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Gaussian Approximation Potentials: a brief tutorial introduction


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Publication Date: 2015-04-27

Journal Title: International Journal of Quantum Chemistry

Publisher: Wiley

Volume: 115

Issue: 16

Pages: 1051

Language: English

Type: Article

Metadata: Show full item record

Citation: Bartók, A. P., & Csányi, G. (2015). Gaussian Approximation Potentials: a brief tutorial introduction. International Journal of Quantum Chemistry, 115 (16), 1051.

Description: This is the author accepted manuscript. The final version is available via Wiley at http://onlinelibrary.wiley.com/doi/10.1002/qua.24927/abstract.

Abstract: We present a swift walk-through of our recent work that uses machine learning to t 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 di erent complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use. 1

Keywords: interatomic potentials, machine learning, Gaussian process, ab initio, atomic environments

Sponsorship: A.P.B. is supported by a Leverhulme Early Career Fellowship and the Isaac Newton Trust. We would like to thank our referees for their comments during the revision process.

Identifiers:

This record's URL: http://dx.doi.org/10.1002/qua.24927http://www.repository.cam.ac.uk/handle/1810/248022





Autor: Bartók, Albert P.Csányi, Gábor

Fuente: https://www.repository.cam.ac.uk/handle/1810/248022



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