Quantum-chemical insights from deep tensor neural networks

Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R. Müller, Alexandre Tkatchenko
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/459cc75532c24b7085302dabbc24454d
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:459cc75532c24b7085302dabbc24454d
record_format	dspace
spelling	oai:doaj.org-article:459cc75532c24b7085302dabbc24454d2021-12-02T14:41:11ZQuantum-chemical insights from deep tensor neural networks10.1038/ncomms138902041-1723https://doaj.org/article/459cc75532c24b7085302dabbc24454d2017-01-01T00:00:00Zhttps://doi.org/10.1038/ncomms13890https://doaj.org/toc/2041-1723Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.Kristof T. SchüttFarhad ArbabzadahStefan ChmielaKlaus R. MüllerAlexandre TkatchenkoNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela Klaus R. Müller Alexandre Tkatchenko Quantum-chemical insights from deep tensor neural networks
description	Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.
format	article
author	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela Klaus R. Müller Alexandre Tkatchenko
author_facet	Kristof T. Schütt Farhad Arbabzadah Stefan Chmiela Klaus R. Müller Alexandre Tkatchenko
author_sort	Kristof T. Schütt
title	Quantum-chemical insights from deep tensor neural networks
title_short	Quantum-chemical insights from deep tensor neural networks
title_full	Quantum-chemical insights from deep tensor neural networks
title_fullStr	Quantum-chemical insights from deep tensor neural networks
title_full_unstemmed	Quantum-chemical insights from deep tensor neural networks
title_sort	quantum-chemical insights from deep tensor neural networks
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/459cc75532c24b7085302dabbc24454d
work_keys_str_mv	AT kristoftschutt quantumchemicalinsightsfromdeeptensorneuralnetworks AT farhadarbabzadah quantumchemicalinsightsfromdeeptensorneuralnetworks AT stefanchmiela quantumchemicalinsightsfromdeeptensorneuralnetworks AT klausrmuller quantumchemicalinsightsfromdeeptensorneuralnetworks AT alexandretkatchenko quantumchemicalinsightsfromdeeptensorneuralnetworks
_version_	1718389986167881728

Quantum-chemical insights from deep tensor neural networks

Ejemplares similares