Gaussian Approximation Potentials: a brief tutorial introduction

Gaussian Approximation Potentials: a brief tutorial introduction. Bartók, A. P. & Csányi, G. arXiv:1502.01366 [cond-mat, physics:physics], February, 2015. arXiv: 1502.01366

Paper abstract bibtex

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, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.

@article{bartok_gaussian_2015,
	title = {Gaussian {Approximation} {Potentials}: a brief tutorial introduction},
	shorttitle = {Gaussian {Approximation} {Potentials}},
	url = {http://arxiv.org/abs/1502.01366},
	abstract = {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, discussing a variety of descriptors, how to train the model on total energies and derivatives and the simultaneous use of multiple models. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for non-commercial use.},
	urldate = {2018-12-10TZ},
	journal = {arXiv:1502.01366 [cond-mat, physics:physics]},
	author = {Bartók, Albert P. and Csányi, Gábor},
	month = feb,
	year = {2015},
	note = {arXiv: 1502.01366},
	keywords = {Condensed Matter - Materials Science, Physics - Chemical Physics}
}

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