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Efficiently Trained Deep Learning Potential for Graphane

Siddarth K. Achar, Linfeng Zhang, J. Karl Johnson
The Journal of Physical Chemistry C, 2021, 125 (27), 14874–14882.
DOI: 10/gmfwwb

Cormorant: Covariant Molecular Neural Networks

Brandon Anderson, Truong-Son Hy, Risi Kondor
Advances in Neural Information Processing Systems 32 (Nips 2019), 2019, 32.

Optimization and Validation of a Deep Learning CuZr Atomistic Potential: Robust Applications for Crystalline and Amorphous Phases with near-DFT Accuracy

Christopher M. Andolina, Philip Williamson, Wissam A. Saidi
Journal of Chemical Physics, 2020, 152 (15).
DOI: 10.1063/5.0005347

Robust, Multi-Length-Scale, Machine Learning Potential for Ag–Au Bimetallic Alloys from Clusters to Bulk Materials

Christopher M. Andolina, Marta Bon, Daniele Passerone, Wissam A. Saidi
The Journal of Physical Chemistry C, 2021.
DOI: 10/gmdj4k

Free Energy of Proton Transfer at the Water-TiO2 Interface from Ab Initio Deep Potential Molecular Dynamics

Marcos F. Calegari Andrade, Hsin-Yu Ko, Linfeng Zhang, Roberto Car, Annabella Selloni
Chemical Science, 2020, 11 (9), 2335–2341.
DOI: 10.1039/c9sc05116c

Hydrogen Dynamics in Supercritical Water Probed by Neutron Scattering and Computer Simulations

Carla Andreani, Giovanni Romanelli, Alexandra Parmentier, Roberto Senesi, Alexander Kolesnikov, Hsin-Yu Ko, Marcos F. Calegari Andrade, Roberto Car
Journal of Physical Chemistry Letters, 2020, 11 (21), 9461–9467.
DOI: 10.1021/acs.jpclett.0c02547

Active Learning Accelerates Ab Initio Molecular Dynamics on Pericyclic Reactive Energy Surfaces

Shi Jun Ang, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod, Rafael Gomez-Bombarelli
2020.

Active Learning Accelerates Ab Initio Molecular Dynamics on Reactive Energy Surfaces

Shi Jun Ang, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod, Rafael Gómez-Bombarelli
Chem, 2021, 7 (3), 738–751.
DOI: 10/gmgdj2

Embedding Quantum Statistical Excitations in a Classical Force Field

Susan R. Atlas
Journal of Physical Chemistry A, 2021, 125 (17), 3760–3775.
DOI: 10.1021/acs.jpca.1c00164

Deep Machine Learning Interatomic Potential for Liquid Silica

I. A. Balyakin, S. Rempel, R. E. Ryltsev, A. A. Rempel
Physical Review E, 2020, 102 (5), 052125.
DOI: 10.1103/PhysRevE.102.052125

Machine-Learning-Based Interatomic Potential for Phonon Transport in Perfect Crystalline Si and Crystalline Si with Vacancies

Hasan Banaei, Ruiqiang Guo, Amirreza Hashemi, Sangyeop Lee
Physical Review Materials, 2019, 3 (7), 074603.
DOI: 10.1103/PhysRevMaterials.3.074603

Structure Motif-Centric Learning Framework for Inorganic Crystalline Systems

Huta R. Banjade, Sandro Hauri, Shanshan Zhang, Francesco Ricci, Weiyi Gong, Geoffroy Hautier, Slobodan Vucetic, Qimin Yan
Science Advances, 2021, 7 (17), eabf1754.
DOI: 10.1126/sciadv.abf1754

Voxelized Atomic Structure Potentials: Predicting Atomic Forces with the Accuracy of Quantum Mechanics Using Convolutional Neural Networks

Matthew C. Barry, Kristopher E. Wise, Surya R. Kalidindi, Satish Kumar
Journal of Physical Chemistry Letters, 2020, 11 (21), 9093–9099.
DOI: 10.1021/acs.jpclett.0c02271

Machine Learning a General-Purpose Interatomic Potential for Silicon

Albert P. Bartók, James Kermode, Noam Bernstein, Gábor Csányi
Physical Review X, 2018, 8 (4), 041048.
DOI: 10.1103/PhysRevX.8.041048

Machine Learning for Multi-Fidelity Scale Bridging and Dynamical Simulations of Materials

R Batra, S Sankaranarayanan - Journal of Physics: Materials, undefined 2020
iopscience.iop.org, 2020, 3, 31002.
DOI: 10.1088/2515-7639/ab8c2d

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials

Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, Boris Kozinsky
2021.

De Novo Exploration and Self-Guided Learning of Potential-Energy Surfaces

Noam Bernstein, Gabor Csanyi, Volker L. Deringer
Npj Computational Materials, 2019, 5, 99.
DOI: 10.1038/s41524-019-0236-6

A Perspective on Inverse Design of Battery Interphases Using Multi-Scale Modelling, Experiments and Generative Deep Learning

Arghya Bhowmik, Ivano E. Castelli, Juan Maria Garcia-Lastra, Peter Bjorn Jorgensen, Ole Winther, Tejs Vegge
Energy Storage Materials, 2019, 21, 446–456.
DOI: 10.1016/j.ensm.2019.06.011

Efficient Sampling of Equilibrium States Using Boltzmann Generators

Jeremy Binagia, Sean Friedowitz, Kevin J Hou
, 6.

Efficient Global Structure Optimization with a Machine-Learned Surrogate Model

Malthe K. Bisbo, Bjørk Hammer
Physical Review Letters, 2020, 124 (8).
DOI: 10.1103/physrevlett.124.086102

Efficient Prediction of 3D Electron Densities Using Machine Learning

Mihail Bogojeski, Felix Brockherde, Leslie Vogt-Maranto, Li Li, Mark E. Tuckerman, Kieron Burke, Klaus-Robert Müller
2018.

Quantum Chemical Accuracy from Density Functional Approximations via Machine Learning

Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Mueller, Kieron Burke
Nature Communications, 2020, 11 (1), 5223.
DOI: 10.1038/s41467-020-19093-1

Neural Networks-Based Variationally Enhanced Sampling

Luigi Bonati, Yue-Yu Zhang, Michele Parrinello
Proceedings of the National Academy of Sciences of the United States of America, 2019, 116 (36), 17641–17647.
DOI: 10.1073/pnas.1907975116

Silicon Liquid Structure and Crystal Nucleation from Ab Initio Deep Metadynamics

Luigi Bonati, Michele Parrinello
Physical review letters, 2018, 121 (26), 265701.
DOI: 10.1103/PhysRevLett.121.265701

Machine Learning in Nano-Scale Biomedical Engineering

Alexandros-Apostolos A. Boulogeorgos, Stylianos E. Trevlakis, Sotiris A. Tegos, Vasilis K. Papanikolaou, George K. Karagiannidis
2020.

Transforming Solid-State Precipitates via Excess Vacancies

Laure Bourgeois, Yong Zhang, Zezhong Zhang, Yiqiang Chen, Nikhil Medhekar
Nature Communications, 2020, 11 (1), 1248.
DOI: 10.1038/s41467-020-15087-1

MB-Fit: Software Infrastructure for Data-Driven Many-Body Potential Energy Functions

Ethan Bull-Vulpe, Marc Riera, Andreas Goetz, Francesco Paesani
2021.

Deep-Learning Approach to First-Principles Transport Simulations

Marius Burkle, Umesha Perera, Florian Gimbert, Hisao Nakamura, Masaaki Kawata, Yoshihiro Asai
Physical Review Letters, 2021, 126 (17), 177701.
DOI: 10.1103/PhysRevLett.126.177701

Gaussian Approximation Potentials for Body-Centered-Cubic Transition Metals

J. Byggmastar, K. Nordlund, F. Djurabekova
Physical Review Materials, 2020, 4 (9), 093802.
DOI: 10.1103/PhysRevMaterials.4.093802

Machine-Learning Interatomic Potential for Radiation Damage and Defects in Tungsten

J. Byggmastar, A. Hamedani, K. Nordlund, F. Djurabekova
Physical Review B, 2019, 100 (14), 144105.
DOI: 10.1103/PhysRevB.100.144105

Structure of Disordered \${\textbackslash mathrm{\vphantom}}TiO\vphantom{}\vphantom{}_{2}\$ Phases from Ab Initio Based Deep Neural Network Simulations

Marcos F. Calegari Andrade, Annabella Selloni
Physical Review Materials, 2020, 4 (11), 113803.
DOI: 10/ghnhd5

Machine-Learning X-Ray Absorption Spectra to Quantitative Accuracy

Matthew R. Carbone, Mehmet Topsakal, Deyu Lu, Shinjae Yoo
Physical Review Letters, 2020, 124 (15), 156401.
DOI: 10.1103/PhysRevLett.124.156401

Computing RPA Adsorption Enthalpies by Machine Learning Thermodynamic Perturbation Theory

Bilal Chehaibou, Michael Badawi, Tomas Bucko, Timur Bazhirov, Dario Rocca
Journal of Chemical Theory and Computation, 2019, 15 (11), 6333–6342.
DOI: 10.1021/acs.jctc.9b00782

Topics in the Mathematical Design of Materials

X Chen, I Fonseca, M Ravnik, V Slastikov, C Zannoni
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021, 379 (2201), 20200108.
DOI: 10.1098/rsta.2020.0108

Direct Prediction of Phonon Density of States with Euclidean Neural Networks

Z Chen, N Andrejevic, T Smidt, Z Ding, Q Xu - Advanced …, undefined 2021
Wiley Online Library, 2021, 8.
DOI: 10.1002/advs.202004214

Atomic Energies from a Convolutional Neural Network

Xin Chen, Mathias S. Jorgensen, Jun Li, Bjork Hammer
Journal of Chemical Theory and Computation, 2018, 14 (7), 3933–3942.
DOI: 10.1021/acs.jctc.8b00149

Competitive Effect of Disorder and Defects on Dynamic Structural Transformation of Compressed Gold

B Chen, Q Zeng, H Wang, D Kang, J Dai
arxiv.org, 2021.
DOI: arXiv:2006.13136

A Critical Review of Machine Learning of Energy Materials

Chi Chen, Yunxing Zuo, Weike Ye, Xiangguo Li, Zhi Deng, Shyue Ping Ong
Advanced Energy Materials, 2020, 10 (8), 1903242.
DOI: 10.1002/aenm.201903242

Machine Learning on Neutron and X-Ray Scattering

Z Chen, N Andrejevic, N Drucker, T Nguyen
arxiv.org.

DeePKS: A Comprehensive Data-Driven Approach toward Chemically Accurate Density Functional Theory

Yixiao Chen, Linfeng Zhang, Han Wang, E. Weinan
Journal of Chemical Theory and Computation, 2021, 17 (1), 170–181.
DOI: 10.1021/acs.jctc.0c00872

DeePKS-Kit: A Package for Developing Machine Learning-Based Chemically Accurate Energy and Density Functional Models

Y Chen, L Zhang, H Wang
arxiv.org, 2021.

Efficient Construction of Excited-State Hessian Matrices with Machine Learning Accelerated Multilayer Energy-Based Fragment Method

Wen-Kai Chen, Yaolong Zhang, Bin Jiang, Wei-Hai Fang, Ganglong Cui
Journal of Physical Chemistry A, 2020, 124 (27), 5684–5695.
DOI: 10.1021/acs.jpca.0c04117

Exploiting Machine Learning to Efficiently Predict Multidimensional Optical Spectra in Complex Environments

Michael S. Chen, Tim J. Zuehlsdorff, Tobias Morawietz, Christine M. Isborn, Thomas E. Markland
Journal of Physical Chemistry Letters, 2020, 11 (18), 7559–7568.
DOI: 10.1021/acs.jpclett.0c02168

Co-Segregation of Mg and Zn Atoms at the Planar Η1-Precipitate/Al Matrix Interface in an Aged Al–Zn–Mg Alloy

Bingqing Cheng, Xiaojun Zhao, Yong Zhang, Houwen Chen, Ian Polmear, Jian-Feng Nie
Scripta Materialia, 2020, 185, 51–55.
DOI: 10/gmgc5h

Deep-Learning Potential Method to Simulate Shear Viscosity of Liquid Aluminum at High Temperature and High Pressure by Molecular Dynamics

Yuqing Cheng, Han Wang, Shuaichuang Wang, Xingyu Gao, Qiong Li, Jun Fang, Hongzhou Song, Weidong Chu, Gongmu Zhang, Haifeng Song, Haifeng Liu
Aip Advances, 2021, 11 (1), 015043.
DOI: 10.1063/5.0036298

Gold Segregation Improves Electrocatalytic Activity of Icosahedron Au@Pt Nanocluster: Insights from Machine Learning

Dingming Chen, Zhuangzhuang Lai, Jiawei Zhang, Jianfu Chen, Peijun Hu, Haifeng Wang
Chinese Journal of Chemistry, 2021, n/a (n/a).
DOI: 10/gmfw5g

Regression Clustering for Improved Accuracy and Training Costs with Molecular-Orbital-Based Machine Learning

Lixue Cheng, Nikola B. Kovachki, Matthew Welborn, Thomas F. Miller
Journal of Chemical Theory and Computation, 2019, 15 (12), 6668–6677.
DOI: 10.1021/acs.jctc.9b00884

Ground State Energy Functional with Hartree-Fock Efficiency and Chemical Accuracy

Yixiao Chen, Linfeng Zhang, Han Wang, E. Weinan
Journal of Physical Chemistry A, 2020, 124 (35), 7155–7165.
DOI: 10.1021/acs.jpca.0c03886

A Universal Density Matrix Functional from Molecular Orbital-Based Machine Learning: Transferability across Organic Molecules

Lixue Cheng, Matthew Welborn, Anders S. Christensen, Thomas F. Miller
Journal of Chemical Physics, 2019, 150 (13), 131103.
DOI: 10.1063/1.5088393

Integrating Machine Learning with the Multilayer Energy-Based Fragment Method for Excited States of Large Systems

Wen-Kai Chen, Wei-Hai Fang, Ganglong Cui
Journal of Physical Chemistry Letters, 2019, 10 (24), 7836–7841.
DOI: 10.1021/acs.jpclett.9b03113

On the Representation of Solutions to Elliptic PDEs in Barron Spaces

Ziang Chen, Jianfeng Lu, Yulong Lu
2021.

