [{'version': 'v1', 'created': 'Fri, 28 Sep 2018 05:02:49 GMT'}] | 2018-10-01 |
Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek
Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar | MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with
Multitask Learning for Material Property Prediction | null | null | null | cs.LG cond-mat.mtrl-sci stat.ML | Developing accurate, transferable and computationally inexpensive machine
learning models can rapidly accelerate the discovery and development of new
materials. Some of the major challenges involved in developing such models are,
(i) limited availability of materials data as compared to other fields, (ii)
lack of uni... | [{'version': 'v1', 'created': 'Wed, 14 Nov 2018 06:13:29 GMT'}] | 2018-11-15 |
Kevin Ryczko, David Strubbe, Isaac Tamblyn | Deep Learning and Density Functional Theory | Phys. Rev. A 100, 022512 (2019) | 10.1103/PhysRevA.100.022512 | null | cond-mat.mtrl-sci physics.comp-ph | We show that deep neural networks can be integrated into, or fully replace,
the Kohn-Sham density functional theory scheme for multi-electron systems in
simple harmonic oscillator and random external potentials with no feature
engineering. We first show that self-consistent charge densities calculated
with different ... | [{'version': 'v1', 'created': 'Wed, 21 Nov 2018 20:03:01 GMT'}, {'version': 'v2', 'created': 'Wed, 24 Feb 2021 15:13:46 GMT'}] | 2021-02-25 |
Rahul Singh, Aayush Sharma, Onur Rauf Bingol, Aditya Balu, Ganesh
Balasubramanian, Duane D. Johnson and Soumik Sarkar | 3D Deep Learning with voxelized atomic configurations for modeling
atomistic potentials in complex solid-solution alloys | null | null | null | cond-mat.mtrl-sci cs.LG physics.comp-ph stat.ML | The need for advanced materials has led to the development of complex,
multi-component alloys or solid-solution alloys. These materials have shown
exceptional properties like strength, toughness, ductility, electrical and
electronic properties. Current development of such material systems are
hindered by expensive ex... | [{'version': 'v1', 'created': 'Fri, 23 Nov 2018 23:12:22 GMT'}] | 2018-11-27 |
Tomohiko Konno, Hodaka Kurokawa, Fuyuki Nabeshima, Yuki Sakishita, Ryo
Ogawa, Iwao Hosako, Atsutaka Maeda | Deep Learning Model for Finding New Superconductors | Phys. Rev. B 103, 014509 (2021) | 10.1103/PhysRevB.103.014509 | null | cs.LG cond-mat.mtrl-sci cond-mat.supr-con cs.CL physics.comp-ph | Exploration of new superconductors still relies on the experience and
intuition of experts and is largely a process of experimental trial and error.
In one study, only 3% of the candidate materials showed superconductivity.
Here, we report the first deep learning model for finding new superconductors.
We introduced t... | [{'version': 'v1', 'created': 'Mon, 3 Dec 2018 05:30:34 GMT'}, {'version': 'v2', 'created': 'Mon, 3 Jun 2019 07:22:53 GMT'}, {'version': 'v3', 'created': 'Sun, 3 Nov 2019 14:29:01 GMT'}, {'version': 'v4', 'created': 'Thu, 14 Jan 2021 14:36:38 GMT'}] | 2021-01-20 |
Myungjoon Kim, Byung Chul Yeo, Sang Soo Han, Donghun Kim | Slab Graph Convolutional Neural Network for Discovery of N2
Electroreduction Catalysts | null | null | null | cond-mat.mtrl-sci | The catalyst development for N2 electroreduction reaction (NRR) with low
onset potential and high Faradaic efficiency is highly desired, but remains
challenging. Machine learning (ML) recently emerged as a complementary tool to
accelerate material discovery; however a ML model for NRR has yet to be
developed. Here, w... | [{'version': 'v1', 'created': 'Fri, 7 Dec 2018 08:50:31 GMT'}, {'version': 'v2', 'created': 'Wed, 27 Mar 2019 01:07:09 GMT'}] | 2019-03-28 |
Shweta Mehta, Sheena Agarwal, and Kavita Joshi | Combining DFT with ML to study size specific interactions between metal
clusters and adsorbates | null | null | null | cond-mat.mtrl-sci | To date, density functional theory (DFT) is one of the most accurate and yet
practical theory to gain insight about materials properties. Although
successful, the computational cost is the main hurdle even today. A way out is
combining DFT with machine learning (ML) to reduce the computational cost
without compromisi... | [{'version': 'v1', 'created': 'Wed, 12 Dec 2018 13:21:40 GMT'}, {'version': 'v2', 'created': 'Wed, 2 Jan 2019 05:22:03 GMT'}, {'version': 'v3', 'created': 'Tue, 9 Apr 2019 10:00:00 GMT'}, {'version': 'v4', 'created': 'Thu, 18 Apr 2019 06:46:13 GMT'}] | 2019-04-19 |
Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey
C. Grossman | Graph Dynamical Networks for Unsupervised Learning of Atomic Scale
Dynamics in Materials | Nat. Commun. 10, 2667 (2019) | 10.1038/s41467-019-10663-6 | null | cond-mat.mtrl-sci cs.LG physics.chem-ph | Understanding the dynamical processes that govern the performance of
functional materials is essential for the design of next generation materials
to tackle global energy and environmental challenges. Many of these processes
involve the dynamics of individual atoms or small molecules in condensed
phases, e.g. lithium... | [{'version': 'v1', 'created': 'Mon, 18 Feb 2019 23:17:27 GMT'}, {'version': 'v2', 'created': 'Wed, 22 May 2019 20:58:39 GMT'}] | 2019-07-11 |
Nouamane Laanait and Qian He and Albina Y. Borisevich | Reconstruction of 3-D Atomic Distortions from Electron Microscopy with
Deep Learning | null | null | null | cond-mat.mtrl-sci cs.LG | Deep learning has demonstrated superb efficacy in processing imaging data,
yet its suitability in solving challenging inverse problems in scientific
imaging has not been fully explored. Of immense interest is the determination
of local material properties from atomically-resolved imaging, such as electron
microscopy,... | [{'version': 'v1', 'created': 'Tue, 19 Feb 2019 03:31:53 GMT'}] | 2019-02-20 |
Mohammad Rashidi, Jeremiah Croshaw, Kieran Mastel, Marcus Tamura,
Hedieh Hosseinzadeh, and Robert A. Wolkow | Deep Learning-Guided Surface Characterization for Autonomous Hydrogen
Lithography | Mach. Learn.: Sci. Technol. 1 025001 (2020) | 10.1088/2632-2153/ab6d5e | null | cond-mat.mtrl-sci | As the development of atom scale devices transitions from novel,
proof-of-concept demonstrations to state-of-the-art commercial applications,
automated assembly of such devices must be implemented. Here we present an
automation method for the identification of defects prior to atomic fabrication
via hydrogen lithogra... | [{'version': 'v1', 'created': 'Sat, 23 Feb 2019 17:37:28 GMT'}, {'version': 'v2', 'created': 'Fri, 11 Oct 2019 18:57:15 GMT'}] | 2020-03-26 |
Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong, Satoshi Watanabe, Seungwu Han | Atomic energy mapping of neural network potential | Phys. Rev. Materials 3, 093802 (2019) | 10.1103/PhysRevMaterials.3.093802 | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | We show that the intelligence of the machine-learning potential arises from
its ability to infer the reference atomic-energy function from a given set of
total energies. By utilizing invariant points in the feature space at which the
atomic energy has a fixed reference value, we examine the atomic energy mapping
of n... | [{'version': 'v1', 'created': 'Mon, 11 Mar 2019 15:21:01 GMT'}] | 2019-09-05 |
Sandeep Madireddy, Ding-Wen Chung, Troy Loeffler, Subramanian K.R.S.
