Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can b...

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Autores principales:	K. T. Schütt, M. Gastegger, A. Tkatchenko, K.-R. Müller, R. J. Maurer
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/23a7395375c64d40967fed2e3eac6dc0
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spelling	oai:doaj.org-article:23a7395375c64d40967fed2e3eac6dc02021-12-02T16:57:20ZUnifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions10.1038/s41467-019-12875-22041-1723https://doaj.org/article/23a7395375c64d40967fed2e3eac6dc02019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-12875-2https://doaj.org/toc/2041-1723Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.K. T. SchüttM. GasteggerA. TkatchenkoK.-R. MüllerR. J. MaurerNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
description	Machine learning models can accurately predict atomistic chemical properties but do not provide access to the molecular electronic structure. Here the authors use a deep learning approach to predict the quantum mechanical wavefunction at high efficiency from which other ground-state properties can be derived.
format	article
author	K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer
author_facet	K. T. Schütt M. Gastegger A. Tkatchenko K.-R. Müller R. J. Maurer
author_sort	K. T. Schütt
title	Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
title_short	Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
title_full	Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
title_fullStr	Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
title_full_unstemmed	Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
title_sort	unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/23a7395375c64d40967fed2e3eac6dc0
work_keys_str_mv	AT ktschutt unifyingmachinelearningandquantumchemistrywithadeepneuralnetworkformolecularwavefunctions AT mgastegger unifyingmachinelearningandquantumchemistrywithadeepneuralnetworkformolecularwavefunctions AT atkatchenko unifyingmachinelearningandquantumchemistrywithadeepneuralnetworkformolecularwavefunctions AT krmuller unifyingmachinelearningandquantumchemistrywithadeepneuralnetworkformolecularwavefunctions AT rjmaurer unifyingmachinelearningandquantumchemistrywithadeepneuralnetworkformolecularwavefunctions
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Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

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