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Reviews

Discovery and Implementation of Fast, Accurate and Transferable Many-Body Interatomic Potentials

Adarsh Balasubramanian
2019.

Four Generations of High-Dimensional Neural Network Potentials

Jörg Behler
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c00868

Dynamical Processes in the Condensed Phase: Methods and Models

Matthew Ralph Carbone
2021.

Machine Learning and the Physical Sciences

Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborova
Reviews of Modern Physics, 2019, 91 (4), 045002.
DOI: 10.1103/RevModPhys.91.045002

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

Designing Models Using Machine Learning: One-Body Reduced Density Matrices and Spectra

Andrea Costamagna
2020.

Interfacial Potentials in Ion Solvation

Carrie Conor Doyle
2020.

Molecular Excited States through a Machine Learning Lens

Pavlo O. Dral, Mario Barbatti
Nature Reviews Chemistry, 2021, 5 (6), 388–405.
DOI: 10.1038/s41570-021-00278-1

Characterizing Magnetic Skyrmions at Their Fundamental Length and Time Scales

Peter Fischer, Sujoy Roy
Magnetic Skyrmions and Their Applications, 2021, 55–97.

Unsupervised Learning Methods for Molecular Simulation Data

Aldo Glielmo, Brooke E. Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé, Alessandro Laio
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01195

The Structure and Dynamics of Materials Using Machine Learning

Mário Rui Gonçalves Marques
2020.

Machine-Learning-Assisted Modeling

Sarah Greenstreet
Physics Today, 2021, 74 (7), 42–47.
DOI: 10.1063/PT.3.4794

Atomic-Scale Representation and Statistical Learning of Tensorial Properties

Andrea Grisafi, David M. Wilkins, Michael J. Willatt, Michele Ceriotti
Machine Learning in Chemistry: Data-Driven Algorithms, Learning Systems, and Predictions, 2019, 1–21.

Adaptive Iron-Based Magnetic Nanomaterials of High Performance for Biomedical Applications

Ning Gu, Zuoheng Zhang, Yan Li
Nano Research, 2021.
DOI: 10.1007/s12274-021-3546-1

Deep Learning for Large-Scale Molecular Dynamics and High-Dimensional Partial Differential Equations

Jiequn Han
2018.

Machine Learning for Alloys

Gus L. W. Hart, Tim Mueller, Cormac Toher, Stefano Curtarolo
Nature Reviews Materials, 2021.
DOI: 10.1038/s41578-021-00340-w

Characterizing Performance Improvement of GPUs

Dodi Heryadi, Scott Hampton
Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (Learning), 2019, 1–5.

Physics-Informed Machine Learning

George Em Karniadakis, Ioannis G. Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, Liu Yang
Nature Reviews Physics, 2021, 3 (6), 422–440.
DOI: 10.1038/s42254-021-00314-5

Neural Network Potentials: A Concise Overview of Methods

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

First-Principles Study on the Structural and Thermal Properties of Molecular Crystals and Liquids

Hsin-Yu Ko
2019.

FMO Interfaced with Molecular Dynamics Simulation

Yuto Komeiji, Takeshi Ishikawa
Recent Advances of the Fragment Molecular Orbital Method, 2021, 373–389.

Classical Molecular Dynamics Using Neural Network Representations of Potential Energy Surfaces

Andreas Godø Lefdalsnes
2019.

Modeling Electrified Metal/Water Interfaces from Ab Initio Molecular Dynamics: Structure and Helmholtz Capacitance

Jia-Bo Le, Jun Cheng
Current Opinion in Electrochemistry, 2021, 27, 100693.
DOI: 10/ghtqnk

Molecular Dynamics Study of Charged Nanomaterials: Electrostatics and Self-Assembly

Yaohua Li
2021.

Discovering and Understanding Materials through Computation

Steven G. Louie, Yang-Hao Chan, Felipe H. da Jornada, Zhenglu Li, Diana Y. Qiu
Nature Materials, 2021, 20 (6), 728–735.
DOI: 10.1038/s41563-021-01015-1

Future Directions of Chemical Theory and Computation

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

Integrating Machine Learning into Protein-Ligand Scoring Function Development

Jianing Lu
2020.