TensorAlloy: An Automatic Atomistic Neural Network Program for Alloys

Xin Chen, Xing-Yu Gao, Ya-Fan Zhao, De-Ye Lin, Wei-Dong Chu, Hai-Feng Song
Computer Physics Communications, 2020, 250, 107057.
DOI: 10.1016/j.cpc.2019.107057

Unsupervised Machine Learning Methods for Polymer Nanocomposites Data via Molecular Dynamics Simulation

Zhudan Chen, Dazi Li, Haixiao Wan, Minghui Liu, Jun Liu
Molecular Simulation, 2020.
DOI: 10.1080/08927022.2020.1851028

Constructing Convex Energy Landscapes for Atomistic Structure Optimization

Siva Chiriki, Mads-Peter Christiansen, B. Hammer
Physical Review B, 2019, 100 (23), 235436.
DOI: 10.1103/PhysRevB.100.235436

Accurate Molecular Dynamics Enabled by Efficient Physically-Constrained Machine Learning Approaches

Stefan Chmiela, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller
2020, 968, 129–154.
DOI: 10/gmgfsq

Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Mueller, Alexandre Tkatchenko
Nature Communications, 2018, 9, 3887.
DOI: 10.1038/s41467-018-06169-2

sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning

Stefan Chmiela, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Mueller, Alexandre Tkatchenko
Computer Physics Communications, 2019, 240, 38–45.
DOI: 10.1016/j.cpc.2019.02.007

Efficient Training of Machine Learning Potentials by a Randomized Atomic-System Generator

Young-Jae Choi, Seung-Hoon Jhi
The Journal of Physical Chemistry B, 2020, 124 (39), 8704–8710.
DOI: 10/gmf6kr

FCHL Revisited: Faster and More Accurate Quantum Machine Learning

Anders S. Christensen, Lars A. Bratholm, Felix A. Faber, O. Anatole von Lilienfeld
Journal of Chemical Physics, 2020, 152 (4), 044107.
DOI: 10.1063/1.5126701

Gaussian Representation for Image Recognition and Reinforcement Learning of Atomistic Structure

Mads Peter V. Christiansen, Henrik Lund Mortensen, Søren Ager Meldgaard, Bjørk Hammer
Journal of Chemical Physics, 2020, 153 (4).
DOI: 10.1063/5.0015571

Autonomous Discovery in the Chemical Sciences Part I: Progress

Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Angewandte Chemie-International Edition, 2020, 59 (51), 22858–22893.
DOI: 10.1002/anie.201909987

Dielectric Response with Short-Ranged Electrostatics

Stephen J. Cox
Proceedings of the National Academy of Sciences, 2020, 117 (33), 19746–19752.
DOI: 10/ghc8bb

Highly Accurate Many-Body Potentials for Simulations of N2O5 in Water: Benchmarks, Development, and Validation

Vinicius Wilian D. Cruzeiro, Eleftherios Lambros, Marc Riera, Ronak Roy, Francesco Paesani, Andreas W. Gotz
Journal of Chemical Theory and Computation, 2021, 17 (7), 3931–3945.
DOI: 10.1021/acs.jctc.1c00069

Analytical Model of Electron Density and Its Machine Learning Inference

Bruno Cuevas-Zuviria, Luis F. Pacios
Journal of Chemical Information and Modeling, 2020, 60 (8), 3831–3842.
DOI: 10.1021/acs.jcim.0c00197

Large Deviations for the Perceptron Model and Consequences for Active Learning

H Cui, L Saglietti, L Zdeborová - Mathematical and Scientific, undefined 2020
proceedings.mlr.press, 2020, 107, 390–430.

Biomolecular QM/MM Simulations: What Are Some of the "Burning Issues"?

Qiang Cui, Tanmoy Pal, Luke Xie
Journal of Physical Chemistry B, 2021, 125 (3), 689–702.
DOI: 10.1021/acs.jpcb.0c09898

Grain Boundary Strengthening in ZrB2 by Segregation of W: Atomistic Simulations with Deep Learning Potential

Fu-Zhi Dai, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of the European Ceramic Society, 2020, 40 (15), 5029–5036.
DOI: 10.1016/j.jeurceramsoc.2020.06.007

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B-2: Molecular Dynamics Simulation by Deep Learning Potential

Fu-Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou
Journal of Materials Science \& Technology, 2021, 72, 8–15.
DOI: 10.1016/j.jmst.2020.07.014

Theoretical Prediction on Thermal and Mechanical Properties of High Entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by Deep Learning Potential

Fu-Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou
Journal of Materials Science \& Technology, 2020, 43, 168–174.
DOI: 10.1016/j.jmst.2020.01.005

Relationship of Structure and Mechanical Property of Silica with Enhanced Sampling and Machine Learning

Yuanpeng Deng, Tao Du, Hui Li
Journal of the American Ceramic Society, 2021, 104 (8), 3910–3920.
DOI: 10/gmfw49

A General-Purpose Machine-Learning Force Field for Bulk and Nanostructured Phosphorus

Volker L. Deringer, Miguel A. Caro, Gabor Csanyi
Nature Communications, 2020, 11 (1), 5461.
DOI: 10.1038/s41467-020-19168-z

Modelling and Understanding Battery Materials with Machine-Learning-Driven Atomistic Simulations

Volker L. Deringer
Journal of Physics-Energy, 2020, 2 (4), 041003.
DOI: 10.1088/2515-7655/abb011

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations

Sebastian Dick, Marivi Fernandez-Serra
The Journal of Chemical Physics, 2019, 151 (14), 144102.
DOI: 10/gmgftv

Hierarchical Machine Learning of Potential Energy Surfaces

Pavlo O. Dral, Alec Owens, Alexey Dral, Gabor Csanyi
Journal of Chemical Physics, 2020, 152 (20).
DOI: 10.1063/5.0006498

MLatom 2: An Integrative Platform for Atomistic Machine Learning

Pavlo O. Dral, Fuchun Ge, Bao-Xin Xue, Yi-Fan Hou, Max Pinheiro, Jianxing Huang, Mario Barbatti
Topics in Current Chemistry, 2021, 379 (4), 27.
DOI: 10.1007/s41061-021-00339-5

Quantum Chemistry in the Age of Machine Learning

Pavlo O. Dral
Journal of Physical Chemistry Letters, 2020, 11 (6), 2336–2347.
DOI: 10.1021/acs.jpclett.9b03664

Toward Efficient Generation, Correction, and Properties Control of Unique Drug-like Structures

Maksym Druchok, Dzvenymyra Yarish, Oleksandr Gurbych, Mykola Maksymenko
Journal of Computational Chemistry, 2021, 42 (11), 746–760.
DOI: 10.1002/jcc.26494

Dynamics \& Spectroscopy with Neutrons-Recent Developments \& Emerging Opportunities

Kacper Druzbicki, Mattia Gaboardi, Felix Fernandez-Alonso
Polymers, 2021, 13 (9), 1440.
DOI: 10.3390/polym13091440

Data-Driven Approaches Can Overcome the Cost-Accuracy Trade-Off in Multireference Diagnostics

Chenru Duan, Fang Liu, Aditya Nandy, Heather J. Kulik
Journal of Chemical Theory and Computation, 2020, 16 (7), 4373–4387.
DOI: 10.1021/acs.jctc.0c00358

Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models

Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, Heather J. Kulik
Journal of Chemical Theory and Computation, 2019, 15 (4), 2331–2345.
DOI: 10.1021/acs.jctc.9b00057

Design, Parameterization, and Implementation of Atomic Force Fields for Adsorption in Nanoporous Materials

D Dubbeldam, KS Walton, TJH Vlugt - Advanced Theory and …, undefined 2019
Wiley Online Library, 2019, 2 (11).
DOI: 10.1002/adts.201900135

Atomic Cluster Expansion: Completeness, Efficiency and Stability

Genevieve Dusson, Markus Bachmayr, Gabor Csanyi, Ralf Drautz, Simon Etter, Cas van der Oord, Christoph Ortner
2021.

Algorithms for Solving High Dimensional PDEs: From Nonlinear Monte Carlo to Machine Learning

Weinan E, Jiequn Han, Arnulf Jentzen, A Jentzen - arXiv preprint ArXiv:2008.13333, undefined 2020
arxiv.org, 2020.

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

J. A. Ellis, L. Fiedler, G. A. Popoola, N. A. Modine, J. A. Stephens, A. P. Thompson, A. Cangi, S. Rajamanickam
Physical Review B, 2021, 104 (3), 035120.
DOI: 10.1103/PhysRevB.104.035120

Neuroevolution Machine Learning Potentials: Combining High Accuracy and Low Cost in Atomistic Simulations and Application to Heat Transport

Zheyong Fan, Zezhu Zeng, Cunzhi Zhang, Yanzhou Wang, Haikuan Dong, Yue Chen, Tapio Ala-Nissila
2021.

A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space

Kuo Gai, Shihua Zhang
2021.

Reactive Uptake of N2O5 by Atmospheric Aerosol Is Dominated by Interfacial Processes

M Galib, DT Limmer
science.sciencemag.org, 2021.

Deep Learning in Protein Structural Modeling and Design

Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
Patterns, 2020, 1 (9), 100142.
DOI: 10.1016/j.patter.2020.100142

Short Solvent Model for Ion Correlations and Hydrophobic Association

Ang Gao, Richard C. Remsing, John D. Weeks
Proceedings of the National Academy of Sciences of the United States of America, 2020, 117 (3), 1293–1302.
DOI: 10.1073/pnas.1918981117

TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Xiang Gao, Farhad Ramezanghorbani, Olexandr Isayev, Justin S. Smith, Adrian E. Roitberg
Journal of Chemical Information and Modeling, 2020, 60 (7), 3408–3415.
DOI: 10.1021/acs.jcim.0c00451

Signatures of a Liquid-Liquid Transition in an Ab Initio Deep Neural Network Model for Water

Thomas E. Gartner, Linfeng Zhang, Pablo M. Piaggi, Roberto Car, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti
Proceedings of the National Academy of Sciences of the United States of America, 2020, 117 (42), 26040–26046.
DOI: 10.1073/pnas.2015440117

Combining Phonon Accuracy with High Transferability in Gaussian Approximation Potential Models

Janine George, Geoffroy Hautier, Albert P. Bartok, Gabor Csanyi, Volker L. Deringer
Journal of Chemical Physics, 2020, 153 (4), 044104.
DOI: 10.1063/5.0013826

The Role of Feature Space in Atomistic Learning

Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano, Michele Ceriotti
Machine Learning-Science and Technology, 2021, 2 (2), 025028.
DOI: 10.1088/2632-2153/abdaf7

Code Interoperability Extends the Scope of Quantum Simulations

Marco Govoni, Jonathan Whitmer, Juan de Pablo, Francois Gygi, Giulia Galli
Npj Computational Materials, 2021, 7 (1), 32.
DOI: 10.1038/s41524-021-00501-z

Incorporating Long-Range Physics in Atomic-Scale Machine Learning

Andrea Grisafi, Michele Ceriotti
Journal of Chemical Physics, 2019, 151 (20), 204105.
DOI: 10.1063/1.5128375

Multi-Scale Approach for the Prediction of Atomic Scale Properties

Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Chemical Science, 2021, 12 (6), 2078–2090.
DOI: 10.1039/d0sc04934d

Deep Neural Network Model for Approximating Eigenmodes Localized by a Confining Potential

L Grubišić, M Hajba, D Lacmanović - Entropy
mdpi.com, 2021, 2, 27001.
DOI: 10.1088/2632-2153/abc940

Finite-Temperature Interplay of Structural Stability, Chemical Complexity, and Elastic Properties of Bcc Multicomponent Alloys from Ab Initio Trained Machine-Learning Potentials

Konstantin Gubaev, Yuji Ikeda, Ferenc Tasnadi, Joerg Neugebauer, Alexander Shapeev, Blazej Grabowski, Fritz Koermann
Physical Review Materials, 2021, 5 (7), 073801.
DOI: 10.1103/PhysRevMaterials.5.073801

Enumeration of de Novo Inorganic Complexes for Chemical Discovery and Machine Learning

Stefan Gugler, Jon Paul Janet, Heather J. Kulik
Molecular Systems Design \& Engineering, 2020, 5 (1), 139–152.
DOI: 10.1039/c9me00069k

High-Repetition-Rate Femtosecond Mid-Infrared Pulses Generated by Nonlinear Optical Modulation of Continuous-Wave QCLs and ICLs

Chenglin Gu, Zhong Zuo, Daping Luo, Daowang Peng, Yuanfeng Di, Xing Zou, Liu Yang, Wenxue Li
Optics Letters, 2019, 44 (23), 5848–5851.
DOI: 10.1364/OL.44.005848

Neural Network Representation of Electronic Structure from Ab Initio Molecular Dynamics

Q Gu, L Zhang, J Feng
arxiv.org, 2021.

Bergman-Type Medium Range Order in Amorphous Zr77Rh23 Alloy Studied by Ab Initio Molecular Dynamics Simulations

Y. R. Guo, Chong Qiao, J. J. Wang, H. Shen, S. Y. Wang, Y. X. Zheng, R. J. Zhang, L. Y. Chen, Wan-Sheng Su, C. Z. Wang, K. M. Ho
Journal of Alloys and Compounds, 2019, 790, 675–682.
DOI: 10.1016/j.jallcom.2019.03.197

The Thermoelectric Performance of New Structure SnSe Studied by Quotient Graph and Deep Learning Potential

D. Guo, C. Li, K. Li, B. Shao, D. Chen, Y. Ma, J. Sun, X. Cao, W. Zeng, X. Chang
Materials Today Energy, 2021, 20, 100665.
DOI: 10/gmgd38

Sparse Gaussian Process Potentials: Application to Lithium Diffusivity in Superionic Conducting Solid Electrolytes

Amir Hajibabaei, Chang Woo Myung, Kwang S. Kim
Physical Review B, 2021, 103 (21), 214102.
DOI: 10.1103/PhysRevB.103.214102

MAISE: Construction of Neural Network Interatomic Models and Evolutionary Structure Optimization

S Hajinazar, A Thorn, ED Sandoval
Elsevier, 2020.

Machine Learning-Assisted Excited State Molecular Dynamics with the State-Interaction State-Averaged Spin-Restricted Ensemble-Referenced Kohn-Sham Approach

Jong-Kwon Ha, Kicheol Kim, Seung Kyu Min
Journal of Chemical Theory and Computation, 2021, 17 (2), 694–702.
DOI: 10.1021/acs.jctc.0c01261

Dynamic Observation of Dendritic Quasicrystal Growth upon Laser-Induced Solid-State Transformation

Insung Han, Joseph T. McKeown, Ling Tang, Cai-Zhuang Wang, Hadi Parsamehr, Zhucong Xi, Ying-Rui Lu, Matthew J. Kramer, Ashwin J. Shahani
Physical Review Letters, 2020, 125 (19), 195503.
DOI: 10.1103/PhysRevLett.125.195503

A Machine Learning Approach for MP2 Correlation Energies and Its Application to Organic Compounds

Ruocheng Han, Mauricio Rodriguez-Mayorga, Sandra Luber
Journal of Chemical Theory and Computation, 2021, 17 (2), 777–790.
DOI: 10.1021/acs.jctc.0c00898

Solving Many-Electron Schrodinger Equation Using Deep Neural Networks

Jiequn Han, Linfeng Zhang, Weinan E
Journal of Computational Physics, 2019, 399, 108929.
DOI: 10.1016/j.jcp.2019.108929

Trajectory-Based Machine Learning Method and Its Application to Molecular Dynamics

R. Han, S. Luber
Molecular Physics, 2020, 118 (19-20).
DOI: 10.1080/00268976.2020.1788189

Uniformly Accurate Machine Learning-Based Hydrodynamic Models for Kinetic Equations

Jiequn Han, Chao Ma, Zheng Ma, Weinan E
Proceedings of the National Academy of Sciences of the United States of America, 2019, 116 (44), 21983–21991.
DOI: 10.1073/pnas.1909854116

Uniformly Accurate Machine Learning-Based Hydrodynamic Models for Kinetic Equations

Jiequn Han, Chao Ma, Zheng Ma, Weinan E
Proceedings of the National Academy of Sciences of the United States of America, 2019, 116 (44), 21983–21991.
DOI: 10.1073/pnas.1909854116

Universal Approximation of Symmetric and Anti-Symmetric Functions

J Han, Y Li, L Lin, J Lu, J Zhang, L Zhang
arxiv.org, 2019.

