Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

Abstract Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal cond...

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Autores principales:	Carla Verdi, Ferenc Karsai, Peitao Liu, Ryosuke Jinnouchi, Georg Kresse
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/2ac9cc793fcf4d22b621028123daf930
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spelling	oai:doaj.org-article:2ac9cc793fcf4d22b621028123daf9302021-12-02T17:18:20ZThermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials10.1038/s41524-021-00630-52057-3960https://doaj.org/article/2ac9cc793fcf4d22b621028123daf9302021-09-01T00:00:00Zhttps://doi.org/10.1038/s41524-021-00630-5https://doaj.org/toc/2057-3960Abstract Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia, an important transition metal oxide. This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point. We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory, which allows to account for anharmonic effects to all orders. This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials.Carla VerdiFerenc KarsaiPeitao LiuRyosuke JinnouchiGeorg KresseNature PortfolioarticleMaterials of engineering and construction. Mechanics of materialsTA401-492Computer softwareQA76.75-76.765ENnpj Computational Materials, Vol 7, Iss 1, Pp 1-9 (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 Carla Verdi Ferenc Karsai Peitao Liu Ryosuke Jinnouchi Georg Kresse Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
description	Abstract Machine-learned interatomic potentials enable realistic finite temperature calculations of complex materials properties with first-principles accuracy. It is not yet clear, however, how accurately they describe anharmonic properties, which are crucial for predicting the lattice thermal conductivity and phase transitions in solids and, thus, shape their technological applications. Here we employ a recently developed on-the-fly learning technique based on molecular dynamics and Bayesian inference in order to generate an interatomic potential capable to describe the thermodynamic properties of zirconia, an important transition metal oxide. This machine-learned potential accurately captures the temperature-induced phase transitions below the melting point. We further showcase the predictive power of the potential by calculating the heat transport on the basis of Green–Kubo theory, which allows to account for anharmonic effects to all orders. This study indicates that machine-learned potentials trained on the fly offer a routine solution for accurate and efficient simulations of the thermodynamic properties of a vast class of anharmonic materials.
format	article
author	Carla Verdi Ferenc Karsai Peitao Liu Ryosuke Jinnouchi Georg Kresse
author_facet	Carla Verdi Ferenc Karsai Peitao Liu Ryosuke Jinnouchi Georg Kresse
author_sort	Carla Verdi
title	Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
title_short	Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
title_full	Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
title_fullStr	Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
title_full_unstemmed	Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
title_sort	thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2ac9cc793fcf4d22b621028123daf930
work_keys_str_mv	AT carlaverdi thermaltransportandphasetransitionsofzirconiabyontheflymachinelearnedinteratomicpotentials AT ferenckarsai thermaltransportandphasetransitionsofzirconiabyontheflymachinelearnedinteratomicpotentials AT peitaoliu thermaltransportandphasetransitionsofzirconiabyontheflymachinelearnedinteratomicpotentials AT ryosukejinnouchi thermaltransportandphasetransitionsofzirconiabyontheflymachinelearnedinteratomicpotentials AT georgkresse thermaltransportandphasetransitionsofzirconiabyontheflymachinelearnedinteratomicpotentials
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Thermal transport and phase transitions of zirconia by on-the-fly machine-learned interatomic potentials

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