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An active learning procedure called Deep Potential Generator (DP-GEN) is proposed for the construction of accurate and transferable machine learning-based models of the potential energy surface (PES) for the molecular modeling of materials.
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Lattice constants and brillouin zone overlap in dilute magnesium alloys
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Elastic constants of magnesium from 4.2 k to 300 k
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A study of the structure of lomer and 60° dislocations in aluminium using high-resolution transmission electron microscopy
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Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
Georg Kresse and Jürgen Furthmüller · 1996
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Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set
Georg Kresse and Jürgen Furthmüller · 1996
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All-atom empirical potential for molecular modeling and dynamics studies of proteins
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How well does a restrained electrostatic potential (resp) model perform in calculating conformational energies of organic and biological molecules?
Junmei Wang, Piotr Cieplak, and Peter A Kollman · 2000
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Reaxff: a reactive force field for hydrocarbons
Adri CT Van Duin, Siddharth Dasgupta, Francois Lorant, and William A Goddard · 2001
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Impurity effect of mg on the generalized planar fault energy of al
Dongdong Zhao, Ole Martin Løvvik, Knut Marthinsen, and Yanjun Li · 2016
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Surface energies of elemental crystals
Richard Tran, Zihan Xu, Balachandran Radhakrishnan, Donald Winston, Wenhao Sun, Kristin A Persson, and Shyue Ping Ong · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Machine learning of accurate energy-conserving molecular force fields
Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Melting of aluminum, molybdenum, and the light actinides
Marvin Ross, Lin H Yang, and Reinhard Boehler · 2004
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The inorganic crystal structure database (icsd)—present and future
Mariette Hellenbrandt · 2004
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Introduction to solid state physics
Charles Kittel · 2004
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Melting curve of aluminum up to 300 gpa obtained through ab initio molecular dynamics simulations
J Bouchet, F Bottin, G Jomard, and G Zérah · 2009
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Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
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Fast and accurate modeling of molecular atomization energies with machine learning
Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller, and O Anatole VonLilienfeld · 2012
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Albert P Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R Kermode, Gábor Csányi, and Michele Ceriotti · 2017
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ANI-1: an extensible neural network potential with dft accuracy at force field computational cost
Justin S Smith, Olexandr Isayev, and Adrian E Roitberg · 2017
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Active learning of linearly parametrized interatomic potentials
Evgeny V Podryabinkin and Alexander V Shapeev · 2017
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ANI-1, a data set of 20 million calculated off-equilibrium conformations for organic molecules
Justin S Smith, Olexandr Isayev, and Adrian E Roitberg · 2017
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Stratified construction of neural network based interatomic models for multicomponent materials
Samad Hajinazar, Junping Shao, and Aleksey N Kolmogorov · 2017
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First-principles investigation of strain effects on the stacking fault energies, dislocation core structure, and peierls stress of magnesium and its alloys
SH Zhang, IJ Beyerlein, Dominik Legut, ZH Fu, Z Zhang, SL Shang, ZK Liu, TC Germann, and RF Zhang · 2017
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Deep Potential: a general representation of a many-body potential energy surface
Jequn Han, Linfeng Zhang, Roberto Car, and Weinan E · 2018
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Atomic energies from a convolutional neural network
Xin Chen, Mathias Siggaard Jørgensen, Jun Li, and Bjørk Hammer · 2018
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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
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End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems
Linfeng Zhang, Jiequn Han, Han Wang, Wissam Saidi, Roberto Car, and Weinan E · 2018
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Less is more: Sampling chemical space with active learning
Justin S Smith, Ben Nebgen, Nicholas Lubbers, Olexandr Isayev, and Adrian E Roitberg · 2018
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Metadynamics for training neural network model chemistries: A competitive assessment
John E Herr, Kun Yao, Ryker McIntyre, David W Toth, and John Parkhill · 2018
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Silicon liquid structure and crystal nucleation from ab-initio deep metadynamics
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Machine learning a general purpose interatomic potential for silicon
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Reinforced dynamics for enhanced sampling in large atomic and molecular systems
Linfeng Zhang, Han Wang, and Weinan E · 2018
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DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E · 2018
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Deepcg: Constructing coarse-grained models via deep neural networks
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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Fast and accurate uncertainty estimation in chemical machine learning
Felix Musil, Michael Willatt, Mikhail A Langovoy, and Michele Ceriotti · 2019
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