Validating First-Principles Molecular Dynamics Calculations of Oxide/Water Interfaces with x-Ray Reflectivity Data

Katherine J. Harmon, Kendra Letchworth-Weaver, Alex P. Gaiduk, Federico Giberti, Francois Gygi, Maria K. Y. Chan, Paul Fenter, Giulia Galli
Physical Review Materials, 2020, 4 (11), 113805.
DOI: 10.1103/PhysRevMaterials.4.113805

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Jason R. Hattrick-Simpers, Brian DeCost, A. Gilad Kusne, Howie Joress, Winnie Wong-Ng, Debra L. Kaiser, Andriy Zakutayev, Caleb Phillips, Shijing Sun, Janak Thapa, Heshan Yu, Ichiro Takeuchi, Tonio Buonassisi
Integrating Materials and Manufacturing Innovation, 2021, 10 (2), 311–318.
DOI: 10/gkhbw2

Fast, Accurate, and Transferable Many-Body Interatomic Potentials by Symbolic Regression

Alberto Hernandez, Adarsh Balasubramanian, Fenglin Yuan, Simon A. M. Mason, Tim Mueller
Npj Computational Materials, 2019, 5, 112.
DOI: 10.1038/s41524-019-0249-1

Compressing Physical Properties of Atomic Species for Improving Predictive Chemistry

John E. Herr, Kevin Koh, Kun Yao, John Parkhill
The Journal of Chemical Physics, 2019, 151 (8), 084103.
DOI: 10/ggb5bq

Compressing Physics with an Autoencoder: Creating an Atomic Species Representation to Improve Machine Learning Models in the Chemical Sciences

John E. Herr, Kevin Koh, Kun Yao, John Parkhill
Journal of Chemical Physics, 2019, 151 (8), 084103.
DOI: 10.1063/1.5108803

In Operando Active Learning of Interatomic Interaction during Large-Scale Simulations

M Hodapp, A Shapeev - Machine Learning: Science And, undefined 2020
iopscience.iop.org, 2020.
DOI: 10.1088/2632-2153/aba373

Machine-Learning Potentials Enable Predictive \$\textbackslash textit{and}\$ Tractable High-Throughput Screening of Random Alloys

Max Hodapp, Alexander Shapeev
2021.

Dielectric Constant of Supercritical Water in a Large Pressure-Temperature Range

Rui Hou, Yuhui Quan, Ding Pan
Journal of Chemical Physics, 2020, 153 (10), 101103.
DOI: 10.1063/5.0020811

Deep Potential Generation Scheme and Simulation Protocol for the Li10GeP2S12-Type Superionic Conductors

Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, E. Weinan
Journal of Chemical Physics, 2021, 154 (9), 094703.
DOI: 10.1063/5.0041849

Ab Initio Machine Learning in Chemical Compound Space

Bing Huang, O. Anatole von Lilienfeld
2021.

Int-Deep: A Deep Learning Initialized Iterative Method for Nonlinear Problems

Jianguo Huang, Haoqin Wang, Haizhao Yang
Journal of Computational Physics, 2020, 419, 109675.
DOI: 10/gg2rtj

Learning Thermodynamically Stable and Galilean Invariant Partial Differential Equations for Non-Equilibrium Flows

Juntao Huang, Zhiting Ma, Yizhou Zhou, Wen An Yong
Journal of Non-Equilibrium Thermodynamics, 2021.
DOI: 10.1515/JNET-2021-0008/HTML

Machine Learning Moment Closure Models for the Radiative Transfer Equation I: Directly Learning a Gradient Based Closure

Juntao Huang, Yingda Cheng, Andrew J. Christlieb, Luke F. Roberts
2021.

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

Deping Hu, Yu Xie, Xusong Li, Lingyue Li, Zhenggang Lan
Journal of Physical Chemistry Letters, 2018, 9 (11), 2725–2732.
DOI: 10.1021/acs.jpclett.8b00684

Neural Network Force Fields for Metal Growth Based on Energy Decompositions

Qin Hu, Mouyi Weng, Xin Chen, Shucheng Li, Feng Pan, Lin-Wang Wang
Journal of Physical Chemistry Letters, 2020, 11 (4), 364–1369.
DOI: 10.1021/acs.jpclett.9b03780

Perspective on Multi-Scale Simulation of Thermal Transport in Solids and Interfaces

Ming Hu, Zhonghua Yang
Physical Chemistry Chemical Physics, 2021, 23 (3), 1785–1801.
DOI: 10.1039/d0cp03372c

Coarse Graining Molecular Dynamics with Graph Neural Networks

Brooke E. Husic, Nicholas E. Charron, Dominik Lemm, Jiang Wang, Adria Perez, Maciej Majewski, Andreas Kramer, Yaoyi Chen, Simon Olsson, Gianni de Fabritiis, Frank Noe, Cecilia Clementi
Journal of Chemical Physics, 2020, 153 (19), 194101.
DOI: 10.1063/5.0026133

Artificial Neutral Networks (ANNs) Applied as CFD Optimization Techniques

Ideen Sadrehaghighi
2021.
DOI: 10/gmf5vh

Efficient Multiscale Optoelectronic Prediction for Conjugated Polymers

Nicholas E. Jackson, Alec S. Bowen, Juan J. de Pablo
Macromolecules, 2020, 53 (1), 482–490.
DOI: 10.1021/acs.macromol.9b02020

Electronic Structure at Coarse-Grained Resolutions from Supervised Machine Learning

Nicholas E. Jackson, Alec S. Bowen, Lucas W. Antony, Michael A. Webb, Venkatram Vishwanath, Juan J. de Pablo
Science Advances, 2019, 5 (3), eaav1190.
DOI: 10.1126/sciadv.aav1190

Recent Advances in Machine Learning towards Multiscale Soft Materials Design

Nicholas E. Jackson, Michael A. Webb, Juan J. de Pablo
Current Opinion in Chemical Engineering, 2019, 23, 106–114.
DOI: 10.1016/j.coche.2019.03.005

Machine Learning for Metallurgy III: A Neural Network Potential for Al-Mg-Si

Abhinav C.P. Jain, Daniel Marchand, Albert Glensk, M. Ceriotti, W. A. Curtin
Physical Review Materials, 2021, 5 (5).
DOI: 10.1103/physrevmaterials.5.053805

A Quantitative Uncertainty Metric Controls Error in Neural Network-Driven Chemical Discovery

Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather J. Kulik
Chemical Science, 2019, 10 (34), 7913–7922.
DOI: 10.1039/c9sc02298h

Uncertain Times Call for Quantitative Uncertainty Metrics: Controlling Error in Neural Network Predictions for Chemical Discovery

Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather Kulik
2019.
DOI: 10.26434/chemrxiv.7900277.v1

Towards Fully Ab Initio Simulation of Atmospheric Aerosol Nucleation

S Jiang, YR Liu, T Huang, YJ Feng, CY Wang
arxiv.org, 2021.

Accurate Deep Potential Model for the Al–Cu–Mg Alloy in the Full Concentration Space

W Jiang, Y Zhang, L Zhang, Wang H
iopscience.iop.org, 2021.

Accurate Deep Potential Model for the Al-Cu-Mg Alloy in the Full Concentration Space

Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Chinese Physics B, 2021, 30 (5), 050706.
DOI: 10.1088/1674-1056/abf134

Self-Healing Mechanism of Lithium Metal

Junyu Jiao, Genming Lai, Jiaze Lu, Xianqi Xu, Jing Wang, Jiaxin Zheng
2021.

Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning

Weile Jia, Han Wang, Mohan Chen, Denghui Lu, L Lin, Lin Lin, Roberto Car, Linfeng Zhang
ieeexplore.ieee.org, 2021.

On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations

Ryosuke Jinnouchi, Kazutoshi Miwa, Ferenc Karsai, Georg Kresse, Ryoji Asahi
Journal of Physical Chemistry Letters, 2020, 11 (17), 6946–6955.
DOI: 10.1021/acs.jpclett.0c01061

Large-Scale Atomic Simulation via Machine Learning Potentials Constructed by Global Potential Energy Surface Exploration

Pei-Lin Kang, Cheng Shang, Zhi-Pan Liu
Accounts of Chemical Research, 2020, 53 (10), 2119–2129.
DOI: 10.1021/acs.accounts.0c00472

Enabling Ab Initio Configurational Sampling of Multicomponent Solids with Long-Range Interactions Using Neural Network Potentials and Active Learning

Shusuke Kasamatsu, Yuichi Motoyama, Kazuyoshi Yoshimi, Ushio Matsumoto, Akihide Kuwabara, Takafumi Ogawa
2020.

Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

John A. Keith, Valentin Vassilev-Galindo, Bingqing Cheng, Stefan Chmiela, Michael Gastegger, Klaus-Robert Müller, Alexandre Tkatchenko
2021.

Reaction Path-Force Matching in Collective Variables: Determining Ab Initio QM/MM Free Energy Profiles by Fitting Mean Force

Bryant Kim, Ryan Snyder, Mulpuri Nagaraju, Yan Zhou, Pedro Ojeda-May, Seth Keeton, Mellisa Hege, Yihan Shao, Jingzhi Pu
Journal of Chemical Theory and Computation, 2021, 17 (8), 4961–4980.
DOI: 10/gmfw5p

Neural Network Potentials: A Concise Overview of Methods

Emir Kocer, TW Tsz Wai Ko, Jörg Behler, J Behler
arxiv.org, 2021.

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based Ab Initio Molecular Dynamics. 1. Theory, Algorithm, and Performance

Hsin-Yu Ko, Junteng Jia, Biswajit Santra, Xifan Wu, Roberto Car, Robert DiStasio
Journal of Chemical Theory and Computation, 2020, 16 (6), 3757–3785.
DOI: 10.1021/acs.jctc.9b01167

Isotope Effects in Liquid Water via Deep Potential Molecular Dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio, Roberto Car
Molecular Physics, 2019, 117 (22), 3269–3281.
DOI: 10.1080/00268976.2019.1652366

N-Body Networks: A Covariant Hierarchical Neural Network Architecture for Learning Atomic Potentials

Risi Kondor
2018.

Manifold Learning for Coarse-Graining Atomistic Simulations: Application to Amorphous Solids

Katiana Kontolati, Darius Alix-Williams, Nicholas M. Boffi, Michael L. Falk, Chris H. Rycroft, Michael D. Shields
2021.

Accessing Thermal Conductivity of Complex Compounds by Machine Learning Interatomic Potentials

P Korotaev, I Novoselov, A Yanilkin, A Shapeev B
APS, 2019, 100 (14), 144308.
DOI: 10.1103/physrevb.100.144308

Dielectric Constant of Liquid Water Determined with Neural Network Quantum Molecular Dynamics

Aravind Krishnamoorthy, Ken-ichi Nomura, Nitish Baradwaj, Kohei Shimamura, Pankaj Rajak, Ankit Mishra, Shogo Fukushima, Fuyuki Shimojo, Rajiv Kalia, Aiichiro Nakano, Priya Vashishta
Physical Review Letters, 2021, 126 (21), 216403.
DOI: 10.1103/PhysRevLett.126.216403

Size and Temperature Transferability of Direct and Local Deep Neural Networks for Atomic Forces

Natalia Kuritz, Goren Gordon, Amir Natan
Physical Review B, 2018, 98 (9), 094109.
DOI: 10/gkv2j9

The Estimation of the Second Virial Coefficients of He and N2 Based on Neural Network Potentials with Quantum Mechanical Calculations

Taejin Kwon, Han Wook Song, Sam Yong Woo, Jong-Ho Kim, Bong June Sung
Chemical Physics, 2021, 548, 111231.
DOI: 10/gmf6ws

Machine-Learning-Based Non-Newtonian Fluid Model with Molecular Fidelity

Huan Lei, Lei Wu, Weinan Weinan
Physical Review E, 2020, 102 (4).
DOI: 10.1103/physreve.102.043309

Modeling Electrochemical Interfaces from Ab Initio Molecular Dynamics: Water Adsorption on Metal Surfaces at Potential of Zero Charge

Jia-Bo Le, Jun Cheng
Current Opinion in Electrochemistry, 2020, 19, 129–136.
DOI: 10.1016/j.coelec.2019.11.008

Non-Classical Nucleation Pathways in Stacking-Disordered Crystals

Fabio Leoni, John Russo
2021.

Nonclassical Nucleation Pathways in Stacking-Disordered Crystals

Fabio Leoni, John Russo
Physical Review X, 2021, 11 (3), 031006.
DOI: 10.1103/PhysRevX.11.031006

Accurate and Transferable Reactive Molecular Dynamics Models from Constrained Density Functional Theory

Chenghan Li, Gregory A Voth
, 31.

Analysis of Trajectory Similarity and Configuration Similarity in On-the-Fly Surface-Hopping Simulation on Multi-Channel Nonadiabatic Photoisomerization Dynamics

Xusong Li, Deping Hu, Yu Xie, Zhenggang Lan
Journal of Chemical Physics, 2018, 149 (24), 244104.
DOI: 10.1063/1.5048049

Machine-Learning-Driven Simulations on Microstructure and Thermophysical Properties of MgCl2-KCl Eutectic

Wenshuo Liang, Guimin Lu, Jianguo Yu
Acs Applied Materials \& Interfaces, 2021, 13 (3), 4034–4042.
DOI: 10.1021/acsami.0c20665

Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine-Learning-Based Deep Potential

Wenshuo Liang, Guimin Lu, Jianguo Yu
Advanced Theory and Simulations, 2020, 3 (12), 2000180.
DOI: 10.1002/adts.202000180

Theoretical Prediction on the Local Structure and Transport Properties of Molten Alkali Chlorides by Deep Potentials

Wenshuo Liang, Guimin Lu, Jianguo Yu
Journal of Materials Science \& Technology, 2021, 75, 78–85.
DOI: 10/gmf63v

Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units

Bo Li, Shanshan Tang, Haijun Yu
Communications in Computational Physics, 2020, 27 (2), 379–411.
DOI: 10.4208/cicp.OA-2019-0168

CONFORMATION-GUIDED MOLECULAR REPRESENTA- TION WITH HAMILTONIAN NEURAL NETWORKS

Ziyao Li, Shuwen Yang, Guojie Song, Lingsheng Cai
2021, 11.

