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Gaussian approximation potentials: A brief tutorial introduction

International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1051-1057 [Peer Reviewed Journal]

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Title:
Gaussian approximation potentials: A brief tutorial introduction
Author: Bartok, Albert P ; Csanyi, Gábor
Subjects: ab initio ; atomic environments ; Gaussian process ; interatomic potentials ; machine learning
Is Part Of: International journal of quantum chemistry, 2015-08, Vol.115 (16), p.1051-1057
Description: We present a swift walk‐through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian approximation potentials (GAP) framework, discuss a variety of descriptors, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for noncommercial use. © 2015 Wiley Periodicals, Inc. Interatomic potentials based on first principles data can be generated using machine learning methods. The Gaussian Approximation Potential framework puts this concept into practice, and its software implementation, QUIP, has been made available. QUIP might be used as a standalone tool or it can be easily interfaced with mainstream molecular simulation packages.
Publisher: Hoboken: Blackwell Publishing Ltd
Language: English;French;German
Identifier: ISSN: 0020-7608
EISSN: 1097-461X
DOI: 10.1002/qua.24927
CODEN: IJQCB2
Source: Alma/SFX Local Collection

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