Sankaranarayanan, David N. Seidman, Prasanna Balaprakash, and Olle Heinonen | Phase Segmentation in Atom-Probe Tomography Using Deep Learning-Based
Edge Detection | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Atom-probe tomography (APT) facilitates nano- and atomic-scale
characterization and analysis of microstructural features. Specifically, APT is
well suited to study the interfacial properties of granular or heterophase
systems. Traditionally, the identification of the interface between, for
precipitate and matrix phas... | [{'version': 'v1', 'created': 'Wed, 10 Apr 2019 20:53:33 GMT'}] | 2019-04-12 |
Jutta Rogal, Elia Schneider, Mark E. Tuckerman | Neural network based path collective variables for enhanced sampling of
phase transformations | Phys. Rev. Lett. 123, 245701 (2019) | 10.1103/PhysRevLett.123.245701 | null | cond-mat.mtrl-sci cond-mat.stat-mech physics.comp-ph | We propose a rigorous construction of a 1D path collective variable to sample
structural phase transformations in condensed matter. The path collective
variable is defined in a space spanned by global collective variables that
serve as classifiers derived from local structural units. A reliable
identification of loca... | [{'version': 'v1', 'created': 'Sat, 4 May 2019 18:05:54 GMT'}] | 2022-12-09 |
Liang Li, Mindren Lu, and Maria K. Y. Chan | A Deep Learning Model for Atomic Structures Prediction Using X-ray
Absorption Spectroscopic Data | null | null | null | physics.comp-ph cond-mat.mtrl-sci | A deep neural network (DNN) model consisting of two hidden layers was
proposed for predicting the immediate environments of specific atoms based on
X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be
adjusted to form a classifier or regressor, to predict the local and overall
coordination e... | [{'version': 'v1', 'created': 'Fri, 10 May 2019 04:08:40 GMT'}] | 2019-05-13 |
Emi Minamitani, Masayoshi Ogura, Satoshi Watanabe | Simulating lattice thermal conductivity in semiconducting materials
using high-dimensional neural network potential | null | 10.7567/1882-0786/ab36bc | null | cond-mat.mtrl-sci physics.comp-ph | We demonstrate that a high-dimensional neural network potential (HDNNP) can
predict the lattice thermal conductivity of semiconducting materials with an
accuracy comparable to that of density functional theory (DFT) calculation.
After a training procedure based on the force, the root mean square error
between the for... | [{'version': 'v1', 'created': 'Tue, 21 May 2019 09:13:33 GMT'}] | 2019-08-16 |
Brian Gallagher, Matthew Rever, Donald Loveland, T. Nathan Mundhenk,
Brock Beauchamp, Emily Robertson, Golam G. Jaman, Anna M. Hiszpanski, and T.
Yong-Jin Han | Predicting Compressive Strength of Consolidated Molecular Solids Using
Computer Vision and Deep Learning | null | 10.1016/j.matdes.2020.108541 | null | physics.comp-ph cond-mat.mtrl-sci | We explore the application of computer vision and machine learning (ML)
techniques to predict material properties (e.g. compressive strength) based on
SEM images. We show that it's possible to train ML models to predict materials
performance based on SEM images alone, demonstrating this capability on the
real-world p... | [{'version': 'v1', 'created': 'Wed, 5 Jun 2019 16:49:00 GMT'}, {'version': 'v2', 'created': 'Sat, 9 Nov 2019 04:52:14 GMT'}, {'version': 'v3', 'created': 'Fri, 28 Feb 2020 01:54:33 GMT'}] | 2020-03-02 |
Cheol Woo Park, Chris Wolverton | Developing an improved Crystal Graph Convolutional Neural Network
framework for accelerated materials discovery | Phys. Rev. Materials 4, 063801 (2020) | 10.1103/PhysRevMaterials.4.063801 | null | physics.comp-ph cond-mat.mtrl-sci physics.data-an | The recently proposed crystal graph convolutional neural network (CGCNN)
offers a highly versatile and accurate machine learning (ML) framework by
learning material properties directly from graph-like representations of
crystal structures ("crystal graphs"). Here, we develop an improved variant of
the CGCNN model (iC... | [{'version': 'v1', 'created': 'Wed, 12 Jun 2019 17:47:43 GMT'}] | 2020-07-01 |
Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, Weinan E, Roberto Car | Deep neural network for the dielectric response of insulators | Phys. Rev. B 102, 041121 (2020) | 10.1103/PhysRevB.102.041121 | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | We introduce a deep neural network to model in a symmetry preserving way the
environmental dependence of the centers of the electronic charge. The model
learns from ab-initio density functional theory, wherein the electronic centers
are uniquely assigned by the maximally localized Wannier functions. When
combined wit... | [{'version': 'v1', 'created': 'Thu, 27 Jun 2019 04:44:07 GMT'}, {'version': 'v2', 'created': 'Tue, 2 Jul 2019 14:45:58 GMT'}, {'version': 'v3', 'created': 'Sat, 7 Sep 2019 05:11:51 GMT'}, {'version': 'v4', 'created': 'Mon, 3 Feb 2020 11:58:17 GMT'}, {'version': 'v5', 'created': 'Tue, 9 Jun 2020 22:38:05 GMT'}] | 2020-07-29 |
Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli | PANNA: Properties from Artificial Neural Network Architectures | null | 10.1016/j.cpc.2020.107402 | null | physics.comp-ph cond-mat.mtrl-sci | Prediction of material properties from first principles is often a
computationally expensive task. Recently, artificial neural networks and other