Development of Robust Neural-Network Interatomic Potential for Molten Salt

Qing-Jie Li, Emine Kucukbenli, Stephen Lam, Boris Khaykovich, Efthimios Kaxiras, Ju Li
Cell Reports Physical Science, 2021, 2 (3), 100359.
DOI: 10.1016/j.xcrp.2021.100359

Effect of Local Structural Disorder on Lithium Diffusion Behavior in Amorphous Silicon

Wenwen Li, Yasunobu Ando
Physical Review Materials, 2020, 4 (4).
DOI: 10.1103/physrevmaterials.4.045602

HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks

Ziyao Li, Shuwen Yang, Guojie Song, Lingsheng Cai
2021.

Introducing Block Design in Graph Neural Networks for Molecular Properties Prediction

Yuquan Li, Pengyong Li, Xing Yang, Chang-Yu Hsieh, Shengyu Zhang, Xiaorui Wang, Ruiqiang Lu, Huanxiang Liu, Xiaojun Yao
Chemical Engineering Journal, 2021, 414, 128817.
DOI: 10.1016/j.cej.2021.128817

Machine Learning in Computational Surface Science and Catalysis: Case Studies on Water and Metal–Oxide Interfaces

Xiaoke Li, Wolfgang Paier, Joachim Paier
Frontiers in Chemistry, 2020, 8, 601029.
DOI: 10/ghnggc

Multilevel Fine-Tuning: Closing Generalization Gaps in Approximation of Solution Maps Under a Limited Budget for Training Data

Zhihan Li, Yuwei Fan, Lexing Ying
Multiscale Modeling \& Simulation, 2021, 19 (1), 344–373.
DOI: 10.1137/20M1326404

Neural Canonical Transformation with Symplectic Flows

Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, Lei Wang
Physical Review X, 2020, 10 (2), 021020.
DOI: 10.1103/PhysRevX.10.021020

A Neural-Network Based Framework of Developing Cross Interaction in Alloy Embedded-Atom Method Potentials: Application to Zr-Nb Alloy

Bo Lin, Jincheng Wang, Junjie Li, Zhijun Wang
Journal of Physics-Condensed Matter, 2021, 33 (8), 084004.
DOI: 10.1088/1361-648X/abcb69

Numerical Methods for Kohn-Sham Density Functional Theory

Lin Lin, Jianfeng Lu, Lexing Ying
Acta Numerica, 2019, 28, 405–539.
DOI: 10.1017/S0962492919000047

Searching Configurations in Uncertainty Space: Active Learning of High-Dimensional Neural Network Reactive Potentials

Qidong Lin, Liang Zhang, Yaolong Zhang, Bin Jiang
Journal of Chemical Theory and Computation, 2021, 17 (5), 2691–2701.
DOI: 10/gmfw5n

Unravelling the Fast Alkali-Ion Dynamics in Paramagnetic Battery Materials Combined with NMR and Deep-Potential Molecular Dynamics Simulation

Min Lin, Xiangsi Liu, Yuxuan Xiang, Feng Wang, Yunpei Liu, Riqiang Fu, Jun Cheng, Yong Yang
Angewandte Chemie-International Edition, 2021, 60 (22), 12547–12553.
DOI: 10.1002/anie.202102740

PowerNet: Efficient Representations of Polynomials and Smooth Functions by Deep Neural Networks with Rectified Power Units

Bo Li, Shanshan Tang, Haijun Yu
Journal of Mathematical Study, 2020, 53 (2), 159–191.
DOI: 10.4208/jms.v53n2.20.03

Theoretical Study of Na+ Transport in the Solid-State Electrolyte Na3OBr Based on Deep Potential Molecular Dynamics

Han-Xiao Li, Xu-Yuan Zhou, Yue-Chao Wang, Hong Jiang
Inorganic Chemistry Frontiers, 2021, 8 (2), 425–432.
DOI: 10.1039/d0qi00921k

Machine Learning Phase Space Quantum Dynamics Approaches

Xinzijian Liu, Linfeng Zhang, Jian Liu
Journal of Chemical Physics, 2021, 154 (18), 184104.
DOI: 10.1063/5.0046689

A Unified Deep Neural Network Potential Capable of Predicting Thermal Conductivity of Silicon in Different Phases

R. Li, E. Lee, T. Luo
Materials Today Physics, 2020, 12, 100181.
DOI: 10.1016/j.mtphys.2020.100181

Rapid Detection of Strong Correlation with Machine Learning for Transition-Metal Complex High-Throughput Screening

Fang Liu, Chenru Duan, Heather J. Kulik
Journal of Physical Chemistry Letters, 2020, 11 (19), 8067–8076.
DOI: 10.1021/acs.jpclett.0c02288

Structure and Dynamics of Warm Dense Aluminum: A Molecular Dynamics Study with Density Functional Theory and Deep Potential

Qianrui Liu, Denghui Lu, Mohan Chen
Journal of Physics-Condensed Matter, 2020, 32 (14), 144002.
DOI: 10.1088/1361-648X/ab5890

Thermal Transport by Electrons and Ions in Warm Dense Aluminum: A Combined Density Functional Theory and Deep Potential Study

Qianrui Liu, Junyi Li, Mohan Chen
Matter and Radiation at Extremes, 2021, 6 (2).
DOI: 10.1063/5.0030123

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

Ziteng Liu, Liqiang Lin, Qingqing Jia, Zheng Cheng, Yanyan Jiang, Yanwen Guo, Jing Ma
Journal of Chemical Information and Modeling, 2021, 61 (3), 1066–1082.
DOI: 10.1021/acs.jcim.0c01224

Active Learning a Coarse-Grained Neural Network Model for Bulk Water from Sparse Training Data

TD Loeffler, TK Patra, Chan H
pubs.rsc.org.

Active Learning a Neural Network Model for Gold Clusters\& Bulk from Sparse First Principles Training Data

TD Loeffler, S Manna, TK Patra, Chan H
arxiv.org, 2020.

Active Learning the Potential Energy Landscape for Water Clusters from Sparse Training Data

Troy D. Loeffler, Tarak K. Patra, Henry Chan, Mathew Cherukara, Subramanian K.R.S. Sankaranarayanan
Journal of Physical Chemistry C, 2020, 124 (8), 4907–4916.
DOI: 10.1021/acs.jpcc.0c00047

PDE-Net 2.0: Learning PDEs from Data with a Numeric-Symbolic Hybrid Deep Network

Zichao Long, Yiping Lu, Bin Dong
Journal of Computational Physics, 2019, 399, 108925.
DOI: 10.1016/j.jcp.2019.108925

PANNA: Properties from Artificial Neural Network Architectures

Ruggero Lot, Franco Pellegrini, Yusuf Shaidu, Emine Kucukbenli
Computer Physics Communications, 2020, 256, 107402.
DOI: 10.1016/j.cpc.2020.107402

Deep Learning: New Engine for the Study of Material Microstructures and Physical Properties

G Lu, S Duan
Modern Physics 现代物理, 2019, 2019 (6), 263–276.
DOI: 10.12677/mp.2019.96026

Dataset Construction to Explore Chemical Space with 3D Geometry and Deep Learning

Jianing Lu, Song Xia, Jieyu Lu, Yingkai Zhang
Journal of Chemical Information and Modeling, 2021, 61 (3), 1095–1104.
DOI: 10.1021/acs.jcim.1c00007

Deep Potential Molecular Dynamics Simulation of 100 Million Atoms with Ab Initio Accuracy

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
Computer Physics Communications, 2021, 259, 107624.
DOI: 10.1016/j.cpc.2020.107624

Deep Potential Molecular Dynamics Simulation of 100 Million Atoms with Ab Initio Accuracy

Denghui Lu, Han Wang, Mohan Chen, Lin Lin, Roberto Car, Weinan E, Weile Jia, Linfeng Zhang
Computer Physics Communications, 2021, 259, 107624.
DOI: 10.1016/j.cpc.2020.107624

DP Train, Then DP Compress: Model Compression in Deep Potential Molecular Dynamics

D Lu, W Jiang, Y Chen, L Zhang, W Jia, H Wang
arxiv.org, 2021.

A Unified Picture of the Covalent Bond within Quantum-Accurate Force Fields: From Organic Molecules to Metallic Complexes' Reactivity

Alessandro Lunghi, Stefano Sanvito
Science Advances, 2019, 5 (5), eaaw2210.
DOI: 10.1126/sciadv.aaw2210

Anomalous Behavior of Viscosity and Electrical Conductivity of MgSiO3 Melt at Mantle Conditions

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geophysical Research Letters, 2021, 48 (13), e2021GL093573.
DOI: 10/gkrt5v

Deep Neural Network Potentials for Diffusional Lithium Isotope Fractionation in Silicate Melts

Haiyang Luo, Bijaya B. Karki, Dipta B. Ghosh, Huiming Bao
Geochimica et Cosmochimica Acta, 2021, 303, 38–50.
DOI: 10/gmf625

Predicting Molecular Energy Using Force-Field Optimized Geometries and Atomic Vector Representations Learned from an Improved Deep Tensor Neural Network

Jianing Lu, Cheng Wang, Yingkai Zhang
Journal of Chemical Theory and Computation, 2019, 15 (7), 4113–4121.
DOI: 10.1021/acs.jctc.9b00001

Deep Learning Observables in Computational Fluid Dynamics

KO Lye, S Mishra, D Ray - Journal of Computational Physics, undefined 2020
Elsevier, 2019.

A Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems

Jonathan P. Mailoa, Mordechai Kornbluth, Simon Batzner, Georgy Samsonidze, Stephen T. Lam, Jonathan Vandermause, Chris Ablitt, Nicola Molinari, Boris Kozinsky
Nature Machine Intelligence, 2019, 1 (10), 471–479.
DOI: 10.1038/s42256-019-0098-0

Evaluation of Experimental Alkali Metal Ion-Ligand Noncovalent Bond Strengths with DLPNO-CCSD(T) Method

Bholanath Maity, Yury Minenkov, Luigi Cavallo
Journal of Chemical Physics, 2019, 151 (1), 014301.
DOI: 10.1063/1.5099580

Transferability of Neural Network Potentials for Varying Stoichiometry: Phonons and Thermal Conductivity of Mn\$_x\$Ge\$_y\$ Compounds

Claudia Mangold, Shunda Chen, Giuseppe Barbalinardo, Joerg Behler, Pascal Pochet, Konstantinos Termentzidis, Yang Han, Laurent Chaput, David Lacroix, Davide Donadio
Journal of Applied Physics, 2020, 127 (24), 244901.
DOI: 10/gg7jww

Machine Learning for Metallurgy I. A Neural-Network Potential for Al-Cu

Daniel Marchand, Abhinav Jain, Albert Glensk, W. A. Curtin
Physical Review Materials, 2020, 4 (10).
DOI: 10.1103/physrevmaterials.4.103601

Simulating Diffusion Properties of Solid-State Electrolytes via a Neural Network Potential: Performance and Training Scheme

Aris Marcolongo, Tobias Binninger, Federico Zipoli, Teodoro Laino
2019.

Connection between Liquid and Non-Crystalline Solid Phases in Water

Fausto Martelli, Fabio Leoni, Francesco Sciortino, John Russo
Journal of Chemical Physics, 2020, 153 (10), 104503.
DOI: 10.1063/5.0018923

Deep Learning in Chemistry

Adam C. Mater, Michelle L. Coote
Journal of Chemical Information and Modeling, 2019, 59 (6), 2545–2559.
DOI: 10.1021/acs.jcim.9b00266

Machine-Learning Interatomic Potentials for Materials Science

Y Mishin - Acta Materialia, undefined 2021
Elsevier, 2021.

Machine Learning Enhanced Global Optimization by Clustering Local Environments to Enable Bundled Atomic Energies

Soren A. Meldgaard, Esben L. Kolsbjerg, Bjork Hammer
Journal of Chemical Physics, 2018, 149 (13), 134104.
DOI: 10.1063/1.5048290

Transformative Applications of Machine Learning for Chemical Reactions

M. Meuwly
2021.

Liquid to Crystal Si Growth Simulation Using Machine Learning Force Field

Ling Miao, Lin Wang Wang
Journal of Chemical Physics, 2020, 153 (7).
DOI: 10.1063/5.0011163

Strategies for the Construction of Machine-Learning Potentials for Accurate and Efficient Atomic-Scale Simulations

April M. Miksch, Tobias Morawietz, Johannes Kaestner, Alexander Urban, Nongnuch Artrith
Machine Learning-Science and Technology, 2021, 2 (3), 031001.
DOI: 10.1088/2632-2153/abfd96

Gas Phase Silver Thermochemistry from First Principles

Irina Minenkova, Valery V. Slizney, Luigi Cavallo, Yury Minenkov
Inorganic Chemistry, 2019, 58 (12), 7873–7885.
DOI: 10.1021/acs.inorgchem.9b00556

An Automated Approach for Developing Neural Network Interatomic Potentials with FLAME

H Mirhosseini, H Tahmasbi, SR Kuchana - Computational Materials …, undefined 2021
Elsevier, 2021.

Molecular Dynamics Properties without the Full Trajectory: A Denoising Autoencoder Network for Properties of Simple Liquids

Alireza Moradzadeh, N. R. Aluru
Journal of Physical Chemistry Letters, 2019, 10 (24), 7568–7576.
DOI: 10.1021/acs.jpclett.9b02820

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

Manuel D. Morales, Javier M. Antelis, Claudia Moreno, Alexander I. Nesterov
Sensors, 2021, 21 (9), 3174.
DOI: 10/gmgfm6

Machine Learning-Accelerated Quantum Mechanics-Based Atomistic Simulations for Industrial Applications

Tobias Morawietz, Nongnuch Artrith
Journal of Computer-Aided Molecular Design, 2021, 35 (4), 557–586.
DOI: 10.1007/s10822-020-00346-6

Transfer Learning of Potential Energy Surfaces for Efficient Atomistic Modeling of Doping and Alloy

Pinghui Mo, Mengchao Shi, Wenze Yao, Jie Liu
IEEE Electron Device Letters, 2020, 41 (4), 633–636.
DOI: 10/gg2bfc

Assessment of Localized and Randomized Algorithms for Electronic Structure

Jonathan E. Moussa, Andrew D. Baczewski
Electronic Structure, 2019, 1 (3), 033001.
DOI: 10.1088/2516-1075/ab2022

The Dynamic Control of the Light Signalling Device in Real-Time

Jan Mrazek, Lucia Duricova Mrazkova, Martin Hromada, Jana Reznickova
MATEC Web of Conferences, 2019, 292, 03014.
DOI: 10/gmgfts

Traffic Control Through Traffic Density

Jan Mrazek, Lucia Duricova Mrazkova, Martin Hromada
2019 3rd European Conference on Electrical Engineering and Computer Science (Eecs 2019), 2019, 19–21.
DOI: 10.1109/EECS49779.2019.00017

Machine Learning for Interatomic Potential Models

Tim Mueller, Alberto Hernandez, Chuhong Wang
Journal of Chemical Physics, 2020, 152 (5), 050902.
DOI: 10.1063/1.5126336

Supervised Learning of Few Dirty Bosons with Variable Particle Number

P Mujal, À Martínez Miguel, A Polls
scipost.org, 2020.