Development of a Machine Learning Potential for Nucleotides in Water

Riccardo Martina
, 57.

Machine Learning for Chemical Reactions

Markus Meuwly
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00033

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

Membrane Models for Molecular Simulations of Peripheral Membrane Proteins

Mahmoud Moqadam, Thibault Tubiana, Emmanuel E. Moutoussamy, Nathalie Reuter
Advances in Physics-X, 2021, 6 (1), 1932589.
DOI: 10.1080/23746149.2021.1932589

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

Physics-Inspired Structural Representations for Molecules and Materials

Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.1c00021

Computational Predictions of the Thermal Conductivity of Solids and Liquids

Marcello Puligheddu
2020.

RANDOM PHASE APPROXIMATION AND BEYOND: FROM THEORY TO REALISTIC MATERIALS

Dario Rocca
2020.

Theoretical Insights into the Surface Physics and Chemistry of Redox-Active Oxides

Roger Rousseau, Vassiliki-Alexandra Glezakou, Annabella Selloni
Nature Reviews Materials, 2020, 5 (6), 460–475.
DOI: 10.1038/s41578-020-0198-9

Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights

Huziel E. Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, Alexandre Tkatchenko
Machine Learning Meets Quantum Physics, 2020, 277–307.

Interatomic Potential for Li-C Systems from Cluster Expansion to Artificial Neural Network Techniques

Yusuf Shaidu
2020.

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

Neural Network for the Prediction of Force Differences between an Amino Acid in Solution and Vacuum

Gopal Narayan Srivastava
2020.

Machine Learning Force Fields

Oliver T. Unke, Stefan Chmiela, Huziel E. Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Chemical Reviews, 2021.
DOI: 10.1021/acs.chemrev.0c01111

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

Force Field Development and Nanoreactor Chemistry

Lee-Ping Wang
Computational Approaches for Chemistry Under Extreme Conditions, 2019, 127–159.

Investigations of Water/Oxide Interfaces by Molecular Dynamics Simulations

Ruiyu Wang, Michael L. Klein, Vincenzo Carnevale, Eric Borguet
Wiley Interdisciplinary Reviews-Computational Molecular Science, 2021, e1537.
DOI: 10.1002/wcms.1537

Physics-Guided Deep Learning for Dynamical Systems: A Survey

Rui Wang
2021.

Integrating Machine Learning with Physics-Based Modeling

E Weinan, Jiequn Han, Zhang Linfeng
2020.

Machine Learning and Computational Mathematics

E. Weinan
Communications in Computational Physics, 2020, 28 (5), 1639–1670.
DOI: 10.4208/cicp.OA-2020-0185

Machine Learning for Electronically Excited States of Molecules

Julia Westermayr, Philipp Marquetand
Chemical Reviews, 2020.
DOI: 10.1021/acs.chemrev.0c00749

Integrating Physics-Based Modeling with Machine Learning: A Survey

J Willard, X Jia, S Xu, M Steinbach, V Kumar
arxiv.org, 2021.

Deep Learning Methods for the Design and Understanding of Solid Materials

Tian Xie
2020.

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

Recent Progress on Multiscale Modeling of Electrochemistry

Xiao‐Hui Yang, Yong‐Bin Zhuang, Jia‐Xin Zhu, Jia‐Bo Le, Jun Cheng
WIREs Computational Molecular Science, 2021.
DOI: 10.1002/wcms.1559

Machine Learning of Coarse-Grained Models for Organic Molecules and Polymers: Progress, Opportunities, and Challenges

Huilin Ye, Weikang Xian, Ying Li
ACS omega, 2021, 6 (3), 1758–1772.
DOI: 10.1021/acsomega.0c05321

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

L Zhang
2020.

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

Non-Contact Ultrasound

Xiang Zhang
2019.