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G aussian approximation potentials: A brief tutorial introduction
Author(s) -
Bartók Albert P.,
Csányi Gábor
Publication year - 2015
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.24927
Subject(s) - variety (cybernetics) , computer science , gaussian , software , statistical physics , sandbox (software development) , work (physics) , computational science , theoretical computer science , physics , artificial intelligence , quantum mechanics , software engineering , programming language
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.