Machine Learning at the Atomic Scale

Felix Musil, Michele Ceriotti
Chimia, 2019, 73 (12), 972–982.
DOI: 10.2533/chimia.2019.972

Non-Empirical Weighted Langevin Mechanics for the Potential Escape Problem: Parallel Algorithm and Application to the Argon Clusters

Yuri S. Nagornov, Ryosuke Akashi
Physica A: Statistical Mechanics and its Applications, 2019, 528, 121481.
DOI: 10.1016/j.physa.2019.121481

Learning Intermolecular Forces at Liquid-Vapor Interfaces

Samuel P. Niblett, Mirza Galib, David T. Limmer
2021.

Recursive Evaluation and Iterative Contraction of N-Body Equivariant Features

Jigyasa Nigam, Sergey Pozdnyakov, Michele Ceriotti
Journal of Chemical Physics, 2020, 153 (12), 121101.
DOI: 10.1063/5.0021116

Quantum-Accurate Magneto-Elastic Predictions with Classical Spin-Lattice Dynamics

Svetoslav Nikolov, Mitchell A. Wood, Attila Cangi, Jean-Bernard Maillet, Mihai-Cosmin Marinica, Aidan P. Thompson, Michael P. Desjarlais, Julien Tranchida
2021.

Ab Initio Phase Diagram and Nucleation of Gallium

Haiyang Niu, Luigi Bonati, Pablo M. Piaggi, Michele Parrinello
Nature Communications, 2020, 11 (1), 2654.
DOI: 10.1038/s41467-020-16372-9

The MLIP Package: Moment Tensor Potentials with MPI and Active Learning

Ivan S. Novikov, Konstantin Gubaev, Evgeny Podryabinkin, Alexander Shapeev
Machine Learning-Science and Technology, 2021, 2 (2), 025002.
DOI: 10.1088/2632-2153/abc9fe

Modeling H2O/Rutile-TiO2(110) Potential Energy Surfaces with Deep Networks

Stefan Oehmcke, Thomas Teusch, Thorben Petersen, Thorsten Kluener, Oliver Kramer
2020 International Joint Conference on Neural Networks (Ijcnn), 2020.

Catalytic Materials and Chemistry Development Using a Synergistic Combination of Machine Learning and Ab Initio Methods

Nilesh Varadan Orupattur, Samir H. Mushrif, Vinay Prasad
Computational Materials Science, 2020, 174, 109474.
DOI: 10.1016/j.commatsci.2019.109474

A Bin and Hash Method for Analyzing Reference Data and Descriptors in Machine Learning Potentials

Martin Leandro Paleico, Joerg Behler
Machine Learning-Science and Technology, 2021, 2 (3), 037001.
DOI: 10.1088/2632-2153/abe663

Machine Learning Assisted Free Energy Simulation of Solution–Phase and Enzyme Reactions

X Pan, R Van, E Epifanovsky, J Ho, J Huang, J Pu
2021.

A DFT Accurate Machine Learning Description of Molten ZnCl2 and Its Mixtures: 1. Potential Development and Properties Prediction of Molten ZnCl2

Gechuanqi Pan, Pin Chen, Hui Yan, Yutong Lu
Computational Materials Science, 2020, 185, 109955.
DOI: 10.1016/j.commatsci.2020.109955

A DFT Accurate Machine Learning Description of Molten ZnCl2 and Its Mixtures: 2. Potential Development and Properties Prediction of ZnCl2-NaCl-KCl Ternary Salt for CSP

Gechuanqi Pan, Jing Ding, Yunfei Du, Duu-Jong Lee, Yutong Lu
Computational Materials Science, 2021, 187, 110055.
DOI: 10.1016/j.commatsci.2020.110055

Accurate and Scalable Graph Neural Network Force Field and Molecular Dynamics with Direct Force Architecture

Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Boris Kozinsky, Jonathan P. Mailoa
Npj Computational Materials, 2021, 7 (1), 73.
DOI: 10.1038/s41524-021-00543-3

Accurate and Scalable Multi-Element Graph Neural Network Force Field and Molecular Dynamics with Direct Force Architecture

Cheol Woo Park, Mordechai Kornbluth, Jonathan Vandermause, Chris Wolverton, Jonathan P Mailoa
, 33.

A Fourier-Based Machine Learning Technique with Application in Engineering

Michael Peigney
International Journal for Numerical Methods in Engineering, 2021, 122 (3), 866–897.
DOI: 10.1002/nme.6565

Efficient Long-Range Convolutions for Point Clouds

Yifan Peng, Lin Lin, Lexing Ying, Leonardo Zepeda-Núñez
2020.

Simulations Meet Machine Learning in Structural Biology

Adrià Pérez, Gerard Martínez-Rosell, Gianni De Fabritiis
Current Opinion in Structural Biology, 2018, 49, 139–144.
DOI: 10/gdnsnp

Enhancing the Formation of Ionic Defects to Study the Ice Ih/XI Transition with Molecular Dynamics Simulations

Pablo M. Piaggi, Roberto Car
Molecular Physics, 2021.
DOI: 10.1080/00268976.2021.1916634

Phase Equilibrium of Water with Hexagonal and Cubic Ice Using the SCAN Functional

Pablo M. Piaggi, Athanassios Z. Panagiotopoulos, Pablo G. Debenedetti, Roberto Car
Journal of Chemical Theory and Computation, 2021, 17 (5), 3065–3077.
DOI: 10.1021/acs.jctc.1c00041

Machine Learning Force Fields: Recent Advances and Remaining Challenges

Igor Poltavsky, Alexandre Tkatchenko
Journal of Physical Chemistry Letters, 2021, 12 (28), 6551–6564.
DOI: 10.1021/acs.jpclett.1c01204

On Application of Deep Learning to Simplified Quantum-Classical Dynamics in Electronically Excited States

Evgeny Posenitskiy, Fernand Spiegelman, Didier Lemoine
Machine Learning-Science and Technology, 2021, 2 (3), 035039.
DOI: 10.1088/2632-2153/abfe3f

Atomistic Simulations of the Thermal Conductivity of Liquids

Marcello Puligheddu, Giulia Galli
Physical Review Materials, 2020, 4 (5), 053801.
DOI: 10.1103/PhysRevMaterials.4.053801

A Comprehensive Assessment of Empirical Potentials for Carbon Materials

Cheng Qian, Ben McLean, Daniel Hedman, Feng Ding
APL Materials, 2021, 9 (6).
DOI: 10.1063/5.0052870

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller
Journal of Chemical Physics, 2020, 153 (12), 124111.
DOI: 10.1063/5.0021955

Interaction Energy Prediction of Organic Molecules Using Deep Tensor Neural Network

Yuan Qi, Hong Ren, Hong Li, Ding-lin Zhang, Hong-qiang Cui, Jun-ben Weng, Guo-hui Li, Gui-yan Wang, Yan Li
Chinese Journal of Chemical Physics, 2021, 34 (1), 112–124.
DOI: 10.1063/1674-0068/cjcp2009163

Machine Learning of Atomic Forces from Quantum Mechanics: A Model Based on Pairwise Interatomic Forces

I Ramzan, L Kong, R A Bryce, N A Burton
, 39.

Unsupervised Learning of Atomic Environments from Simple Features

Wesley F. Reinhart
Computational Materials Science, 2021, 196, 110511.
DOI: 10.1016/j.commatsci.2021.110511

Halogen Bond Structure and Dynamics from Molecular Simulations

Richard C. Remsing, Michael L. Klein
Journal of Physical Chemistry B, 2019, 123 (29), 6266–6273.
DOI: 10.1021/acs.jpcb.9b04820

Machine Learning Kinetic Energy Functional for a One-Dimensional Periodic System

Hong-Bin Ren, Lei Wang, Xi Dai
Chinese Physics Letters, 2021, 38 (5), 050701.
DOI: 10.1088/0256-307X/38/5/050701

Spatial Density Neural Network Force Fields with First-Principles Level Accuracy and Application to Thermal Transport

Alejandro Rodriguez, Yinqiao Liu, Ming Hu
Physical Review B, 2020, 102 (3), 035203.
DOI: 10.1103/PhysRevB.102.035203

Biophysical Analysis of SARS-CoV-2 Transmission and Theranostic Development via N Protein Computational Characterization

Godfred O. Sabbih, Maame A. Korsah, Jaison Jeevanandam, Michael K. Danquah
Biotechnology Progress, 2021, 37 (2), e3096.
DOI: 10.1002/btpr.3096

Active Learning of Potential-Energy Surfaces of Weakly-Bound Complexes with Regression-Tree Ensembles

Yahya Saleh, Vishnu Sanjay, Armin Iske, Andrey Yachmenev, Jochen Küpper
2021.

Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution

Janani Sampath, Sarah Alamdari, Jim Pfaendtner
Chemistry of Materials, 2020, 32 (19), 8043–8059.
DOI: 10.1021/acs.chemmater.0c01891

Scalable Neural Networks for the Efficient Learning of Disordered Quantum Systems

N. Saraceni, S. Cantori, S. Pilati
Physical Review E, 2020, 102 (3).
DOI: 10.1103/physreve.102.033301

Molecular Force Fields with Gradient-Domain Machine Learning: Construction and Application to Dynamics of Small Molecules with Coupled Cluster Forces

Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
The Journal of Chemical Physics, 2019, 150 (11), 114102.
DOI: 10/ghqtd7

Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

Christoph Scherer, Rene Scheid, Denis Andrienko, Tristan Bereau
Journal of Chemical Theory and Computation, 2020, 16 (5), 3194–3204.
DOI: 10.1021/acs.jctc.9b01256

From DFT to Machine Learning: Recent Approaches to Materials Science-a Review

Gabriel R. Schleder, Antonio C. M. Padilha, Carlos Mera Acosta, Marcio Costa, Adalberto Fazzio
Journal of Physics-Materials, 2019, 2 (3), 032001.
DOI: 10.1088/2515-7639/ab084b

Recent Advances and Applications of Machine Learning in Solid-State Materials Science

Jonathan Schmidt, Mario R. G. Marques, Silvana Botti, Miguel A. L. Marques
Npj Computational Materials, 2019, 5, 83.
DOI: 10.1038/s41524-019-0221-0

Committee Neural Network Potentials Control Generalization Errors and Enable Active Learning

Christoph Schran, Krystof Brezina, Ondrej Marsalek
Journal of Chemical Physics, 2020, 153 (10), 104105.
DOI: 10.1063/5.0016004

Transferability of Machine Learning Potentials: Protonated Water Neural Network Potential Applied to the Protonated Water Hexamer

Christoph Schran, Fabien Brieuc, Dominik Marx
Journal of Chemical Physics, 2021, 154 (5), 051101.
DOI: 10.1063/5.0035438

Schnet–a Deep Learning Architecture for Molecules and Materials

Kristof T. Schütt, Huziel E. Sauceda, P.-J. Kindermans, Alexandre Tkatchenko, K.-R. Müller
The Journal of Chemical Physics, 2018, 148 (24), 241722.
DOI: 10.1063/1.5019779

SchNetPack: A Deep Learning Toolbox For Atomistic Systems

K. T. Schütt, P. Kessel, M. Gastegger, K. Nicoli, A. Tkatchenko, K.-R. Müller
Journal of Chemical Theory and Computation, 2019, 15 (1), 448–455.
DOI: 10/gfrbqm

Differentiable Sampling of Molecular Geometries with Uncertainty-Based Adversarial Attacks

Daniel Schwalbe-Koda, Aik Rui Tan, Rafael Gómez-Bombarelli
2021.

Topology Automated Force-Field Interactions (TAFFI): A Framework for Developing Transferable Force-Fields

Bumjoon Seo, Zih-Yu Lin, Qiyuan Zhao, Michael A Webb, M Savoie
, 43.

Anharmonic Raman Spectra Simulation of Crystals from Deep Neural Networks

Honghui Shang, Haidi Wang
Aip Advances, 2021, 11 (3), 035105.
DOI: 10.1063/5.0040190

Modelling Bulk Electrolytes and Electrolyte Interfaces with Atomistic Machine Learning

Yunqi Shao, Lisanne Knijff, Florian M. Dietrich, Kersti Hermansson, Chao Zhang
Batteries \& Supercaps, 2021, 4 (4), 585–595.
DOI: 10.1002/batt.202000262

PiNN: A Python Library for Building Atomic Neural Networks of Molecules and Materials

Yunqi Shao, Matti Hellstrom, Pavlin D. Mitev, Lisanne Knijff, Chao Zhang
Journal of Chemical Information and Modeling, 2020, 60 (3), 1184–1193.
DOI: 10.1021/acs.jcim.9b00994

Elinvar Effect in Beta-Ti Simulated by on-the-Fly Trained Moment Tensor Potential

Alexander Shapeev, Evgeny Podryabinkin, Konstantin Gubaev, Ferenc Tasnadi, Igor A. Abrikosov
New Journal of Physics, 2020, 22 (11), 113005.
DOI: 10.1088/1367-2630/abc392

PFNN: A Penalty-Free Neural Network Method for Solving a Class of Second-Order Boundary-Value Problems on Complex Geometries

Hailong Sheng, Chao Yang
Journal of Computational Physics, 2021, 428, 110085.
DOI: 10.1016/j.jcp.2020.110085

Quantum Trajectory Mean-Field Method for Nonadiabatic Dynamics in Photochemistry

Lin Shen, Diandong Tang, Binbin Xie, Wei-Hai Fang
Journal of Physical Chemistry A, 2019, 123 (34), 7337–7350.
DOI: 10.1021/acs.jpca.9b03480

Application of Genetic Algorithm in the Global Structure Optimization of Catalytic System

Xiangcheng Shi, Zhijian Zhao, Jinlong Gong
Huagong Xuebao/CIESC Journal, 2021, 72 (1), 27–41.
DOI: 10.11949/0438-1157.20201037

Learning Gradient Fields for Molecular Conformation Generation

Chence Shi, Shitong Luo, Minkai Xu, Jian Tang
2021.

Computational and Training Requirements for Interatomic Potential Based on Artificial Neural Network for Estimating Low Thermal Conductivity of Silver Chalcogenides

Kohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo
Journal of Chemical Physics, 2020, 153 (23), 234301.
DOI: 10.1063/5.0027058

Estimating Thermal Conductivity of α-Ag2Se Using ANN Potential with Chebyshev Descriptor

Kohei Shimamura, Yusuke Takeshita, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo
Chemical Physics Letters, 2021, 778, 138748.
DOI: 10/gj42cx

Guidelines for Creating Artificial Neural Network Empirical Interatomic Potential from First-Principles Molecular Dynamics Data under Specific Conditions and Its Application to Alpha-Ag2Se

Kohei Shimamura, Shogo Fukushima, Akihide Koura, Fuyuki Shimojo, Masaaki Misawa, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta, Takashi Matsubara, Shigenori Tanaka
Journal of Chemical Physics, 2019, 151 (12), 124303.
DOI: 10.1063/1.5116420

Water Dipole and Quadrupole Moment Contributions to the Ion Hydration Free Energy by the Deep Neural Network Trained with Ab Initio Molecular Dynamics Data

Yu Shi, Carrie C Doyle, Thomas L Beck
, 20.

