Fetching the paper…
Reading the bibliography…
Molecular dynamics simulations are an invaluable tool in numerous scientific fields.
Angular momentum in quantum mechanics
A. R Edmonds · 1957
Earlier work this paper cites.
The opls [optimized potentials for liquid simulations] potential functions for proteins, energy minimizations for crystals of cyclic peptides and crambin
William L Jorgensen and Julian Tirado-Rives · 1988
Earlier work this paper cites.
Angular momentum: An illustrated guide to rotational symmetries for physical systems
William J Thompson and LeRoy F Cook · 1995
Earlier work this paper cites.
Improved adsorption energetics within density-functional theory using revised perdew-burke-ernzerhof functionals
B. Hammer, L. B. Hansen, and J. K. Nørskov · 1999
Earlier work this paper cites.
Reaxff: A reactive force field for hydrocarbons
Adri C. T van Duin, Siddharth Dasgupta, Francois Lorant, and William A Goddard · 2001
Earlier work this paper cites.
Molecular dynamics simulations of biomolecules
J. Andrew McCammon and Martin Karplus · 2002
Earlier work this paper cites.
Maxwell–cartesian spherical harmonics in multipole potentials and atomic orbitals
Jon Applequist · 2002
Earlier work this paper cites.
Ab initio molecular dynamics: Concepts, recent developments, and future trends
Radu Iftimie, Peter Minary, Mark E. Tuckerman, and Bruce J. Berne · 2005
Earlier work this paper cites.
The amber biomolecular simulation programs
David A Case, Thomas E Cheatham, Tom Darden, Holger Gohlke, Ray Luo, Kenneth M Merz, Alexey Onufriev, Carlos Simmerling, Bing Wang, and Robert J Woods · 2005
Earlier work this paper cites.
Chapter 7 empirical force fields for proteins: Current status and future directions
Alexander D MacKerell · 2005
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
Critical assessment of the performance of density functional methods for several atomic and molecular properties
Kevin E Riley, Bryan T Op’t Holt, and Kenneth M Merz · 2007
Earlier work this paper cites.
Ab Initio Molecular Dynamics: Basic Theory and Advanced Methods
Dominik Marx and Jürg Hutter · 2009
Earlier work this paper cites.
Charmm: The biomolecular simulation program
B. R Brooks, C. L Brooks, A. D Mackerell, L Nilsson, R. J Petrella, B Roux, Y Won, G Archontis, C Bartels, S Boresch, A Caflisch, L Caves, Q Cui, A. R Dinner, M Feig, S Fischer, J Gao, M Hodoscek, W Im, K Kuczera, T Lazaridis, J Ma, V Ovchinnikov, E Paci, R. W Pastor, C. B Post, J. Z Pu, M Schaefer, B Tidor, R. M Venable, H. L Woodcock, X Wu, W Yang, D. M York, and M Karplus · 2009
Earlier work this paper cites.
Potential energy surfaces fitted by artificial neural networks
Chris M Handley and Paul L. A Popelier · 2010
Earlier work this paper cites.
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Nongnuch Artrith, Tobias Morawietz, and Jörg Behler · 2011
Earlier work this paper cites.
Perspective on density functional theory
Kieron Burke · 2012
Earlier work this paper cites.
Random-phase approximation and its applications in computational chemistry and materials science
Xinguo Ren, Patrick Rinke, Christian Joas, and Matthias Scheffler · 2012
Earlier work this paper cites.
Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole von Lilienfeld · 2012
Earlier work this paper cites.
High-dimensional neural network potentials for metal surfaces: A prototype study for copper
Nongnuch Artrith and Jörg Behler · 2012
Earlier work this paper cites.
Optimized norm-conserving vanderbilt pseudopotentials
D. R. Hamann · 2013
Earlier work this paper cites.
Neural network potentials for metals and oxides – first applications to copper clusters at zinc oxide
Nongnuch Artrith, Björn Hiller, and Jörg Behler · 2013
Earlier work this paper cites.
Perspective: Fifty years of density-functional theory in chemical physics
Axel D. Becke · 2014
Cited alongside, same era.
Representing potential energy surfaces by high-dimensional neural network potentials
J Behler · 2014
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2014
Cited alongside, same era.
Molecular dynamics simulations: advances and applications
Adam Hospital, Josep Ramon Goñi, Modesto Orozco, and Josep L Gelpí · 2015
Cited alongside, same era.
Ab initio molecular dynamics simulations of methylammonium lead iodide perovskite degradation by water
Edoardo Mosconi, Jon M Azpiroz, and Filippo De Angelis · 2015
Cited alongside, same era.
