Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accurac...

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Autores principales:	Yu Xie, Jonathan Vandermause, Lixin Sun, Andrea Cepellotti, Boris Kozinsky
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/f04a0cef3d874031836d2287821283f8
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spelling	oai:doaj.org-article:f04a0cef3d874031836d2287821283f82021-12-02T13:18:00ZBayesian force fields from active learning for simulation of inter-dimensional transformation of stanene10.1038/s41524-021-00510-y2057-3960https://doaj.org/article/f04a0cef3d874031836d2287821283f82021-03-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00510-yhttps://doaj.org/toc/2057-3960Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.Yu XieJonathan VandermauseLixin SunAndrea CepellottiBoris KozinskyNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765
spellingShingle	Materials of engineering and construction. Mechanics of materials TA401-492 Computer software QA76.75-76.765 Yu Xie Jonathan Vandermause Lixin Sun Andrea Cepellotti Boris Kozinsky Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
description	Abstract We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows for automated active learning of models combining near-quantum accuracy, built-in uncertainty, and constant cost of evaluation that is comparable to classical analytical models, capable of simulating millions of atoms. Using this approach, we perform large-scale molecular dynamics simulations of the stability of the stanene monolayer. We discover an unusual phase transformation mechanism of 2D stanene, where ripples lead to nucleation of bilayer defects, densification into a disordered multilayer structure, followed by formation of bulk liquid at high temperature or nucleation and growth of the 3D bcc crystal at low temperature. The presented method opens possibilities for rapid development of fast accurate uncertainty-aware models for simulating long-time large-scale dynamics of complex materials.
format	article
author	Yu Xie Jonathan Vandermause Lixin Sun Andrea Cepellotti Boris Kozinsky
author_facet	Yu Xie Jonathan Vandermause Lixin Sun Andrea Cepellotti Boris Kozinsky
author_sort	Yu Xie
title	Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
title_short	Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
title_full	Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
title_fullStr	Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
title_full_unstemmed	Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
title_sort	bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f04a0cef3d874031836d2287821283f8
work_keys_str_mv	AT yuxie bayesianforcefieldsfromactivelearningforsimulationofinterdimensionaltransformationofstanene AT jonathanvandermause bayesianforcefieldsfromactivelearningforsimulationofinterdimensionaltransformationofstanene AT lixinsun bayesianforcefieldsfromactivelearningforsimulationofinterdimensionaltransformationofstanene AT andreacepellotti bayesianforcefieldsfromactivelearningforsimulationofinterdimensionaltransformationofstanene AT boriskozinsky bayesianforcefieldsfromactivelearningforsimulationofinterdimensionaltransformationofstanene
_version_	1718393364754202624

Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene

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