Wavelet Scattering Networks for Atomistic Systems with Extrapolation of Material Properties

Paul Sinz, Michael W. Swift, Xavier Brumwell, Jialin Liu, Kwang Jin Kim, Yue Qi, Matthew Hirn
Journal of Chemical Physics, 2020, 153 (8), 084109.
DOI: 10.1063/5.0016020

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide

Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy, Marius Stan, Gábor Csányi, Álvaro Vázquez-Mayagoitia, Chris J. Benmore
Physical Review Letters, 2021, 126 (15), 156002.
DOI: 10/gkx66f

The ANI-1ccx and ANI-1x Data Sets, Coupled-Cluster and Density Functional Theory Properties for Molecules

Justin S. Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E. Roitberg, Olexandr Isayev, Sergei Tretiak
Scientific Data, 2020, 7 (1), 134.
DOI: 10/gh48xw

Raman Spectrum and Polarizability of Liquid Water from Deep Neural Networks

Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang, Roberto Car
Physical Chemistry Chemical Physics, 2020, 22 (19), 10592–10602.
DOI: 10.1039/d0cp01893g

Machine Learning for Metallurgy II. A Neural-Network Potential for Magnesium

Markus Stricker, Binglun Yin, Eleanor Mak, W. A. Curtin
Physical Review Materials, 2020, 4 (10).
DOI: 10.1103/physrevmaterials.4.103602

Toward Exascale Design of Soft Mesoscale Materials

S Succi, G Amati, F Bonaccorso, M Lauricella - Journal of …, undefined 2020
Elsevier, 2020.

Gaussian Process Model of 51-Dimensional Potential Energy Surface for Protonated Imidazole Dimer

Hiroki Sugisawa, Tomonori Ida, R. Krems
Journal of Chemical Physics, 2020, 153 (11), 114101.
DOI: 10.1063/5.0023492

TeaNet: Universal Neural Network Interatomic Potential Inspired by Iterative Electronic Relaxations

So Takamoto, Satoshi Izumi, Ju Li
2019.

Interatomic Potential in a Simple Dense Neural Network Representation

Ka-Ming Tam, Nicholas Walker, Samuel Kellar, Mark Jarrell
2019.

Prediction of Formation Energies of Large-Scale Disordered Systems via Active-Learning-Based Executions of Ab Initio Local-Energy Calculations: A Case Study on a Fe Random Grain Boundary Model with Millions of Atoms

Tomoyuki Tamura, Masayuki Karasuyama
Physical Review Materials, 2020, 4 (11).
DOI: 10.1103/physrevmaterials.4.113602

ChebNet: Efficient and Stable Constructions of Deep Neural Networks with Rectified Power Units Using Chebyshev Approximations

Shanshan Tang, Bo Li, Haijun Yu
2019.

Development of Interatomic Potential for Al-Tb Alloys Using a Deep Neural Network Learning Method

L. Tang, Z. J. Yang, T. Q. Wen, K. M. Ho, M. J. Kramer, C. Z. Wang
Physical Chemistry Chemical Physics, 2020, 22 (33), 18467–18479.
DOI: 10.1039/d0cp01689f

Short- and Medium-Range Orders in Al90Tb10 Glass and Their Relation to the Structures of Competing Crystalline Phases

L. Tang, Z. J. Yang, T. Q. Wen, K. M. Ho, M. J. Kramer, C. Z. Wang
Acta Materialia, 2021, 204, 116513.
DOI: 10.1016/j.actamat.2020.116513

Machine Learning and Molecular Design of Self-Assembling Pi-Conjugated Oligopeptides

Bryce A. Thurston, Andrew L. Ferguson
Molecular Simulation, 2018, 44 (11), 930–945.
DOI: 10.1080/08927022.2018.1469754

The Repetitive Local Sampling and the Local Distribution Theory

Pu Tian
, 32.

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery

Qunchao Tong, Pengyue Gao, Hanyu Liu, Yu Xie, Jian Lv, Yanchao Wang, Jijun Zhao
Journal of Physical Chemistry Letters, 2020, 11 (20), 8710–8720.
DOI: 10.1021/acs.jpclett.0c02357

Machine Learning Metadynamics Simulation of Reconstructive Phase Transition

Qunchao Tong, Xiaoshan Luo, Adebayo A. Adeleke, Pengyue Gao, Yu Xie, Hanyu Liu, Quan Li, Yanchao Wang, Jian Lv, Yansun Yao, Yanming Ma
Physical Review B, 2021, 103 (5), 054107.
DOI: 10/gmf5zv

Geometric Prediction: Moving Beyond Scalars

Raphael J. L. Townshend, Brent Townshend, Stephan Eismann, Ron O. Dror
2020.

Transferrable End-to-End Learning for Protein Interface Prediction

Raphael JL Townshend, Rishi Bedi, Ron O. Dror
2018.

A Machine Learning Based Deep Potential for Seeking the Low-Lying Candidates of Al Clusters

P. Tuo, X. B. Ye, B. C. Pan
Journal of Chemical Physics, 2020, 152 (11), 114105.
DOI: 10.1063/5.0001491

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

Oliver T. Unke, Markus Meuwly
Journal of Chemical Theory and Computation, 2019, 15 (6), 3678–3693.
DOI: 10.1021/acs.jctc.9b00181

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

Oliver T. Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel E. Sauceda, Klaus-Robert Müller
2021.

Active Learning of Reactive Bayesian Force Fields: Application to Heterogeneous Hydrogen-Platinum Catalysis Dynamics

J Vandermause, Y Xie, JS Lim, CJ Owen - arXiv preprint arXiv …, undefined 2021
arxiv.org, 2021.

On-the-Fly Active Learning of Interpretable Bayesian Force Fields for Atomistic Rare Events

Jonathan Vandermause, Steven B. Torrisi, Simon Batzner, Yu Xie, Lixin Sun, Alexie M. Kolpak, Boris Kozinsky
Npj Computational Materials, 2020, 6 (1), 20.
DOI: 10.1038/s41524-020-0283-z

On-the-Fly Bayesian Active Learning of Interpretable Force-Fields for Atomistic Rare Events

J Vandermause, SB Torrisi, S Batzner
projects.iq.harvard.edu, 2019.

Challenges for Machine Learning Force Fields in Reproducing Potential Energy Surfaces of Flexible Molecules

Valentin Vassilev-Galindo, Gregory Fonseca, Igor Poltavsky, Alexandre Tkatchenko
Journal of Chemical Physics, 2021, 154 (9), 094119.
DOI: 10.1063/5.0038516

Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces

S. Venturi, R. L. Jaffe, M. Panesi
Journal of Physical Chemistry A, 2020, 124 (25), 5129–5146.
DOI: 10.1021/acs.jpca.0c02395

Molecular Modeling Investigations of Sorption and Diffusion of Small Molecules in Glassy Polymers

Niki Vergadou, Doros N. Theodorou
Membranes, 2019, 9 (8), 98.
DOI: 10.3390/membranes9080098

Faster Exact Exchange in Periodic Systems Using Single-Precision Arithmetic

John Vinson
Journal of Chemical Physics, 2020, 153 (20), 204106.
DOI: 10.1063/5.0030493

Combining the Fragmentation Approach and Neural Network Potential Energy Surfaces of Fragments for Accurate Calculation of Protein Energy

Zhilong Wang, Yanqiang Han, Jinjin Li, Xiao He
Journal of Physical Chemistry B, 2020, 124 (15), 3027–3035.
DOI: 10.1021/acs.jpcb.0c01370

Complex Reaction Network Thermodynamic and Kinetic Autoconstruction Based on \emphAb Initio Statistical Mechanics: A Case Study of O \textsubscript2 Activation on Ag \textsubscript4 Clusters

Weiqi Wang, Xiangyue Liu, Jesús Pérez-Ríos
The Journal of Physical Chemistry A, 2021, 125 (25), 5670–5680.
DOI: 10/gmfw5m

Crystal Structure Prediction of Binary Alloys via Deep Potential

Haidi Wang, Yuzhi Zhang, Linfeng Zhang, Han Wang
Frontiers in Chemistry, 2020, 8, 589795.
DOI: 10.3389/fchem.2020.589795

Deep Learning Inter-Atomic Potential Model for Accurate Irradiation Damage Simulations

Hao Wang, Xun Guo, Linfeng Zhang, Han Wang, Jianming Xue
Applied Physics Letters, 2019, 114 (24), 244101.
DOI: 10.1063/1.5098061

Deep-Learning Interatomic Potential for Irradiation Damage Simulations in MoS2 with Ab Initial Accuracy

Hao Wang, Xun Guo, Jianming Xue
2020.

DeePMD-Kit: A Deep Learning Package for Many-Body Potential Energy Representation and Molecular Dynamics

Han Wang, Linfeng Zhang, Jiequn Han, Weinan E
Computer Physics Communications, 2018, 228, 178–184.
DOI: 10.1016/j.cpc.2018.03.016

Differentiable Molecular Simulations for Control and Learning

Wujie Wang, Simon Axelrod, Rafael Gómez-Bombarelli
2020.

Electronically Driven 1D Cooperative Diffusion in a Simple Cubic Crystal

Yong Wang, Junjie Wang, Andreas Hermann, Cong Liu, Hao Gao, Erio Tosatti, Hui-Tian Wang, Dingyu Xing, Jian Sun
Physical Review X, 2021, 11 (1), 011006.
DOI: 10.1103/PhysRevX.11.011006

Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

Jiang Wang, Stefan Chmiela, Klaus-Robert Mueller, Frank Noe, Cecilia Clementi
Journal of Chemical Physics, 2020, 152 (19).
DOI: 10.1063/5.0007276

An Extendible, Graph-Neural-Network-Based Approach for Accurate Force Field Development of Large Flexible Organic Molecules

Xufei Wang, Yuanda Xu, Han Zheng, Kuang Yu
arxiv.org, 2021.

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adria Perez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noe, Cecilia Clementi
Acs Central Science, 2019, 5 (5), 755–767.
DOI: 10.1021/acscentsci.8b00913

Multi-Body Effects in a Coarse-Grained Protein Force Field

Jiang Wang, Nicholas Charron, Brooke Husic, Simon Olsson, Frank Noé, Cecilia Clementi
Journal of Chemical Physics, 2021, 154 (16).
DOI: 10.1063/5.0041022

Predicting Adsorption Ability of Adsorbents at Arbitrary Sites for Pollutants Using Deep Transfer Learning

Zhilong Wang, Haikuo Zhang, Jiahao Ren, Xirong Lin, Tianli Han, Jinyun Liu, Jinjin Li
Npj Computational Materials, 2021, 7 (1), 19.
DOI: 10.1038/s41524-021-00494-9

Symmetry-Adapted Graph Neural Networks for Constructing Molecular Dynamics Force Fields

Zun Wang, Chong Wang, Sibo Zhao, Shiqiao Du, Yong Xu, Bing-Lin Gu, Wenhui Duan
2021.

Integrating Machine Learning with Physics-Based Modeling

E Weinan, Jiequn Han, Zhang Linfeng
2020.

Properties of Alpha-Brass Nanoparticles. 1. Neural Network Potential Energy Surface

Jan Weinreich, Anton Roemer, Martin Leandro Paleico, Joerg Behler
Journal of Physical Chemistry C, 2020, 124 (23), 12682–12695.
DOI: 10.1021/acs.jpcc.0c00559

Development of a Deep Machine Learning Interatomic Potential for Metalloid-Containing Pd-Si Compounds

Tongqi Wen, Cai-Zhuang Wang, M. J. Kramer, Yang Sun, Beilin Ye, Haidi Wang, Xueyuan Liu, Chao Zhang, Feng Zhang, Kai-Ming Ho, Nan Wang
Physical Review B, 2019, 100 (17), 174101.
DOI: 10.1103/PhysRevB.100.174101

Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

Julia Westermayr, Michael Gastegger, Philipp Marquetand
Journal of Physical Chemistry Letters, 2020, 11 (10), 3828–3834.
DOI: 10.1021/acs.jpclett.0c00527

Machine Learning and Excited-State Molecular Dynamics

Julia Westermayr, Philipp Marquetand
Machine Learning: Science and Technology, 2020, 1 (4), 043001.
DOI: 10/gksxpp

Atom-Density Representations for Machine Learning

Michael J. Willatt, Flix Musil, Michele Ceriotti
Journal of Chemical Physics, 2019, 150 (15), 154110.
DOI: 10.1063/1.5090481

Feature Optimization for Atomistic Machine Learning Yields A Data-Driven Construction of the Periodic Table of the Elements

Michael J. Willatt, Félix Musil, Michele Ceriotti
Physical Chemistry Chemical Physics, 2018, 20 (47), 29661–29668.
DOI: 10/gfz26d

Targeted Free Energy Estimation via Learned Mappings

Peter Wirnsberger, Andrew J. Ballard, George Papamakarios, Stuart Abercrombie, Sebastien Racaniere, Alexander Pritzel, Danilo Jimenez Rezende, Charles Blundell
Journal of Chemical Physics, 2020, 153 (14), 144112.
DOI: 10.1063/5.0018903

Active Learning Approach to Optimization of Experimental Control

Y Wu, Z Meng, K Wen, C Mi, Zhang J
iopscience.iop.org.

Deep Learning of Accurate Force Field of Ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Physical Review B, 2021, 103 (2), 024108.
DOI: 10.1103/PhysRevB.103.024108

Deep Learning of Accurate Force Field of Ferroelectric HfO2

Jing Wu, Yuzhi Zhang, Linfeng Zhang, Shi Liu
Physical Review B, 2021, 103 (2), 024108.
DOI: 10.1103/PhysRevB.103.024108

Modeling of Metal Nanoparticles: Development of Neural-Network Interatomic Potential Inspired by Features of the Modified Embedded-Atom Method

Feifeng Wu, Hang Min, Yanwei Wen, Rong Chen, Yunkun Zhao, Mike Ford, Bin Shan
Physical Review B, 2020, 102 (14), 144107.
DOI: 10.1103/PhysRevB.102.144107

High-Throughput Study of Lattice Thermal Conductivity in Binary Rocksalt and Zinc Blende Compounds Including Higher-Order Anharmonicity

Yi Xia, Vinay Hegde, Koushik Pal, Xia Hua, Dale Gaines, Shane Patel, Jiangang He, Muratahan Aykol, Chris Wolverton
Physical Review X, 2020, 10 (4), 041029.
DOI: 10.1103/PhysRevX.10.041029

Ab-Initio Study of Interacting Fermions at Finite Temperature with Neural Canonical Transformation

Hao Xie, Linfeng Zhang, Lei Wang
arxiv.org, 2021.

Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene

Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, Boris Kozinsky
Npj Computational Materials, 2021, 7 (1), 40.
DOI: 10.1038/s41524-021-00510-y

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, Jeffrey C. Grossman
Nature Communications, 2019, 10, 2667.
DOI: 10.1038/s41467-019-10663-6

Automated Construction of Neural Network Potential Energy Surface: The Enhanced Self-Organizing Incremental Neural Network Deep Potential Method

Mingyuan Xu, Tong Zhu, John Z H Zhang
, 18.