machine learning approaches have been successfully employed to obtain accurate
models at a low computational cost by leveraging existing example data. Here,
we present a so... | [{'version': 'v1', 'created': 'Sat, 6 Jul 2019 00:42:46 GMT'}] | 2020-07-15 |
Kirk Swanson, Shubhendu Trivedi, Joshua Lequieu, Kyle Swanson, Risi
Kondor | Deep Learning for Automated Classification and Characterization of
Amorphous Materials | null | null | null | cond-mat.soft cond-mat.dis-nn cond-mat.mtrl-sci cs.LG stat.ML | It is difficult to quantify structure-property relationships and to identify
structural features of complex materials. The characterization of amorphous
materials is especially challenging because their lack of long-range order
makes it difficult to define structural metrics. In this work, we apply deep
learning algo... | [{'version': 'v1', 'created': 'Tue, 10 Sep 2019 17:49:04 GMT'}] | 2019-09-11 |
Giovanni Drera, Chahan M. Kropf, Luigi Sangaletti | Deep neural network for X-ray photoelectron spectroscopy data analysis | null | null | null | cond-mat.dis-nn cond-mat.mtrl-sci physics.comp-ph | In this work, we characterize the performance of a deep convolutional neural
network designed to detect and quantify chemical elements in experimental X-ray
photoelectron spectroscopy data. Given the lack of a reliable database in
literature, in order to train the neural network we computed a large ($>$100 k)
dataset... | [{'version': 'v1', 'created': 'Thu, 12 Sep 2019 09:28:21 GMT'}] | 2019-09-13 |
Mingjian Wen and Ellad B. Tadmor | Hybrid neural network potential for multilayer graphene | Phys. Rev. B 100, 195419 (2019) | 10.1103/PhysRevB.100.195419 | null | cond-mat.mtrl-sci physics.comp-ph | Monolayer and multilayer graphene are promising materials for applications
such as electronic devices, sensors, energy generation and storage, and
medicine. In order to perform large-scale atomistic simulations of the
mechanical and thermal behavior of graphene-based devices, accurate interatomic
potentials are requi... | [{'version': 'v1', 'created': 'Mon, 23 Sep 2019 03:01:10 GMT'}, {'version': 'v2', 'created': 'Sat, 16 Nov 2019 21:41:07 GMT'}] | 2019-11-27 |
Anton S. Bochkarev, Ambroise van Roekeghem, Stefano Mossa, Natalio
Mingo | Anharmonic Thermodynamics of Vacancies Using a Neural Network Potential | null | 10.1103/PhysRevMaterials.3.093803 | null | cond-mat.mtrl-sci | Lattice anharmonicity is thought to strongly affect vacancy concentrations in
metals at high temperatures. It is however non-trivial to account for this
effect directly using density functional theory (DFT). Here we develop a deep
neural network potential for aluminum that overcomes the limitations inherent
to DFT, a... | [{'version': 'v1', 'created': 'Mon, 23 Sep 2019 09:52:30 GMT'}] | 2019-10-02 |
Nouamane Laanait, Joshua Romero, Junqi Yin, M. Todd Young, Sean
Treichler, Vitalii Starchenko, Albina Borisevich, Alex Sergeev, Michael
Matheson | Exascale Deep Learning for Scientific Inverse Problems | null | null | null | cs.LG cond-mat.mtrl-sci cs.DC physics.comp-ph stat.ML | We introduce novel communication strategies in synchronous distributed Deep
Learning consisting of decentralized gradient reduction orchestration and
computational graph-aware grouping of gradient tensors. These new techniques
produce an optimal overlap between computation and communication and result in
near-linear ... | [{'version': 'v1', 'created': 'Tue, 24 Sep 2019 19:40:59 GMT'}] | 2019-09-26 |
Shenghong Ju, Ryo Yoshida, Chang Liu, Kenta Hongo, Terumasa Tadano,
Junichiro Shiomi | Exploring diamond-like lattice thermal conductivity crystals via
feature-based transfer learning | Phys. Rev. Materials 5, 053801 (2021) | 10.1103/PhysRevMaterials.5.053801 | null | cond-mat.mtrl-sci physics.comp-ph | Ultrahigh lattice thermal conductivity materials hold great importance since
they play a critical role in the thermal management of electronic and optical
devices. Models using machine learning can search for materials with
outstanding higher-order properties like thermal conductivity. However, the
lack of sufficient... | [{'version': 'v1', 'created': 'Wed, 25 Sep 2019 00:10:13 GMT'}] | 2021-05-19 |
Pietro D'Antuono and Michele Ciavarella | Mean stress effect on Ga{\ss}ner curves interpreted as shifted W\"ohler
curves | null | null | null | physics.app-ph cond-mat.mtrl-sci | A criterion for the mean stress effect correction in the shift factor
approach for variable amplitude life prediction is presented for both smooth
and notched specimens. The criterion is applied to the simple idea proposed by
the authors in a previous note that Ga{\ss}ner curves can be interpreted as
shifted W\"ohler... | [{'version': 'v1', 'created': 'Sun, 29 Sep 2019 17:28:27 GMT'}] | 2019-10-01 |
Tarak K Patra, Troy D. Loeffler, Henry Chan, Mathew J. Cherukara,
Badri Narayanan and Subramanian K.R.S. Sankaranarayanan | A coarse-grained deep neural network model for liquid water | null | 10.1063/1.5116591 | null | physics.comp-ph cond-mat.mtrl-sci physics.chem-ph | We introduce a coarse-grained deep neural network model (CG-DNN) for liquid
water that utilizes 50 rotational and translational invariant coordinates, and
is trained exclusively against energies of ~30,000 bulk water configurations.
