Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in o...

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Autores principales:	Jose Antonio Garrido Torres, Vahe Gharakhanyan, Nongnuch Artrith, Tobias Hoffmann Eegholm, Alexander Urban
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4e7b0698a52f42319f93d8d5a7ab17e2
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id	oai:doaj.org-article:4e7b0698a52f42319f93d8d5a7ab17e2
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spelling	oai:doaj.org-article:4e7b0698a52f42319f93d8d5a7ab17e22021-12-05T12:22:46ZAugmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures10.1038/s41467-021-27154-22041-1723https://doaj.org/article/4e7b0698a52f42319f93d8d5a7ab17e22021-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-27154-2https://doaj.org/toc/2041-1723Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in oxides.Jose Antonio Garrido TorresVahe GharakhanyanNongnuch ArtrithTobias Hoffmann EegholmAlexander UrbanNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jose Antonio Garrido Torres Vahe Gharakhanyan Nongnuch Artrith Tobias Hoffmann Eegholm Alexander Urban Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
description	Computational material design often does not account for temperature effects. The present manuscript combines quantum-mechanics based calculations with a machine-learned correction to establish a unified thermodynamics framework for accurate prediction of high temperature reaction free energies in oxides.
format	article
author	Jose Antonio Garrido Torres Vahe Gharakhanyan Nongnuch Artrith Tobias Hoffmann Eegholm Alexander Urban
author_facet	Jose Antonio Garrido Torres Vahe Gharakhanyan Nongnuch Artrith Tobias Hoffmann Eegholm Alexander Urban
author_sort	Jose Antonio Garrido Torres
title	Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
title_short	Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
title_full	Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
title_fullStr	Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
title_full_unstemmed	Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
title_sort	augmenting zero-kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e7b0698a52f42319f93d8d5a7ab17e2
work_keys_str_mv	AT joseantoniogarridotorres augmentingzerokelvinquantummechanicswithmachinelearningforthepredictionofchemicalreactionsathightemperatures AT vahegharakhanyan augmentingzerokelvinquantummechanicswithmachinelearningforthepredictionofchemicalreactionsathightemperatures AT nongnuchartrith augmentingzerokelvinquantummechanicswithmachinelearningforthepredictionofchemicalreactionsathightemperatures AT tobiashoffmanneegholm augmentingzerokelvinquantummechanicswithmachinelearningforthepredictionofchemicalreactionsathightemperatures AT alexanderurban augmentingzerokelvinquantummechanicswithmachinelearningforthepredictionofchemicalreactionsathightemperatures
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Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures

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