Automatically Constructed Neural Network Potentials for Molecular Dynamics Simulation of Zinc Proteins

Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Frontiers in Chemistry, 2021, 9, 692200.
DOI: 10.3389/fchem.2021.692200

A Deep-Learning Potential for Crystalline and Amorphous Li-Si Alloys

Nan Xu, Yao Shi, Yi He, Qing Shao
Journal of Physical Chemistry C, 2020, 124 (30), 16278–16288.
DOI: 10.1021/acs.jpcc.0c03333

Ab Initio Molecular Dynamics Simulation of Zinc Metalloproteins with Enhanced Self-Organizing Incremental High Dimensional Neural Network

Mingyuan Xu, Tong Zhu, John Z H Zhang
, 27.

Isotope Effects in Molecular Structures and Electronic Properties of Liquid Water via Deep Potential Molecular Dynamics Based on the SCAN Functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Physical Review B, 2020, 102 (21), 214113.
DOI: 10.1103/PhysRevB.102.214113

Isotope Effects in Molecular Structures and Electronic Properties of Liquid Water via Deep Potential Molecular Dynamics Based on the SCAN Functional

Jianhang Xu, Chunyi Zhang, Linfeng Zhang, Mohan Chen, Biswajit Santra, Xifan Wu
Physical Review B, 2020, 102 (21), 214113.
DOI: 10.1103/PhysRevB.102.214113

Molecular Dynamics Simulation of Zinc Ion in Water with an Ab Initio Based Neural Network Potential

Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Journal of Physical Chemistry A, 2019, 123 (30), 6587–6595.
DOI: 10.1021/acs.jpca.9b04087

De Novo Molecule Design through Molecular Generative Model Conditioned by 3D Information of Protein Binding Sites

Mingyuan Xu, Ting Ran, Hongming Chen
, 25.

Optimizing Training Data Set for the Machine Learning Potential of Li-Si Alloys via Structural Similarity-Based Screening

Nan Xu, Chen Li, Yao Shi, Qing Shao, Yi He
arxiv.org, 2021.

Perspective on Computational Reaction Prediction Using Machine Learning Methods in Heterogeneous Catalysis

Jiayan Xu, Xiao-Ming Cao, P. Hu
Physical Chemistry Chemical Physics, 2021, 23 (19), 11155–11179.
DOI: 10.1039/d1cp01349a

Using Metadynamics to Build Neural Network Potentials for Reactive Events: The Case of Urea Decomposition in Water

M Yang, L Bonati, D Polino, Parrinello M
Elsevier, 2021.

Construction of a Neural Network Energy Function for Protein Physics

Huan Yang, Zhaoping Xiong, Francesco Zonta
2021.
DOI: 10.1101/2021.04.26.441401

Role of Water in the Reaction Mechanism and Endo/Exo Selectivity of 1,3-Dipolar Cycloadditions Elucidated by Quantum Chemistry and Machine Learning

Xin Yang, Jun Zou, Yifei Wang, Ying Xue, Shengyong Yang
Chemistry-a European Journal, 2019, 25 (35), 8289–8303.
DOI: 10.1002/chem.201900617

Active Learning Algorithm for Computational Physics

J Yao, Y Wu, J Koo, B Yan, Zhai H
APS, 2020, 2 (1), 13287.
DOI: 10.1103/physrevresearch.2.013287

Nuclear Quantum Effect and Its Temperature Dependence in Liquid Water from Random Phase Approximation via Artificial Neural Network

Yi Yao, Yosuke Kanai
The Journal of Physical Chemistry Letters, 2021, 12 (27), 6354–6362.
DOI: 10/gk5v27

Atomic Energy Mapping of Neural Network Potential

Dongsun Yoo, Kyuhyun Lee, Wonseok Jeong, Dongheon Lee, Satoshi Watanabe, Seungwu Han
Physical Review Materials, 2019, 3 (9), 093802.
DOI: 10.1103/PhysRevMaterials.3.093802

A Transferable Active-Learning Strategy for Reactive Molecular Force Fields

Tom A. Young, Tristan Johnston-Wood, Volker L. Deringer, Fernanda Duarte
Chemical Science, 2021.
DOI: 10.1039/d1sc01825f

When Do Short-Range Atomistic Machine-Learning Models Fall Short?

Shuwen Yue, Maria Carolina Muniz, Marcos F. Calegari Andrade, Linfeng Zhang, Roberto Car, Athanassios Z. Panagiotopoulos
The Journal of Chemical Physics, 2021, 154 (3), 034111.
DOI: 10/gkcq6f

Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

J Zeng, L Zhang, H Wang, T Zhu
2020.

Development of Range-Corrected Deep Learning Potentials for Fast, Accurate Quantum Mechanical/Molecular Mechanical Simulations of Chemical Reactions in Solution

J Zeng, TJ Giese, Ş Ekesan, DM York
2021.

Complex Reaction Processes in Combustion Unraveled by Neural Network-Based Molecular Dynamics Simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Nature Communications, 2020, 11 (1), 5713.
DOI: 10.1038/s41467-020-19497-z

Complex Reaction Processes in Combustion Unraveled by Neural Network-Based Molecular Dynamics Simulation

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John Z. H. Zhang
Nature Communications, 2020, 11 (1), 5713.
DOI: 10.1038/s41467-020-19497-z

Exploring the Chemical Space of Linear Alkane Pyrolysis via Deep Potential GENerator

Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu
Energy \& Fuels, 2021, 35 (1), 762–769.
DOI: 10.1021/acs.energyfuels.0c03211

Neural Network Based in Silico Simulation of Combustion Reactions

Jinzhe Zeng, Liqun Cao, Mingyuan Xu, Tong Zhu, John ZH Zhang
arxiv.org, 2019.

Deep Density: Circumventing the Kohn-Sham Equations via Symmetry Preserving Neural Networks

Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin
Elsevier, 2019.

Active Learning of Many-Body Configuration Space: Application to the Cs+-Water MB-Nrg Potential Energy Function as a Case Study

Yaoguang Zhai, Alessandro Caruso, Sicun Gao, Francesco Paesani
Journal of Chemical Physics, 2020, 152 (14), 144103.
DOI: 10.1063/5.0002162

BubbleNet: Inferring Micro-Bubble Dynamics with Semi-Physics-Informed Deep Learning

Hanfeng Zhai, Guohui Hu
2021.

Machine Learning for Multi-Scale Molecular Modeling: Theories, Algorithms, and Applications

L Zhang
2020.

Accelerating Atomistic Simulations with Piecewise Machine-Learned Ab Initio Potentials at a Classical Force Field-like Cost

Yaolong Zhang, Ce Hu, Bin Jiang
Physical Chemistry Chemical Physics, 2021, 23 (3), 1815–1821.
DOI: 10.1039/d0cp05089j

Active Learning of Uniformly Accurate Interatomic Potentials for Materials Simulation

Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, Weinan E
Physical Review Materials, 2019, 3 (2), 023804.
DOI: 10.1103/PhysRevMaterials.3.023804

Adaptive Coupling of a Deep Neural Network Potential to a Classical Force Field

Linfeng Zhang, Han Wang, Weinan E
The Journal of chemical physics, 2018, 149 (15), 154107.
DOI: 10.1063/1.5042714

Anomalous Phase Separation and Hidden Coarsening of Super-Clusters in the Falicov-Kimball Model

Sheng Zhang, Puhan Zhang, Gia-Wei Chern
2021.

Arrested Phase Separation in Double-Exchange Models: Machine-Learning Enabled Large-Scale Simulation

Puhan Zhang, Gia-Wei Chern
2021.

Bridging the Gap between Direct Dynamics and Globally Accurate Reactive Potential Energy Surfaces Using Neural Networks

Yaolong Zhang, Xueyao Zhou, Bin Jiang
Journal of Physical Chemistry Letters, 2019, 10 (6), 1185–1191.
DOI: 10.1021/acs.jpclett.9b00085

Crystallization of the P3Sn4 Phase upon Cooling P2Sn5 Liquid by Molecular Dynamics Simulation Using a Machine Learning Interatomic Potential

Chao Zhang, Yang Sun, Hai-Di Wang, Feng Zhang, Tong-Qi Wen, Kai-Ming Ho, Cai-Zhuang Wang
Journal of Physical Chemistry C, 2021, 125 (5), 3127–3133.
DOI: 10.1021/acs.jpcc.0c08873

DeePCG: Constructing Coarse-Grained Models via Deep Neural Networks

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E. Weinan
2018, 149 (3).
DOI: 10.1063/1.5027645

Deep Neural Network for the Dielectric Response of Insulators

Linfeng Zhang, Mohan Chen, Xifan Wu, Han Wang, E. Weinan, Roberto Car
Physical Review B, 2020, 102 (4), 041121.
DOI: 10.1103/PhysRevB.102.041121

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, E. Weinan
Physical Review Letters, 2018, 120 (14), 143001.
DOI: 10.1103/PhysRevLett.120.143001

DP-GEN: A Concurrent Learning Platform for the Generation of Reliable Deep Learning Based Potential Energy Models

Yuzhi Zhang, Haidi Wang, Weijie Chen, Jinzhe Zeng, Linfeng Zhang, Han Wang, E. Weinan
Computer Physics Communications, 2020, 253, 107206.
DOI: 10.1016/j.cpc.2020.107206

Efficient and Accurate Simulations of Vibrational and Electronic Spectra with Symmetry-Preserving Neural Network Models for Tensorial Properties

Yaolong Zhang, Sheng Ye, Jinxiao Zhang, Ce Hu, Jun Jiang, Bin Jiang
The Journal of Physical Chemistry B, 2020, 124 (33), 7284–7290.
DOI: 10.1021/acs.jpcb.0c06926

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

Yaolong Zhang, Ce Hu, Bin Jiang
Journal of Physical Chemistry Letters, 2019, 10 (17), 4962–4967.
DOI: 10.1021/acs.jpclett.9b02037

Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation

Yaolong Zhang, Ce Hu, Bin Jiang
Journal of Physical Chemistry Letters, 2019, 10 (17), 4962–4967.
DOI: 10.1021/acs.jpclett.9b02037

End-to-End Symmetry Preserving Inter-Atomic Potential Energy Model for Finite and Extended Systems

Linfeng Zhang, Jiequn Han, Han Wang, Wissam A. Saidi, Roberto Car, Weinan E
2018.
DOI: arXiv:1805.09003

Global Optimization of Chemical Cluster Structures: Methods, Applications, and Challenges

Jun Zhang, Vassiliki-Alexandra Glezakou
International Journal of Quantum Chemistry, 2021, 121 (7), e26553.
DOI: 10.1002/qua.26553

Isotope Effects in X-Ray Absorption Spectra of Liquid Water

Chunyi Zhang, Linfeng Zhang, Jianhang Xu, Fujie Tang, Biswajit Santra, Xifan Wu
Physical Review B, 2020, 102 (11), 115155.
DOI: 10.1103/PhysRevB.102.115155

A Linear Frequency Principle Model to Understand the Absence of Overfitting in Neural Networks

Yaoyu Zhang, Tao Luo, Zheng Ma, Zhi-Qin John Xu
Chinese Physics Letters, 2021, 38 (3), 038701.
DOI: 10.1088/0256-307X/38/3/038701

Machine Learning Dynamics of Phase Separation in Correlated Electron Magnets

Puhan Zhang, Preetha Saha, Gia-Wei Chern
2020.

Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales

Jun Zhang, Yaqiang Zhou, Yao-Kun Lei, Yi Isaac Yang, Yi Qin Gao
, 14.

Monge-Amp\$\textbackslash backslash\$ere Flow for Generative Modeling

Linfeng Zhang, Lei Wang
2018.

A Perspective on Deep Learning for Molecular Modeling and Simulations

Jun Zhang, Yao-Kun Lei, Zhen hZang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao
Journal of Physical Chemistry A, 2020, 124 (34), 6745–6763.
DOI: 10.1021/acs.jpca.0c04473

Phase Diagram of a Deep Potential Water Model

Linfeng Zhang, Han Wang, Roberto Car, E. Weinan
Physical Review Letters, 2021, 126 (23), 236001.
DOI: 10.1103/PhysRevLett.126.236001

Reinforced Dynamics for Enhanced Sampling in Large Atomic and Molecular Systems

Linfeng Zhang, Han Wang, Weinan E
The Journal of chemical physics, 2018, 148 (12), 124113.
DOI: 10.1063/1.5019675

Reinforcement Learning for Multi-Scale Molecular Modeling

Jun Zhang, Yao-Kun Lei, Yi Isaac Yang, Yi Qin Gao
, 26.

A Type of Generalization Error Induced by Initialization in Deep Neural Networks

Yaoyu Zhang, Zhi-Qin John Xu, Tao Luo, Zheng Ma
, 21.

Warm Dense Matter Simulation via Electron Temperature Dependent Deep Potential Molecular Dynamics

Yuzhi Zhang, Chang Gao, Qianrui Liu, Linfeng Zhang, Han Wang, Mohan Chen
Physics of Plasmas, 2020, 27 (12), 122704.
DOI: 10.1063/5.0023265

Learning the Physics of Pattern Formation from Images

Hongbo Zhao, Brian D. Storey, Richard D. Braatz, Martin Z. Bazant
Physical Review Letters, 2020, 124 (6), 060201.
DOI: 10.1103/PhysRevLett.124.060201

Theoretical Prediction on the Redox Potentials of Rare-Earth Ions by Deep Potentials

Jia Zhao, Wenshuo Liang, Guimin Lu
Ionics, 2021, 27 (5), 2079–2088.
DOI: 10/gmfwvw

Retention and Recycling of Deuterium in Liquid Lithium-Tin Slab Studied by First-Principles Molecular Dynamics

Daye Zheng, Zhen-Xiong Shen, Mohan Chen, Xinguo Ren, Lixin He
Journal of Nuclear Materials, 2021, 543, 152542.
DOI: 10.1016/j.jnucmat.2020.152542

Atomic-State-Dependent Screening Model for Hot and Warm Dense Plasmas

Fuyang Zhou, Yizhi Qu, Junwen Gao, Yulong Ma, Yong Wu, Jianguo Wang
Communications Physics, 2021, 4 (1), 148.
DOI: 10.1038/s42005-021-00652-x

Frame-Independent Vector-Cloud Neural Network for Nonlocal Constitutive Modelling on Arbitrary Grids

Xu-Hui Zhou, Jiequn Han, Heng Xiao
2021.