Our CG-DNN potential accurately predicts both the energies and molecular forces
of wa... | [{'version': 'v1', 'created': 'Tue, 1 Oct 2019 08:32:01 GMT'}, {'version': 'v2', 'created': 'Mon, 14 Oct 2019 16:28:45 GMT'}] | 2020-01-08 |
Ari Frankel, Kousuke Tachida, Reese Jones | Prediction of the evolution of the stress field of polycrystals
undergoing elastic-plastic deformation with a hybrid neural network model | null | null | null | physics.comp-ph cond-mat.mtrl-sci | Crystal plasticity theory is often employed to predict the mesoscopic states
of polycrystalline metals, and is well-known to be costly to simulate. Using a
neural network with convolutional layers encoding correlations in time and
space, we were able to predict the evolution of the stress field given only the
initial... | [{'version': 'v1', 'created': 'Tue, 8 Oct 2019 02:16:45 GMT'}] | 2019-10-09 |
Ryan Jacobs, Tam Mayeshiba, Ben Afflerbach, Luke Miles, Max Williams,
Matthew Turner, Raphael Finkel, Dane Morgan | The Materials Simulation Toolkit for Machine Learning (MAST-ML): an
automated open source toolkit to accelerate data-driven materials research | Computational Materials Science, 176, 2020 | 10.1016/j.commatsci.2020.109544 | null | physics.comp-ph cond-mat.mtrl-sci | As data science and machine learning methods are taking on an increasingly
important role in the materials research community, there is a need for the
development of machine learning software tools that are easy to use (even for
nonexperts with no programming ability), provide flexible access to the most
important al... | [{'version': 'v1', 'created': 'Mon, 14 Oct 2019 17:20:56 GMT'}] | 2020-06-26 |
Jeffrey M. Ede | Deep Learning Supersampled Scanning Transmission Electron Microscopy | null | null | null | eess.IV cond-mat.mtrl-sci cs.CV | Compressed sensing can increase resolution, and decrease electron dose and
scan time of electron microscope point-scan systems with minimal information
loss. Building on a history of successful deep learning applications in
compressed sensing, we have developed a two-stage multiscale generative
adversarial network to... | [{'version': 'v1', 'created': 'Wed, 23 Oct 2019 11:30:25 GMT'}, {'version': 'v2', 'created': 'Fri, 25 Oct 2019 09:39:05 GMT'}] | 2019-10-28 |
Divya Kaushik, Utkarsh Singh, Upasana Sahu, Indu Sreedevi and Debanjan
Bhowmik | Comparing domain wall synapse with other Non Volatile Memory devices for
on-chip learning in Analog Hardware Neural Network | null | null | null | physics.app-ph cond-mat.mtrl-sci cs.NE | Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices
have been popularly used as synapses in crossbar array based analog Neural
Network (NN) circuit to achieve more energy and time efficient data
classification compared to conventional computers. Here we demonstrate the
advantages of recently p... | [{'version': 'v1', 'created': 'Mon, 28 Oct 2019 19:25:21 GMT'}] | 2019-10-30 |
Chengqiang Lu, Qi Liu, Qiming Sun, Chang-Yu Hsieh, Shengyu Zhang,
Liang Shi, and Chee-Kong Lee | Deep Learning for Optoelectronic Properties of Organic Semiconductors | J. Phys. Chem. C 2020, 124, 13, 7048 | null | null | physics.chem-ph cond-mat.mtrl-sci physics.comp-ph | Atomistic modeling of energetic disorder in organic semiconductors (OSCs) and
its effects on the optoelectronic properties of OSCs requires a large number of
excited-state electronic-structure calculations, a computationally daunting
task for many OSC applications. In this work, we advocate the use of deep
learning t... | [{'version': 'v1', 'created': 'Tue, 29 Oct 2019 21:42:02 GMT'}] | 2021-05-10 |
Ka-Ming Tam, Nicholas Walker, Samuel Kellar, Mark Jarrell | Interatomic Potential in a Simple Dense Neural Network Representation | null | null | null | physics.comp-ph cond-mat.mtrl-sci cond-mat.stat-mech | Simulations at the atomic scale provide a direct and effective way to
understand the mechanical properties of materials. In the regime of classical
mechanics, simulations for the thermodynamic properties of metals and alloys
can be done by either solving the equations of motion or performing Monte Carlo
sampling. The... | [{'version': 'v1', 'created': 'Mon, 4 Nov 2019 17:51:13 GMT'}] | 2019-11-05 |
Anh D. Phan, Cuong V. Nguyen, Pham T. Linh, Tran V. Huynh, Vu D. Lam,
and Anh-Tuan Le | Deep Learning for The Inverse Design of Mid-infrared Graphene Plasmons | null | null | null | physics.app-ph cond-mat.mes-hall cond-mat.mtrl-sci physics.optics | We theoretically investigate the plasmonic properties of mid-infrared
graphene-based metamaterials and apply deep learning of a neural network for
the inverse design. These artificial structures have square periodic arrays of
graphene plasmonic resonators deposited on dielectric thin films. Optical
spectra vary signi... | [{'version': 'v1', 'created': 'Thu, 28 Nov 2019 07:36:31 GMT'}, {'version': 'v2', 'created': 'Wed, 19 Feb 2020 06:17:32 GMT'}] | 2020-02-20 |
So Takamoto, Satoshi Izumi, Ju Li | TeaNet: universal neural network interatomic potential inspired by
iterative electronic relaxations | Computational Materials Science 207 (2022) 111280 | 10.1016/j.commatsci.2022.111280 | null | physics.comp-ph cond-mat.mtrl-sci cs.LG stat.ML | A universal interatomic potential for an arbitrary set of chemical elements
is urgently needed in computational materials science. Graph convolution neural
network (GCN) has rich expressive power, but previously was mainly employed to
transport scalars and vectors, not rank $\ge 2$ tensors. As classic interatomic
pot... | [{'version': 'v1', 'created': 'Mon, 2 Dec 2019 08:47:16 GMT'}, {'version': 'v2', 'created': 'Sun, 10 Oct 2021 18:34:11 GMT'}] | 2022-03-17 |
Ruiyang Li, Eungkyu Lee, Tengfei Luo | A Unified Deep Neural Network Potential Capable of Predicting Thermal
Conductivity of Silicon in Different Phases | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Molecular dynamics simulations have been extensively used to predict thermal
properties, but simulating different phases with similar precision using a
unified force field is often difficult, due to the lack of accurate and
transferrable interatomistic potential fields. As a result, this issue has
become a major barr... | [{'version': 'v1', 'created': 'Tue, 10 Dec 2019 23:17:49 GMT'}] | 2019-12-12 |
James P. Horwath, Dmitri N. Zakharov, Remi Megret, Eric A. Stach | Understanding Important Features of Deep Learning Models for
Transmission Electron Microscopy Image Segmentation | null | null | null | eess.IV cond-mat.mtrl-sci cs.LG | Cutting edge deep learning techniques allow for image segmentation with great
speed and accuracy. However, application to problems in materials science is
often difficult since these complex models may have difficultly learning
physical parameters. In situ electron microscopy provides a clear platform for
utilizing a... | [{'version': 'v1', 'created': 'Thu, 12 Dec 2019 16:52:23 GMT'}] | 2019-12-13 |
R. Ravinder, Karthikeya H. Sreedhara, Suresh Bishnoi, Hargun Singh
Grover, Mathieu Bauchy, Jayadeva, Hariprasad Kodamana, N. M. Anoop Krishnan | Deep Learning Aided Rational Design of Oxide Glasses | null | null | null | cond-mat.mtrl-sci cond-mat.dis-nn physics.data-an | Despite the extensive usage of oxide glasses for a few millennia, the
composition-property relationships in these materials still remain poorly
understood. While empirical and physics-based models have been used to predict
properties, these remain limited to a few select compositions or a series of
glasses. Designing... | [{'version': 'v1', 'created': 'Wed, 25 Dec 2019 03:04:59 GMT'}] | 2019-12-30 |
Reza Rashetnia and Mohammad Pour-Ghaz | Deep learning surrogate interacting Markov chain Monte Carlo based full
wave inversion scheme for properties of materials quantification | null | null | null | cond-mat.mtrl-sci cs.LG stat.ML | Full Wave Inversion (FWI) imaging scheme has many applications in
engineering, geoscience and medical sciences. In this paper, a surrogate deep
learning FWI approach is presented to quantify properties of materials using
stress waves. Such inverse problems, in general, are ill-posed and nonconvex,
especially in cases... | [{'version': 'v1', 'created': 'Mon, 16 Dec 2019 19:43:51 GMT'}] | 2020-01-08 |
Chia-Hao Lee (1), Abid Khan (2 and 3), Di Luo (2 and 3), Tatiane P.
