Machine learning based energy-free structure predictions of molecules, transition states, and solids

Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.

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Autores principales:	Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/63dbdfe92c2f4710af3a11c94b9cdfc4
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spelling	oai:doaj.org-article:63dbdfe92c2f4710af3a11c94b9cdfc42021-12-02T17:55:05ZMachine learning based energy-free structure predictions of molecules, transition states, and solids10.1038/s41467-021-24525-72041-1723https://doaj.org/article/63dbdfe92c2f4710af3a11c94b9cdfc42021-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24525-7https://doaj.org/toc/2041-1723Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.Dominik LemmGuido Falk von RudorffO. Anatole von LilienfeldNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dominik Lemm Guido Falk von Rudorff O. Anatole von Lilienfeld Machine learning based energy-free structure predictions of molecules, transition states, and solids
description	Accurate computational prediction of atomistic structure with traditional methods is challenging. The authors report a kernel-based machine learning model capable of reconstructing 3D atomic coordinates from predicted interatomic distances across a variety of system classes.
format	article
author	Dominik Lemm Guido Falk von Rudorff O. Anatole von Lilienfeld
author_facet	Dominik Lemm Guido Falk von Rudorff O. Anatole von Lilienfeld
author_sort	Dominik Lemm
title	Machine learning based energy-free structure predictions of molecules, transition states, and solids
title_short	Machine learning based energy-free structure predictions of molecules, transition states, and solids
title_full	Machine learning based energy-free structure predictions of molecules, transition states, and solids
title_fullStr	Machine learning based energy-free structure predictions of molecules, transition states, and solids
title_full_unstemmed	Machine learning based energy-free structure predictions of molecules, transition states, and solids
title_sort	machine learning based energy-free structure predictions of molecules, transition states, and solids
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/63dbdfe92c2f4710af3a11c94b9cdfc4
work_keys_str_mv	AT dominiklemm machinelearningbasedenergyfreestructurepredictionsofmoleculestransitionstatesandsolids AT guidofalkvonrudorff machinelearningbasedenergyfreestructurepredictionsofmoleculestransitionstatesandsolids AT oanatolevonlilienfeld machinelearningbasedenergyfreestructurepredictionsofmoleculestransitionstatesandsolids
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Machine learning based energy-free structure predictions of molecules, transition states, and solids

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