Structure and Dynamics of Supercooled Liquid Ge \textsubscript2 Sb \textsubscript2 Te \textsubscript5 from Machine‐Learning‐Driven Simulations

Yu-Xing Zhou, Han-Yi Zhang, Volker L. Deringer, Wei Zhang
physica status solidi (RRL) – Rapid Research Letters, 2021, 15 (3), 2000403.
DOI: 10/gmf6g6

Discriminating High-Pressure Water Phases Using Rare-Event Determined Ionic Dynamical Properties*

Lin Zhuan, Qijun Ye, Ding Pan, Xin-Zheng Li
Chinese Physics Letters, 2020, 37 (4), 043101.
DOI: 10.1088/0256-307X/37/4/043101

Development of Multimodal Machine Learning Potentials: Toward a Physics-Aware Artificial Intelligence

Tetiana Zubatiuk, Olexandr Isayev
Accounts of Chemical Research, 2021, 54 (7), 1575–1585.
DOI: 10.1021/acs.accounts.0c00868

Machine Learned Hückel Theory: Interfacing Physics and Deep Neural Networks

Tetiana Zubatiuk, Benjamin Nebgen, Nicholas Lubbers, Justin S. Smith, Roman Zubatyuk, Guoqing Zhou, Christopher Koh, Kipton Barros, Olexandr Isayev, Sergei Tretiak
The Journal of Chemical Physics, 2021, 154 (24), 244108.
DOI: 10.1063/5.0052857

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Yunxing Zuo, Chi Chen, Xiangguo Li, Zhi Deng, Yiming Chen, Joerg Behler, Gabor Csanyi, Alexander Shapeev, Aidan P. Thompson, Mitchell A. Wood, Shyue Ping Ong
Journal of Physical Chemistry A, 2020, 124 (4), 731–745.
DOI: 10.1021/acs.jpca.9b08723

Modified Embedded-Atom Method Potentials for the Plasticity and Fracture Behaviors of Unary Fcc Metals

ZH Aitken, V Sorkin, ZG Yu, S Chen, Z Wu, YW Zhang - Physical Review B, undefined 2021
APS.

Machine Learning and Computational Mathematics

E Weinan - arXiv preprint ArXiv:2009.14596, undefined 2020
arxiv.org, 1920.

Research on Microstructure and Physical Properties of Molten Carbonate Salt Based on Machine Learning

YANG Bo, L. U. Guimin
华东理工大学学报 (自然科学版), 2021, 1–11.

Machine Learning on Neutron and X-Ray Scattering and Spectroscopies

Zhantao Chen, Nina Andrejevic, Nathan C. Drucker, Thanh Nguyen, R. Patrick Xian, Tess Smidt, Yao Wang, Ralph Ernstorfer, D. Alan Tennant, Maria Chan, Mingda Li
Chemical Physics Reviews, 2021, 2 (3), 031301.
DOI: 10.1063/5.0049111

Deep Learning for Nonadiabatic Excited-State Dynamics

Wen-Kai Chen, Xiang-Yang Liu, Wei-Hai Fang, Pavlo O. Dral, Ganglong Cui
The journal of physical chemistry letters, 2018, 9 (23), 6702–6708.
DOI: 10.1021/acs.jpclett.8b03026

Building Machine Learning Force Fields of Proteins with Fragment-Based Approach and Transfer Learning

Zheng Cheng, Jiahui Du, Lei Zhang, Jing Ma, Wei Li, Shuhua Li
2021.

The Study of the Optical Phonon Frequency of 3C-SiC by Molecular Dynamics Simulations with Deep Neural Network Potential

Wei Chen, Liang-Sheng Li
Journal of Applied Physics, 2021, 129 (24), 244104.
DOI: 10.1063/5.0049464

On the Role of Gradients for Machine Learning of Molecular Energies and Forces

Anders S. Christensen, O. Anatole von Lilienfeld
Machine Learning: Science and Technology, 2020, 1 (4), 045018.
DOI: 10.1088/2632-2153/abba6f

Long-Lived Hot Electron in a Metallic Particle for Plasmonics and Catalysis: Ab Initio Nonadiabatic Molecular Dynamics with Machine Learning

Weibin Chu, Wissam A. Saidi, Oleg V. Prezhdo
ACS nano, 2020, 14 (8), 10608–10615.
DOI: 10.1021/acsnano.0c04736

Implementing a Neural Network Interatomic Model with Performance Portability for Emerging Exascale Architectures

Saaketh Desai, Samuel Temple Reeve, James F. Belak
2020.

Nonadiabatic Excited-State Dynamics with Machine Learning

Pavlo O. Dral, Mario Barbatti, Walter Thiel
The journal of physical chemistry letters, 2018, 9 (19), 5660–5663.
DOI: 10.1021/acs.jpclett.8b02469

Machine Learning and Computational Mathematics

Weinan E
2020.

Deterministic and Statistical Approaches to Quantum Chemistry

Alberto Fabrizio
2020.

The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety

Donal P. Finegan, Juner Zhu, Xuning Feng, Matt Keyser, Marcus Ulmefors, Wei Li, Martin Z. Bazant, Samuel J. Cooper
Joule, 2020.

Heat and Charge Transport in H 2 O at Ice-Giant Conditions from Ab Initio Molecular Dynamics Simulations

Federico Grasselli, Lars Stixrude, Stefano Baroni
Nature communications, 2020, 11 (1), 1–7.
DOI: 10.1038/s41467-020-17275-5

Transferable Machine-Learning Model of the Electron Density

Andrea Grisafi, Alberto Fabrizio, Benjamin Meyer, David M. Wilkins, Clemence Corminboeuf, Michele Ceriotti
ACS central science, 2018, 5 (1), 57–64.
DOI: 10.1021/acscentsci.8b00551

Accuracy, Transferability, and Efficiency of Coarse-Grained Models of Molecular Liquids

M. G. Guenza, M. Dinpajooh, J. McCarty, I. Y. Lyubimov
The Journal of Physical Chemistry B, 2018, 122 (45), 10257–10278.
DOI: 10.1021/acs.jpcb.8b06687

High-Throughput Production of Force-Fields for Solid-State Electrolyte Materials

Ryo Kobayashi, Yasuhiro Miyaji, Koki Nakano, Masanobu Nakayama
APL Materials, 2020, 8 (8), 081111.
DOI: 10.1063/5.0015373

Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory Based \$ Ab \$\$ Initio \$ Molecular Dynamics II: Extensions to the Isobaric-Isoenthalpic and Isobaric-Isothermal Ensembles

Hsin-Yu Ko, Biswajit Santra, Robert A. DiStasio Jr
2020.

Molecular Dynamics Simulations of Molten Magnesium Chloride Using Machine‐Learning‐Based Deep Potential

W Liang, G Lu, J Yu
Wiley Online Library, 2020.

A Deep Neural Network Interatomic Potential for Studying Thermal Conductivity of β-Ga2O3

Ruiyang Li, Zeyu Liu, Andrew Rohskopf, Kiarash Gordiz, Asegun Henry, Eungkyu Lee, Tengfei Luo
Applied Physics Letters, 2020, 117 (15), 152102.
DOI: 10.1063/5.0025051

Effects of Density and Composition on the Properties of Amorphous Alumina: A High-Dimensional Neural Network Potential Study

Wenwen Li, Yasunobu Ando, Satoshi Watanabe
The Journal of Chemical Physics, 2020, 153 (16), 164119.
DOI: 10.1063/5.0026289

Automatically Growing Global Reactive Neural Network Potential Energy Surfaces: A Trajectory-Free Active Learning Strategy

Qidong Lin, Yaolong Zhang, Bin Zhao, Bin Jiang
2020, 152 (15).
DOI: 10.1063/5.0004944

Active Learning for Robust, High-Complexity Reactive Atomistic Simulations

Rebecca K. RK Lindsey, LE Laurence E. Fried, N Goldman - The Journal of Chemical …, undefined 2020, Nir Goldman, Sorin Bastea
2020, 153 (13).
DOI: 10.1063/5.0021965

Future Directions of Chemical Theory and Computation

Yuyuan Lu, Geng Deng, Zhigang Shuai
Pure and Applied Chemistry, 2021.
DOI: 10.1515/pac-2020-1006

A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

Yulong Lu, Jianfeng Lu
2020.

Understanding Simple Liquids through Statistical and Deep Learning Approaches

A. Moradzadeh, N. R. Aluru
The Journal of Chemical Physics, 2021, 154 (20), 204503.
DOI: 10.1063/5.0046226

Atomistic Structure Learning Algorithm with Surrogate Energy Model Relaxation

HL Henrik Lund Mortensen, Søren Ager SA Meldgaard, Malthe Kjær Bisbo, Mads Peter V. Christiansen, Bjørk Hammer, MK Bisbo - Physical Review B, undefined 2020
2020, 102 (7).
DOI: 10.1103/physrevb.102.075427

Machine Learning in Nano-Scale Biomedical Engineering

BPN Behler-Parrinello Network
.

Ring Polymer Molecular Dynamics and Active Learning of Moment Tensor Potential for Gas-Phase Barrierless Reactions: Application to S + H2

IS Ivan S. Novikov, Alexander V. Shapeev, Yury V. Suleimanov, AV Shapeev - The Journal of chemical …, undefined 2019
2019, 151 (22).
DOI: 10.1063/1.5127561

Automated Calculation of Thermal Rate Coefficients Using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning

Ivan S. Novikov, Yury V. Suleimanov, Alexander V. Shapeev
Physical Chemistry Chemical Physics, 2018, 20 (46), 29503–29512.
DOI: 10.1039/C8CP06037A

Modeling H 2 O/Rutile-TiO 2 (110) Potential Energy Surfaces with Deep Networks

Stefan Oehmcke, Thomas Teusch, Thorben Petersen, Thorsten Klüner, Oliver Kramer
2020 International Joint Conference on Neural Networks (IJCNN), 2020, 1–7.
DOI: 10.1109/IJCNN48605.2020.9207275

Deep Learning Interatomic Potential for Simulation of Radiation Damage in Vanadium-Rich V-Cr-Ti Ternary Alloys

H. S. M. Phuong, M. D. Starostenkov, N. T. H. Trung
Эволюция Дефектных Структур в Конденсированных Средах, 2020, 141–142.

Development of a General-Purpose Machine-Learning Interatomic Potential for Aluminum by the Physically Informed Neural Network Method

GPP P.Purja Pun, V. Yamakov, J. Hickman, E. H. Glaessgen, Y. Mishin, EH Glaessgen - Physical Review …, undefined 2020
2020, 4 (11).
DOI: 10.1103/physrevmaterials.4.113807

Four Generations of High-Dimensional Neural Network Potentials

J Behler - Chemical Reviews, undefined 2021
ACS Publications.

Representing Local Atomic Environment Using Descriptors Based on Local Correlations

Amit Samanta
The Journal of chemical physics, 2018, 149 (24), 244102.
DOI: 10.1063/1.5055772

Unsupervised Learning of Atomic Environments from Simple Features

WF Reinhart - Computational Materials Science, undefined 2021
Elsevier.

A Systematic Approach to Generating Accurate Neural Network Potentials: The Case of Carbon

Y Shaidu, E Küçükbenli, R Lot, F Pellegrini
nature.com.

Elinvar Effect in β-Ti Simulated by on-the-Fly Trained Moment Tensor Potential

AV Shapeev, EV Podryabinkin, K Gubaev
iopscience.iop.org.

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

Andreas Singraber, Jörg Behler, Christoph Dellago
Journal of chemical theory and computation, 2019, 15 (3), 1827–1840.
DOI: 10.1021/acs.jctc.8b00770

Machine-Learned Interatomic Potentials by Active Learning: Amorphous and Liquid Hafnium Dioxide

G Sivaraman, AN Krishnamoorthy, M Baur - npj Computational …, undefined 2020
nature.com.

Automated Discovery of a Robust Interatomic Potential for Aluminum

JS Smith, B Nebgen, N Mathew, J Chen
nature.com.

Efficient Estimation of Material Property Curves and Surfaces via Active Learning

Yuan Tian, Dezhen Xue, Ruihao Yuan, Yumei Zhou, Xiangdong Ding, Jun Sun, Turab Lookman, J Sun - Physical Review …, undefined 2021
2021, 5 (1).
DOI: 10.1103/physrevmaterials.5.013802

Generalizable Protein Interface Prediction with End-to-End Learning

R. J. Townshend, Rishi Bedi, Ron O. Dror
2018.

Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks

Masashi Tsubaki, Teruyasu Mizoguchi
The journal of physical chemistry letters, 2018, 9 (19), 5733–5741.
DOI: 10.1021/acs.jpclett.8b01837

Towards Modeling Spatiotemporal Processes in Metal–Organic Frameworks

Veronique Van Speybroeck, Sander Vandenhaute, Alexander EJ Hoffman, Sven MJ Rogge
Trends in Chemistry, 2021.
DOI: 10.1016/j.trechm.2021.04.003

Uncertainty Quantification in Molecular Simulations with Dropout Neural Network Potentials

M Wen, EB Tadmor
nature.com, 2020.

Deep Learning for UV Absorption Spectra with SchNarc: First Steps toward Transferability in Chemical Compound Space

Julia Westermayr, Philipp Marquetand
The Journal of Chemical Physics, 2020, 153 (15), 154112.
DOI: 10.1063/5.0021915

Machine Learning for Nonadiabatic Molecular Dynamics

Julia Westermayr, Philipp Marquetand
Machine Learning in Chemistry, 2020, 17, 76.
DOI: 10.1039/9781839160233-00076

A Data-Driven Construction of the Periodic Table of the Elements

Michael J. Willatt, Félix Musil, Michele Ceriotti
2018.

Theory and Practice of Atom-Density Representations for Machine Learning

Michael J. Willatt, Félix Musil, Michele Ceriotti
arXiv preprint, 2018.

Modeling and Predicting Responses of Magnetoelectric Materials

Ben Xu, Ce-Wen Nan
MRS Bulletin, 2018, 43 (11), 829–833.
DOI: 10.1557/mrs.2018.259

Theoretical Investigation of Halide Perovskites for Solar Cell and Optoelectronic Applications

Jingxiu Yang, Peng Zhang, Jianping Wang, Su Huai Wei
Chinese Physics B, 2020, 29 (10).
DOI: 10.1088/1674-1056/abb3f6

OnsagerNet: Learning Stable and Interpretable Dynamics Using a Generalized Onsager Principle

Haijun Yu, Xinyuan Tian, Q Li - arXiv preprint ArXiv:2009.02327, undefined 2020, Weinan E, Qianxiao Li
arxiv.org, 2020.

Exploration of Transferable and Uniformly Accurate Neural Network Interatomic Potentials Using Optimal Experimental Design

V Zaverkin, J Kästner
iopscience.iop.org, 2021.

Discovery and Design of Soft Polymeric Bio-Inspired Materials with Multiscale Simulations and Artificial Intelligence

Chenxi Zhai, Tianjiao Li, Haoyuan Shi, Jingjie Yeo
Journal of Materials Chemistry B, 2020, 8 (31), 6562–6587.
DOI: 10.1039/D0TB00896F

Inferring Micro-Bubble Dynamics with Physics-Informed Deep Learning

Hanfeng Zhai, Guohui Hu
2021.

Arrested Phase Separation in Double-Exchange Models: Machine-Learning Enabled Large-Scale Simulation

Puhan Zhang, Gia-Wei Chern
2021.

Physically Inspired Atom-Centered Symmetry Functions for the Construction of High Dimensional Neural Network Potential Energy Surfaces

Kangyu Zhang, Lichang Yin, Gang Liu
Computational Materials Science, 2021, 186, 110071.
DOI: 10.1016/j.commatsci.2020.110071

Adaptive Genetic Algorithm for Structure Prediction and Application to Magnetic Materials

Xin Zhao, Shunqing Wu, Manh Cuong Nguyen, Kai-Ming Ho, Cai-Zhuang Wang
Handbook of Materials Modeling: Applications: Current and Emerging Materials, 2020, 2757–2776.
DOI: 10.1007/978-3-319-44680-6_73

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