Santos (1), Chuqiao Shi (1), Blanka E. Janicek (1), Sangmin Kang (4), Wenjuan
Zhu (4), Nahil A. Sobh (5), Andr\'e Schleife (1, 6 and 7), Bryan K. Clark
(2), Pinshane Y. Huang (1 and 6) ((1) Department of Materials Science and
Engineering, (2) D... | Deep Learning Enabled Strain Mapping of Single-Atom Defects in 2D
Transition Metal Dichalcogenides with Sub-picometer Precision | null | 10.1021/acs.nanolett.0c00269 | null | cond-mat.mtrl-sci cond-mat.mes-hall | 2D materials offer an ideal platform to study the strain fields induced by
individual atomic defects, yet challenges associated with radiation damage have
so-far limited electron microscopy methods to probe these atomic-scale strain
fields. Here, we demonstrate an approach to probe single-atom defects with
sub-picome... | [{'version': 'v1', 'created': 'Wed, 22 Jan 2020 19:05:53 GMT'}] | 2020-04-29 |
Saaketh Desai, Samuel Temple Reeve, James F. Belak | Implementing a neural network interatomic model with performance
portability for emerging exascale architectures | null | null | null | physics.comp-ph cond-mat.mtrl-sci | The two main thrusts of computational science are more accurate predictions
and faster calculations; to this end, the zeitgeist in molecular dynamics (MD)
simulations is pursuing machine learned and data driven interatomic models,
e.g. neural network potentials, and novel hardware architectures, e.g. GPUs.
Current im... | [{'version': 'v1', 'created': 'Fri, 31 Jan 2020 20:49:30 GMT'}, {'version': 'v2', 'created': 'Tue, 4 Feb 2020 19:22:38 GMT'}, {'version': 'v3', 'created': 'Fri, 21 Feb 2020 18:54:46 GMT'}] | 2020-02-24 |
Maxim Ziatdinov, Udi Fuchs, James H.G. Owen, John N. Randall, Sergei
V. Kalinin | Robust multi-scale multi-feature deep learning for atomic and defect
identification in Scanning Tunneling Microscopy on H-Si(100) 2x1 surface | null | null | null | cond-mat.mtrl-sci physics.app-ph | The nature of the atomic defects on the hydrogen passivated Si (100) surface
is analyzed using deep learning and scanning tunneling microscopy (STM). A
robust deep learning framework capable of identifying atomic species, defects,
in the presence of non-resolved contaminates, step edges, and noise is
developed. The a... | [{'version': 'v1', 'created': 'Tue, 11 Feb 2020 22:18:28 GMT'}] | 2020-02-19 |
Christopher M. Andolina, Philip Williamson, and Wissam A. Saidi | Optimization and Validation of a Deep Learning CuZr Atomistic Potential:
Robust Applications for Crystalline and Amorphous Phases with near-DFT
Accuracy | J. Chem. Phys. 152, 154701 (2020) | 10.1063/5.0005347 | null | cond-mat.mtrl-sci | We show that a deep-learning neural network potential (DP) based on density
functional theory (DFT) calculations can well describe Cu-Zr materials, an
example of a binary alloy system that can coexist in several ordered
intermetallics and as an amorphous phase. The complex phase diagram for Cu-Zr
makes it a challengi... | [{'version': 'v1', 'created': 'Mon, 17 Feb 2020 04:15:31 GMT'}] | 2020-04-29 |
Chongze Hu, Yunxing Zuo, Chi Chen, Shyue Ping Ong, Jian Luo | Genetic Algorithm-Guided Deep Learning of Grain Boundary Diagrams:
Addressing the Challenge of Five Degrees of Freedom | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Grain boundaries (GBs) often control the processing and properties of
polycrystalline materials. Here, a potentially transformative research is
represented by constructing GB property diagrams as functions of temperature
and bulk composition, also called "complexion diagrams," as a general materials
science tool on p... | [{'version': 'v1', 'created': 'Tue, 25 Feb 2020 02:43:36 GMT'}] | 2020-02-26 |
Xiaoyu Sun, Nathaniel J. Krakauer, Alexander Politowicz, Wei-Ting
Chen, Qiying Li, Zuoyi Li, Xianjia Shao, Alfred Sunaryo, Mingren Shen, James
Wang, Dane Morgan | Assessing Graph-based Deep Learning Models for Predicting Flash Point | Mol. Inf. 2020, 39, 1900101 | 10.1002/minf.201900101 | null | physics.comp-ph cond-mat.mtrl-sci cs.LG | Flash points of organic molecules play an important role in preventing
flammability hazards and large databases of measured values exist, although
millions of compounds remain unmeasured. To rapidly extend existing data to new
compounds many researchers have used quantitative structure-property
relationship (QSPR) an... | [{'version': 'v1', 'created': 'Wed, 26 Feb 2020 06:10:12 GMT'}] | 2020-02-28 |
Brian DeCost, Jason Hattrick-Simpers, Zachary Trautt, Aaron Kusne, Eva
Campo and Martin Green | Scientific AI in materials science: a path to a sustainable and scalable
paradigm | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Recently there has been an ever-increasing trend in the use of machine
learning (ML) and artificial intelligence (AI) methods by the materials
science, condensed matter physics, and chemistry communities. This perspective
article identifies key scientific, technical, and social opportunities that the
materials commun... | [{'version': 'v1', 'created': 'Wed, 18 Mar 2020 20:59:05 GMT'}] | 2020-03-20 |
Chunyang Wang, Guanglei Ding, Yitong Liu, Huolin L. Xin | 0.71-{\AA} resolution electron tomography enabled by deep learning aided
information recovery | null | null | null | cond-mat.mtrl-sci eess.IV physics.app-ph physics.ins-det | Electron tomography, as an important 3D imaging method, offers a powerful
method to probe the 3D structure of materials from the nano- to the
atomic-scale. However, as a grant challenge, radiation intolerance of the
nanoscale samples and the missing-wedge-induced information loss and artifacts
greatly hindered us fro... | [{'version': 'v1', 'created': 'Fri, 27 Mar 2020 07:16:30 GMT'}] | 2020-03-30 |
Haotong Liang, Valentin Stanev, A. Gilad Kusne, Ichiro Takeuchi | CRYSPNet: Crystal Structure Predictions via Neural Network | Phys. Rev. Materials 4, 123802 (2020) | 10.1103/PhysRevMaterials.4.123802 | null | cond-mat.mtrl-sci stat.ML | Structure is the most basic and important property of crystalline solids; it
determines directly or indirectly most materials characteristics. However,
predicting crystal structure of solids remains a formidable and not fully
solved problem. Standard theoretical tools for this task are computationally
expensive and a... | [{'version': 'v1', 'created': 'Tue, 31 Mar 2020 16:05:18 GMT'}] | 2021-01-04 |
Sehyun Chun, Sidhartha Roy, Yen Thi Nguyen, Joseph B. Choi, H.S.
