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Recently, machine learning potentials have been advanced as candidates to combine the high-accuracy of quantum mechanical simulations with the speed of classical interatomic potentials.
Computer simulation of local order in condensed phases of silicon
Frank H Stillinger and Thomas A Weber · 1907
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Double exponential formulas for numerical integration
Hidetosi Takahasi and Masatake Mori · 1974
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Neural network models of potential energy surfaces
Thomas B Blank, Steven D Brown, August W Calhoun, and Douglas J Doren · 1995
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3d zernike moments and zernike affine invariants for 3d image analysis and recognition
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3d Zernike descriptors for content based shape retrieval
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EM Arvacheh and HR Tizhoosh · 2005
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
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Rotational invariance based on fourier analysis in polar and spherical coordinates
Qing Wang, Olaf Ronneberger, and Hans Burkhardt · 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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Support vector machine regression (svr/ls-svm)—an alternative to neural networks (ann) for analytical chemistry? comparison of nonlinear methods on near infrared (nir) spectroscopy data
Roman M Balabin and Ekaterina I Lomakina · 2011
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High-dimensional neural network potentials for metal surfaces: A prototype study for copper
Nongnuch Artrith and Jörg Behler · 2012
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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 Von Lilienfeld · 2012
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The implicit function theorem: history, theory, and applications
Steven G Krantz and Harold R Parks · 2012
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On representing chemical environments
Albert P Bartók, Risi Kondor, and Gábor Csányi · 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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Perspective: Machine learning potentials for atomistic simulations
Prediction errors of molecular machine learning models lower than hybrid dft error
Felix A Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S Schoenholz, George E Dahl, Oriol Vinyals, Steven Kearnes, Patrick F Riley, and O Anatole Von Lilienfeld · 2017
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Accurate interatomic force fields via machine learning with covariant kernels
Aldo Glielmo, Peter Sollich, and Alessandro De Vita · 2017
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Unified representation of molecules and crystals for machine learning
Haoyan Huo and Matthias Rupp · 2017
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Machine learning based interatomic potential for amorphous carbon
Volker L Deringer and Gábor Csányi · 2017
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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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Jörg Behler · 2016
Cited alongside, same era.
Machine learning force fields: Construction, validation, and outlook
Venkatesh Botu, Rohit Batra, James Chapman, and Rampi Ramprasad · 2016
Cited alongside, same era.
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for tio2
Nongnuch Artrith and Alexander Urban · 2016
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Amp: A modular approach to machine learning in atomistic simulations
Alireza Khorshidi and Andrew A Peterson · 2016
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Moment tensor potentials: A class of systematically improvable interatomic potentials
Alexander V Shapeev · 2016
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Development of a machine learning potential for graphene
Patrick Rowe, Gábor Csányi, Dario Alfè, and Angelos Michaelides · 2018
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Constructing high-dimensional neural network potential energy surfaces for gas–surface scattering and reactions
Qinghua Liu, Xueyao Zhou, Linsen Zhou, Yaolong Zhang, Xuan Luo, Hua Guo, and Bin Jiang · 2018
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Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials
Giulio Imbalzano, Andrea Anelli, Daniele Giofré, Sinja Klees, Jörg Behler, and Michele Ceriotti · 2018
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A novel approach to describe chemical environments in high-dimensional neural network potentials
Emir Kocer, Jeremy K Mason, and Hakan Erturk · 2019
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Miguel A Caro · 2019
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