Adaptive machine learning framework to accelerate ab initio molecular dynamics
Hierarchical modeling of molecular energies using a deep neural network
Nicholas Lubbers, Justin S Smith, and Kipton Barros · 2018
Later among the works it cites.
Alchemical and structural distribution based representation for universal quantum machine learning
Felix A Faber, Anders S Christensen, Bing Huang, and O. Anatole von Lilienfeld · 2018
Later among the works it cites.
Deep potential molecular dynamics: A scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
Later among the works it cites.
Towards exact molecular dynamics simulations with machine-learned force fields
Stefan Chmiela, Huziel E. Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko · 2018
Later among the works it cites.
Structure prediction drives materials discovery
Artem R Oganov, Chris J Pickard, Qiang Zhu, and Richard J Needs · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Venkatesh Botu and Rampi Ramprasad · 2015
Cited alongside, same era.
Role of molecular dynamics and related methods in drug discovery
Marco De Vivo, Matteo Masetti, Giovanni Bottegoni, and Andrea Cavalli · 2016
Cited alongside, same era.
The reaxff reactive force-field: development, applications and future directions
Thomas P Senftle, Sungwook Hong, Md Mahbubul Islam, Sudhir B Kylasa, Yuanxia Zheng, Yun Kyung Shin, Chad Junkermeier, Roman Engel-Herbert, Michael J Janik, Hasan Metin Aktulga, Toon Verstraelen, Ananth Grama, and Adri C T van Duin · 2016
Cited alongside, same era.
Perspective: Machine learning potentials for atomistic simulations
Jörg Behler · 2016
Cited alongside, same era.
Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi and Andrew A. Peterson · 2016
Cited alongside, same era.
Thirty years of density functional theory in computational chemistry: an overview and extensive assessment of 200 density functionals
Narbe Mardirossian and Martin Head-Gordon · 2017
Cited alongside, same era.
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad, Rohit Batra, Ghanshyam Pilania, Arun Mannodi-Kanakkithodi, and Chiho Kim · 2017
Cited alongside, same era.
Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Oliver T Unke and Markus Meuwly · 2019
Later among the works it cites.
Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials
Miguel A. Caro · 2019
Later among the works it cites.
Design and analysis of machine learning exchange-correlation functionals via rotationally invariant convolutional descriptors
Xiangyun Lei and Andrew J. Medford · 2019
Later among the works it cites.
Simple-nn: An efficient package for training and executing neural-network interatomic potentials
Kyuhyun Lee, Dongsun Yoo, Wonseok Jeong, and Seungwu Han · 2019
Later among the works it cites.
Exploring chemical compound space with quantum-based machine learning
O. Anatole von Lilienfeld, Klaus-Robert Müller, and Alexandre Tkatchenko · 2019
Later among the works it cites.
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
Later among the works it cites.
Molecular mechanics-driven graph neural network with multiplex graph for molecular structures, 2020
Shuo Zhang, Yang Liu, and Lei Xie · 2020
Later among the works it cites.
Heterogeneous molecular graph neural networks for predicting molecule properties
Z. Shui and G. Karypis · 2020
Later among the works it cites.
Singlenn: Modified behler–parrinello neural network with shared weights for atomistic simulations with transferability
Mingjie Liu and John R. Kitchin · 2020
Later among the works it cites.
Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning
Marcel F. Langer, Alex Goeßmann, and Matthias Rupp · 2020
Later among the works it cites.
Gaussian moments as physically inspired molecular descriptors for accurate and scalable machine learning potentials
V. Zaverkin and J. Kästner · 2020
Later among the works it cites.
https://github.com/ulissigroup/amptorch , 2020
Amptorch · 2020
Later among the works it cites.
Enabling robust offline active learning for machine learning potentials using simple physics-based priors
Muhammed Shuaibi, Saurabh Sivakumar, Rui Qi Chen, and Zachary W Ulissi · 2020
Later among the works it cites.
Automated fitting of neural network potentials at coupled cluster accuracy: Protonated water clusters as testing ground
Christoph Schran, Jörg Behler, and Dominik Marx · 2020
Later among the works it cites.
Physics-inspired structural representations for molecules and materials
Felix Musil, Andrea Grisafi, Albert P. Bartók, Christoph Ortner, Gábor Csányi, and Michele Ceriotti · 2021
Closest in time.
Open catalyst 2020 (OC20) dataset and community challenges
Lowik Chanussot, Abhishek Das, Siddharth Goyal, Thibaut Lavril, Muhammed Shuaibi, Morgane Riviere, Kevin Tran, Javier Heras-Domingo, Caleb Ho, Weihua Hu, Aini Palizhati, Anuroop Sriram, Brandon Wood, Junwoong Yoon, Devi Parikh, C. Lawrence Zitnick, and Zachary Ulissi · 2021
Closest in time.