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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.
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