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Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling.
Back propagation neural networks
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Robert A. DiStasio Jr., Biswajit Santra, Zhaofeng Li, Xifan Wu, and Roberto Car · 2014
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Song Han, Huizi Mao, and William J Dally · 2015
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Machine learning of accurate energy-conserving molecular force fields
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Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 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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Quantized neural networks: Training neural networks with low precision weights and activations
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio · 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 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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Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E · 2018
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Tejalal Choudhary, Vipul Mishra, Anurag Goswami, and Jagannathan Sarangapani · 2020
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Efficient synthesis of compact deep neural networks
Wenhan Xia, Hongxu Yin, and Niraj K Jha · 2020
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Train big, then compress: Rethinking model size for efficient training and inference of transformers
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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 Weinan E · 2020
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Deep neural network for the dielectric response of insulators
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Linfeng Zhang, De-Ye Lin, Han Wang, Roberto Car, and Weinan E · 2019
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Isotope effects in liquid water via deep potential molecular dynamics
Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A DiStasio Jr, and Roberto Car · 2019
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Efficient and accurate simulations of vibrational and electronic spectra with symmetry-preserving neural network models for tensorial properties
Yaolong Zhang, Sheng Ye, Jinxiao Zhang, Ce Hu, Jun Jiang, and Bin Jiang · 2020
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Signatures of a liquid–liquid transition in an ab initio deep neural network model for water
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Dynamic observation of dendritic quasicrystal growth upon laser-induced solid-state transformation
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Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
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Raman spectrum and polarizability of liquid water from deep neural networks
Grace M. Sommers, Marcos F. Calegari Andrade, Linfeng Zhang, Han Wang, and Roberto Car · 2020
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Accelerating atomistic simulations with piecewise machine-learned ab initio potentials at a classical force field-like cost
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86 pflops deep potential molecular dynamics simulation of 100 million atoms with ab initio accuracy
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Reactive uptake of n2o5 by atmospheric aerosol is dominated by interfacial processes
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Electronically driven 1d cooperative diffusion in a simple cubic crystal
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Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
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