Udaykumar, Stephen S. Baek | Deep learning for synthetic microstructure generation in a
materials-by-design framework for heterogeneous energetic materials | null | 10.1038/s41598-020-70149-0 | null | cond-mat.mtrl-sci cs.LG | The sensitivity of heterogeneous energetic (HE) materials (propellants,
explosives, and pyrotechnics) is critically dependent on their microstructure.
Initiation of chemical reactions occurs at hot spots due to energy localization
at sites of porosities and other defects. Emerging multi-scale predictive
models of HE ... | [{'version': 'v1', 'created': 'Sun, 5 Apr 2020 16:58:31 GMT'}] | 2023-05-12 |
Arash Rabbani, Masoud Babaei, Reza Shams, Ying Da Wang, Traiwit Chung | DeePore: a deep learning workflow for rapid and comprehensive
characterization of porous materials | Advances in Water Resources, 2020, 103787 | 10.1016/j.advwatres.2020.103787 | null | cond-mat.mtrl-sci cs.LG | DeePore is a deep learning workflow for rapid estimation of a wide range of
porous material properties based on the binarized micro-tomography images. By
combining naturally occurring porous textures we generated 17700 semi-real 3-D
micro-structures of porous geo-materials with size of 256^3 voxels and 30
physical pr... | [{'version': 'v1', 'created': 'Sun, 3 May 2020 08:46:09 GMT'}, {'version': 'v2', 'created': 'Sat, 10 Oct 2020 09:06:32 GMT'}] | 2020-10-13 |
Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Joerg Behler,
Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David
Lacroix, Davide Donadio | Transferability of neural network potentials for varying stoichiometry:
phonons and thermal conductivity of Mn$_x$Ge$_y$ compounds | Journal of Applied Physics 127, 244901 (2020) | 10.1063/5.0009550 | null | cond-mat.mtrl-sci | Germanium manganese compounds exhibit a variety of stable and metastable
phases with different stoichiometry. These materials entail interesting
electronic, magnetic and thermal properties both in their bulk form and as
heterostructures. Here we develop and validate a transferable machine learning
potential, based on... | [{'version': 'v1', 'created': 'Tue, 19 May 2020 17:15:00 GMT'}] | 2020-07-02 |
George S. Baggs, Paul Guerrier, Andrew Loeb, Jason C. Jones | Automated Copper Alloy Grain Size Evaluation Using a Deep-learning CNN | null | null | null | cs.CV cond-mat.mtrl-sci cs.LG stat.ML | Moog Inc. has automated the evaluation of copper (Cu) alloy grain size using
a deep-learning convolutional neural network (CNN). The proof-of-concept
automated image acquisition and batch-wise image processing offers the
potential for significantly reduced labor, improved accuracy of grain
evaluation, and decreased o... | [{'version': 'v1', 'created': 'Wed, 20 May 2020 13:13:38 GMT'}] | 2020-05-21 |
Samad Hajinazar, Aidan Thorn, Ernesto D. Sandoval, Saba Kharabadze,
Aleksey N. Kolmogorov | MAISE: Construction of neural network interatomic models and
evolutionary structure optimization | null | 10.1016/j.cpc.2020.107679 | null | physics.comp-ph cond-mat.mtrl-sci | Module for ab initio structure evolution (MAISE) is an open-source package
for materials modeling and prediction. The code's main feature is an automated
generation of neural network (NN) interatomic potentials for use in global
structure searches. The systematic construction of Behler-Parrinello-type NN
models appro... | [{'version': 'v1', 'created': 'Mon, 25 May 2020 14:15:39 GMT'}, {'version': 'v2', 'created': 'Tue, 15 Sep 2020 02:25:40 GMT'}] | 2020-10-26 |
Tarak K Patra, Troy D. Loeffler and Subramanian K R S Sankaranarayanan | Accelerating Copolymer Inverse Design using AI Gaming algorithm | null | null | null | cond-mat.soft cond-mat.mes-hall cond-mat.mtrl-sci | There exists a broad class of sequencing problems, for example, in proteins
and polymers that can be formulated as a heuristic search algorithm that
involve decision making akin to a computer game. AI gaming algorithms such as
Monte Carlo tree search (MCTS) gained prominence after their exemplary
performance in the c... | [{'version': 'v1', 'created': 'Mon, 1 Jun 2020 21:27:55 GMT'}] | 2020-06-08 |
Troy D Loeffler, Sukriti Manna, Tarak K Patra, Henry Chan, Badri
Narayanan, and Subramanian Sankaranarayanan | Active Learning A Neural Network Model For Gold Clusters \& Bulk From
Sparse First Principles Training Data | null | 10.1002/cctc.202000774 | null | physics.comp-ph cond-mat.mtrl-sci | Small metal clusters are of fundamental scientific interest and of tremendous
significance in catalysis. These nanoscale clusters display diverse geometries
and structural motifs depending on the cluster size; a knowledge of this
size-dependent structural motifs and their dynamical evolution has been of
longstanding ... | [{'version': 'v1', 'created': 'Fri, 5 Jun 2020 20:44:30 GMT'}] | 2020-07-21 |
Henry Chan, Youssef S.G. Nashed, Saugat Kandel, Stephan Hruszkewycz,
Subramanian Sankaranarayanan, Ross J. Harder, Mathew J. Cherukara | Real-time 3D Nanoscale Coherent Imaging via Physics-aware Deep Learning | null | 10.1063/5.0031486 | null | eess.IV cond-mat.mtrl-sci cs.LG physics.app-ph | Phase retrieval, the problem of recovering lost phase information from
measured intensity alone, is an inverse problem that is widely faced in various
imaging modalities ranging from astronomy to nanoscale imaging. The current
process of phase recovery is iterative in nature. As a result, the image
formation is time-... | [{'version': 'v1', 'created': 'Tue, 16 Jun 2020 18:35:32 GMT'}] | 2024-06-12 |
Marco Eckhoff, Florian Sch\"onewald, Marcel Risch, Cynthia A. Volkert,
Peter E. Bl\"ochl, J\"org Behler | Closing the gap between theory and experiment for lithium manganese
oxide spinels using a high-dimensional neural network potential | Phys. Rev. B 102, 174102 (2020) | 10.1103/PhysRevB.102.174102 | null | cond-mat.mtrl-sci | Many positive electrode materials in lithium ion batteries include transition
metals which are difficult to describe by electronic structure methods like
density functional theory (DFT) due to the presence of multiple oxidation
states. A prominent example is the lithium manganese oxide spinel
Li$_x$Mn$_2$O$_4$ with $... | [{'version': 'v1', 'created': 'Wed, 1 Jul 2020 08:44:44 GMT'}, {'version': 'v2', 'created': 'Fri, 2 Oct 2020 16:40:30 GMT'}] | 2020-11-11 |
Haotian Feng and Pavana Prabhakar | Difference-Based Deep Learning Framework for Stress Predictions in
Heterogeneous Media | null | 10.1016/j.compstruct.2021.113957 | null | physics.app-ph cond-mat.mtrl-sci cs.LG | Stress analysis of heterogeneous media, like composite materials, using
Finite Element Analysis (FEA) has become commonplace in design and analysis.
