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Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation.
Self-consistent equations including exchange and correlation effects
Walter Kohn and Lu Jeu Sham · 1965
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Unified approach for molecular dynamics and density-functional theory
Roberto Car and Michele Parrinello · 1985
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Generalized gradient approximation made simple
John P Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Molecular dynamics and the accuracy of numerically computed averages
S.D. Bond and B.J. Leimkuhler · 2007
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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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First principles study of the li10gep2s12 lithium super ionic conductor material
Yifei Mo, Shyue Ping Ong, and Gerbrand Ceder · 2012
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Commentary: The materials project: A materials genome approach to accelerating materials innovation
Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, et al · 2013
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Single-crystal x-ray structure analysis of the superionic conductor li 10 gep 2 s 12
Alexander Kuhn, Jürgen Köhler, and Bettina V Lotsch · 2013
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Accuracy and transferability of gaussian approximation potential models for tungsten
Wojciech J. Szlachta, Albert P. Bartók, and Gábor Csányi · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Aidan P Thompson, Laura P Swiler, Christian R Trott, Stephen M Foiles, and Garritt J Tucker · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 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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Active learning of linearly parametrized interatomic potentials
Evgeny V Podryabinkin and Alexander V Shapeev · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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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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Ionic correlations and failure of nernst-einstein relation in solid-state electrolytes
Aris Marcolongo and Nicola Marzari · 2017
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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 EJPRL Weinan · 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, et al · 2018
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Machine learning a general-purpose interatomic potential for silicon
Albert P. Bartók, James Kermode, Noam Bernstein, and Gábor Csányi · 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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
A generalizable machine learning potential of Ag–Au nanoalloys and its application to surface reconstruction, segregation and diffusion
YiNan Wang, LinFeng Zhang, Ben Xu, XiaoYang Wang, and Han Wang · 2021
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Specialising neural network potentials for accurate properties and application to the mechanical response of titanium
Tongqi Wen, Rui Wang, Linyu Zhu, Linfeng Zhang, Han Wang, David J. Srolovitz, and Zhaoxuan Wu · 2021
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3d infomax improves gnns for molecular property prediction
Hannes Stärk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan Günnemann, and Pietro Liò · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra, 2021
Kristof T. Schütt, Oliver T. Unke, and Michael Gastegger · 2021
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Rotation invariant graph neural networks using spin convolutions, 2021
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Atomic cluster expansion for accurate and transferable interatomic potentials
Ralf Drautz · 2019
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Directional message passing for molecular graphs
Johannes Gasteiger, Janek Groß, and Stephan Günnemann · 2019
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Embedded atom neural network potentials: Efficient and accurate machine learning with a physically inspired representation
Yaolong Zhang, Ce Hu, and Bin Jiang · 2019
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Active learning of uniformly accurate interatomic potentials for materials simulation
Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, and E Weinan · 2019
Cited alongside, same era.
Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning
Justin S Smith, Benjamin T Nebgen, Roman Zubatyuk, Nicholas Lubbers, Christian Devereux, Kipton Barros, Sergei Tretiak, Olexandr Isayev, and Adrian E Roitberg · 2019
Cited alongside, same era.
A general-purpose machine-learning force field for bulk and nanostructured phosphorus
Volker L Deringer, Miguel A Caro, and Gábor Csányi · 2020
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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, and E Weinan · 2020
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Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C. Lawrence Zitnick · 2021
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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, et al · 2021
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Deep potential generation scheme and simulation protocol for the li10gep2s12-type superionic conductors
Jianxing Huang, Linfeng Zhang, Han Wang, Jinbao Zhao, Jun Cheng, and Weinan E · 2021
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Deep potentials for materials science
Tongqi Wen, Linfeng Zhang, Han Wang, Weinan E, and David J Srolovitz · 2022
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A tungsten deep neural-network potential for simulating mechanical property degradation under fusion service environment
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Pre-training molecular graph representation with 3d geometry
Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang · 2022
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Uni-mol: A universal 3d molecular representation learning framework
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Fast and uncertainty-aware directional message passing for non-equilibrium molecules, 2022
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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements
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Spherical channels for modeling atomic interactions, 2022
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A universal graph deep learning interatomic potential for the periodic table
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Learning local equivariant representations for large-scale atomistic dynamics
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Equivariant graph attention networks for molecular property prediction, 2022
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Attention mechanisms in computer vision: A survey
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Equiformer: Equivariant graph attention transformer for 3d atomistic graphs, 2023
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