Gaussian approximation potentials: A brief tutorial introduction

被引：472

作者：

Bartok, Albert P. ^{[1
]}

Csanyi, Gabor ^{[1
]}

机构：

[1] Univ Cambridge, Engn Lab, Cambridge CB2 1PZ, England

来源：

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY | 2015年 / 115卷 / 16期

基金：

英国工程与自然科学研究理事会;

关键词：

interatomic potentials; machine learning; Gaussian process; ab initio; atomic environments; ENERGY SURFACES;

D O I：

10.1002/qua.24927

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

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. (c) 2015 Wiley Periodicals, Inc.

引用

页码：1051 / 1057

页数：7

共 17 条

[1] [Anonymous], 2001, J. Am. Stat. Assoc.
[2] On representing chemical environments
Bartok, Albert P.
Kondor, Risi
Csanyi, Gabor
[J]. PHYSICAL REVIEW B, 2013, 87 (18)
[3] Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, Joerg
Parrinello, Michele
[J]. PHYSICAL REVIEW LETTERS, 2007, 98 (14)
[4] Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, Joerg
[J]. PHYSICAL CHEMISTRY CHEMICAL PHYSICS, 2011, 13 (40) : 17930 - 17955
[5] Classical and quasiclassical spectral analysis of CH5+ using an ab initio potential energy surface
Brown, A
Braams, BJ
Christoffel, K
Jin, Z
Bowman, JM
[J]. JOURNAL OF CHEMICAL PHYSICS, 2003, 119 (17) : 8790 - 8793
[6] First-principles energetics of water clusters and ice: A many-body analysis
Gillan, M. J.
Alfe, D.
Bartok, A. P.
Csanyi, G.
[J]. JOURNAL OF CHEMICAL PHYSICS, 2013, 139 (24)
[7] Potential Energy Surfaces Fitted by Artificial Neural Networks
Handley, Chris M.
Popelier, Paul L. A.
[J]. JOURNAL OF PHYSICAL CHEMISTRY A, 2010, 114 (10) : 3371 - 3383
[8] Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, Katja
Montavon, Gregoire
Biegler, Franziska
Fazli, Siamac
Rupp, Matthias
Scheffler, Matthias
von Lilienfeld, O. Anatole
Tkatchenko, Alexandre
Mueller, Klaus-Robert
[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2013, 9 (08) : 3404 - 3419
[9] Mackay D. J. C., 2003, Information Theory, Inference, and Learning Algorithms
[10] Development of a "First-Principles" Water Potential with Flexible Monomers. III. Liquid Phase Properties
Medders, Gregory R.
Babin, Volodymyr
Paesani, Francesco
[J]. JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2014, 10 (08) : 2906 - 2910

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