However, determining stress distributions in heterogeneous media using FEA can
be computationally expensive in situations like optimization and multi-scaling.
To address... | [{'version': 'v1', 'created': 'Wed, 1 Jul 2020 00:18:14 GMT'}, {'version': 'v2', 'created': 'Wed, 15 Jul 2020 03:30:14 GMT'}, {'version': 'v3', 'created': 'Mon, 29 Mar 2021 12:01:43 GMT'}] | 2021-04-22 |
Mart\'in Leandro Paleico, J\"org Behler | Global Optimization of Copper Clusters at the ZnO(10-10) Surface Using a
DFT-based Neural Network Potential and Genetic Algorithms | null | null | null | physics.chem-ph cond-mat.mtrl-sci physics.comp-ph | The determination of the most stable structures of metal clusters supported
at solid surfaces by computer simulations represents a formidable challenge due
to the complexity of the potential-energy surface. Here we combine a
high-dimensional neural network potential, which allows to predict the energies
and forces of... | [{'version': 'v1', 'created': 'Mon, 13 Jul 2020 15:50:20 GMT'}] | 2020-07-14 |
Ryan Cohn (1) and Elizabeth Holm (1) ((1) Department of Materials
Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, USA) | Unsupervised machine learning via transfer learning and k-means
clustering to classify materials image data | null | 10.1007/s40192-021-00205-8 | null | cond-mat.mtrl-sci cs.LG eess.IV | Unsupervised machine learning offers significant opportunities for extracting
knowledge from unlabeled data sets and for achieving maximum machine learning
performance. This paper demonstrates how to construct, use, and evaluate a high
performance unsupervised machine learning system for classifying images in a
popul... | [{'version': 'v1', 'created': 'Thu, 16 Jul 2020 14:36:04 GMT'}] | 2021-04-13 |
Joohwi Lee and Ryoji Asahi | Transfer learning for materials informatics using crystal graph
convolutional neural network | null | 10.1016/j.commatsci.2021.110314 | null | cond-mat.mtrl-sci physics.comp-ph | For successful applications of machine learning in materials informatics, it
is necessary to overcome the inaccuracy of predictions ascribed to insufficient
amount of data. In this study, we propose a transfer learning using a crystal
graph convolutional neural network (TL-CGCNN). Herein, TL-CGCNN is pretrained
with ... | [{'version': 'v1', 'created': 'Mon, 20 Jul 2020 08:27:57 GMT'}, {'version': 'v2', 'created': 'Tue, 18 Aug 2020 01:51:07 GMT'}, {'version': 'v3', 'created': 'Tue, 10 Nov 2020 02:38:18 GMT'}, {'version': 'v4', 'created': 'Fri, 29 Jan 2021 08:05:58 GMT'}] | 2021-02-01 |
Zachary D. McClure and Alejandro H. Strachan | Expanding materials selection via transfer learning for high-temperature
oxide selection | null | null | null | cond-mat.mtrl-sci | Materials with higher operating temperatures than today's state of the art
can improve system performance in several applications and enable new
technologies. Under most scenarios, a protective oxide scale with high melting
temperatures and thermodynamic stability as well as low ionic diffusivity is
required. Thus, t... | [{'version': 'v1', 'created': 'Mon, 20 Jul 2020 16:45:59 GMT'}] | 2020-07-27 |
Shreshth A. Malik, Rhys E. A. Goodall, Alpha A. Lee | Materials Graph Transformer predicts the outcomes of inorganic reactions
with reliable uncertainties | Chemistry of Materials 2021 33 (2), 616-624 | 10.1021/acs.chemmater.0c03885 | null | physics.comp-ph cond-mat.mtrl-sci | A common bottleneck for materials discovery is synthesis. While recent
methodological advances have resulted in major improvements in the ability to
predicatively design novel materials, researchers often still rely on
trial-and-error approaches for determining synthesis procedures. In this work,
we develop a model t... | [{'version': 'v1', 'created': 'Thu, 30 Jul 2020 21:39:58 GMT'}, {'version': 'v2', 'created': 'Thu, 3 Sep 2020 17:08:03 GMT'}] | 2021-01-27 |
Anran Wei, Jie Xiong, Weidong Yang, Fenglin Guo | Identifying the elastic isotropy of architectured materials based on
deep learning method | null | 10.1016/j.eml.2021.101173 | null | physics.app-ph cond-mat.dis-nn cond-mat.mtrl-sci | With the achievement on the additive manufacturing, the mechanical properties
of architectured materials can be precisely designed by tailoring
microstructures. As one of the primary design objectives, the elastic isotropy
is of great significance for many engineering applications. However, the
prevailing experimenta... | [{'version': 'v1', 'created': 'Sun, 2 Aug 2020 10:16:28 GMT'}, {'version': 'v2', 'created': 'Sun, 9 Aug 2020 03:15:22 GMT'}] | 2021-04-15 |
Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi, Ushio
Matsumoto, Akihide Kuwabara, and Takafumi Ogawa | Facilitating {\it ab initio} configurational sampling of multicomponent
solids using an on-lattice neural network model and active learning | null | 10.1063/5.0096645 | null | physics.comp-ph cond-mat.mtrl-sci | We propose a scheme for {\it ab initio} configurational sampling in
multicomponent crystalline solids using Behler-Parinello type neural network
potentials (NNPs) in an unconventional way: the NNPs are trained to predict the
energies of relaxed structures from the perfect lattice with configurational
disorder instead... | [{'version': 'v1', 'created': 'Thu, 6 Aug 2020 11:07:32 GMT'}, {'version': 'v2', 'created': 'Wed, 20 Apr 2022 12:33:53 GMT'}] | 2024-06-19 |
Koji Shimizu, Elvis F. Arguelles, Wenwen Li, Yasunobu Ando, Emi
Minamitani, and Satoshi Watanabe | Phase stability of Au-Li binary systems studied using neural network
potential | Phys. Rev. B 103, 094112 (2021) | 10.1103/PhysRevB.103.094112 | null | cond-mat.mtrl-sci | The miscibility of Au and Li exhibits a potential application as an adhesion
layer and electrode material in secondary batteries. Here, to explore alloying
properties, we constructed a neural network potential (NNP) of Au-Li binary
systems based on density functional theory (DFT) calculations. To accelerate
construct... | [{'version': 'v1', 'created': 'Wed, 12 Aug 2020 03:57:09 GMT'}] | 2021-03-31 |
Mohammadreza Karamad, Rishikesh Magar, Yuting Shi, Samira Siahrostami,
Ian D. Gates and Amir Barati Farimani | Orbital Graph Convolutional Neural Network for Material Property
Prediction | Phys. Rev. Materials 4, 093801 (2020) | 10.1103/PhysRevMaterials.4.093801 | null | physics.comp-ph cond-mat.mtrl-sci cs.LG | Material representations that are compatible with machine learning models
play a key role in developing models that exhibit high accuracy for property
prediction. Atomic orbital interactions are one of the important factors that
govern the properties of crystalline materials, from which the local chemical
environment... | [{'version': 'v1', 'created': 'Fri, 14 Aug 2020 15:22:22 GMT'}] | 2020-10-06 |
Kaiqi Yang, Yifan Cao, Youtian Zhang, Ming Tang, Daniel Aberg, Babak
Sadigh, Fei Zhou | Self-Supervised Learning and Prediction of Microstructure Evolution with
Recurrent Neural Networks | null | null | null | cond-mat.mtrl-sci physics.comp-ph | Microstructural evolution is a key aspect of understanding and exploiting the
structure-property-performance relation of materials. Modeling microstructure
evolution usually relies on coarse-grained simulations with evolution
principles described by partial differential equations (PDEs). Here we
demonstrate that conv... | [{'version': 'v1', 'created': 'Mon, 17 Aug 2020 22:46:12 GMT'}, {'version': 'v2', 'created': 'Sun, 30 Aug 2020 01:09:41 GMT'}] | 2020-09-01 |
Rongzhi Dong, Yabo Dan, Xiang Li, Jianjun Hu | Inverse Design of Composite Metal Oxide Optical Materials based on Deep
Transfer Learning | Computational Materials Science (2020): 110166 | 10.1016/j.commatsci.2020.110166 | null | cond-mat.mtrl-sci | Optical materials with special optical properties are widely used in a broad
span of technologies, from computer displays to solar energy utilization
leading to large dataset accumulated from years of extensive materials
synthesis and optical characterization. Previously, machine learning models
have been developed t... | [{'version': 'v1', 'created': 'Mon, 24 Aug 2020 18:00:14 GMT'}] | 2020-11-26 |
Juhyeok Lee and Chaehwa Jeong and Yongsoo Yang | Single-atom level determination of 3-dimensional surface atomic
structure via neural network-assisted atomic electron tomography | Nature Communications 12, 1962 (2021) | 10.1038/s41467-021-22204-1 | null | cond-mat.mtrl-sci | Functional properties of nanomaterials strongly depend on their surface
atomic structure, but they often become largely different from their bulk
structure, exhibiting surface reconstructions and relaxations. However, most of
the surface characterization methods are either limited to 2-dimensional
measurements or not... | [{'version': 'v1', 'created': 'Thu, 27 Aug 2020 10:08:28 GMT'}] | 2021-10-01 |
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker and J\"org Behler | A Fourth-Generation High-Dimensional Neural Network Potential with
Accurate Electrostatics Including Non-local Charge Transfer | null | 10.1038/s41467-020-20427-2 | null | cond-mat.mtrl-sci physics.chem-ph physics.comp-ph | Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials
science. Most of the established methods, however, rely on local properties and
are thus unable to take global changes in the electronic structure into
account, which r... | [{'version': 'v1', 'created': 'Mon, 14 Sep 2020 14:43:31 GMT'}] | 2021-03-17 |
G.P. Purja Pun, V. Yamakov, J. Hickman, E. H. Glaessgen, Y. Mishin | Development of a general-purpose machine-learning interatomic potential
for aluminum by the physically-informed neural network method | Phys. Rev. Materials 4, 113807 (2020) | 10.1103/PhysRevMaterials.4.113807 | null | physics.comp-ph cond-mat.mtrl-sci | Abstract Interatomic potentials constitute the key component of large-scale
atomistic simulations of materials. The recently proposed physically-informed
neural network (PINN) method combines a high-dimensional regression implemented
by an artificial neural network with a physics-based bond-order interatomic
potentia... | [{'version': 'v1', 'created': 'Mon, 14 Sep 2020 15:59:28 GMT'}, {'version': 'v2', 'created': 'Mon, 26 Oct 2020 17:11:19 GMT'}, {'version': 'v3', 'created': 'Mon, 9 Nov 2020 12:17:48 GMT'}] | 2020-11-25 |
Jeffrey M. Ede | Review: Deep Learning in Electron Microscopy | null | 10.1088/2632-2153/abd614 | null | eess.IV cond-mat.mtrl-sci cs.CV cs.LG | Deep learning is transforming most areas of science and technology, including
electron microscopy. This review paper offers a practical perspective aimed at
developers with limited familiarity. For context, we review popular
applications of deep learning in electron microscopy. Afterwards, we discuss
hardware and sof... | [{'version': 'v1', 'created': 'Thu, 17 Sep 2020 14:23:55 GMT'}, {'version': 'v2', 'created': 'Fri, 18 Sep 2020 14:38:31 GMT'}, {'version': 'v3', 'created': 'Mon, 5 Oct 2020 07:22:38 GMT'}, {'version': 'v4', 'created': 'Tue, 3 Nov 2020 15:38:31 GMT'}, {'version': 'v5', 'created': 'Sun, 27 Dec 2020 22:14:24 GMT'}, {'vers... | 2021-03-09 |
Mahmudul Islam, Md Shajedul Hoque Thakur, Satyajit Mojumder and
Mohammad Nasim Hasan | Extraction of Material Properties through Multi-fidelity Deep Learning
from Molecular Dynamics Simulation | null | 10.1016/j.commatsci.2020.110187 | null | physics.comp-ph cond-mat.dis-nn cond-mat.mtrl-sci | Simulation of reasonable timescales for any long physical process using
molecular dynamics (MD) is a major challenge in computational physics. In this
study, we have implemented an approach based on multi-fidelity physics informed
neural network (MPINN) to achieve long-range MD simulation results over a large
sample ... | [{'version': 'v1', 'created': 'Mon, 28 Sep 2020 07:32:24 GMT'}, {'version': 'v2', 'created': 'Tue, 29 Sep 2020 03:14:41 GMT'}, {'version': 'v3', 'created': 'Sat, 14 Nov 2020 22:44:06 GMT'}] | 2020-12-08 |
End of preview. Expand in Data Studio
Dataset Card for "Materials-Informatics"
Dataset Name: Materials-Informatics
Dataset Owner: cs-mubashir
Language: English
Size: ~600+ entries
Last Updated: May 2025
Source: Extracted from arxiv dataset research repository
Dataset Summary
The Materials-Informatics dataset is a curated collection of research papers from arxiv repository focusing on the intersection of artificial intelligence (AI) and materials science and engineering (MSE). Each entry provides metadata and descriptive information about a research paper, including its title, authors, abstract, keywords, publication year, material types, AI techniques used, and application domains.
This dataset aims to serve as a valuable resource for researchers and practitioners working at the convergence of machine learning, deep learning, and materials discovery/design. It can be used for tasks like information retrieval, scientific NLP, trend analysis, paper classification, and LLM fine-tuning for domain-specific tasks.
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