Universal fragment descriptors for predicting properties of inorganic crystals

Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical pro...

Full description

Saved in:

Bibliographic Details
Main Authors:	Olexandr Isayev, Corey Oses, Cormac Toher, Eric Gossett, Stefano Curtarolo, Alexander Tropsha
Format:	article
Language:	EN
Published:	Nature Portfolio 2017
Subjects:	Science Q
Online Access:	https://doaj.org/article/e51aabf9bad84fe6a8a7bf233cac04c5
Tags:	Add Tag No Tags, Be the first to tag this record!

id	oai:doaj.org-article:e51aabf9bad84fe6a8a7bf233cac04c5
record_format	dspace
spelling	oai:doaj.org-article:e51aabf9bad84fe6a8a7bf233cac04c52021-12-02T15:38:39ZUniversal fragment descriptors for predicting properties of inorganic crystals10.1038/ncomms156792041-1723https://doaj.org/article/e51aabf9bad84fe6a8a7bf233cac04c52017-06-01T00:00:00Zhttps://doi.org/10.1038/ncomms15679https://doaj.org/toc/2041-1723Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical properties.Olexandr IsayevCorey OsesCormac ToherEric GossettStefano CurtaroloAlexander TropshaNature PortfolioarticleScienceQENNature Communications, Vol 8, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Olexandr Isayev Corey Oses Cormac Toher Eric Gossett Stefano Curtarolo Alexander Tropsha Universal fragment descriptors for predicting properties of inorganic crystals
description	Machine learning methods can be useful for materials discovery; however certain properties remain difficult to predict. Here, the authors present a universal machine learning approach for modelling the properties of inorganic crystals, which is validated for eight electronic and thermomechanical properties.
format	article
author	Olexandr Isayev Corey Oses Cormac Toher Eric Gossett Stefano Curtarolo Alexander Tropsha
author_facet	Olexandr Isayev Corey Oses Cormac Toher Eric Gossett Stefano Curtarolo Alexander Tropsha
author_sort	Olexandr Isayev
title	Universal fragment descriptors for predicting properties of inorganic crystals
title_short	Universal fragment descriptors for predicting properties of inorganic crystals
title_full	Universal fragment descriptors for predicting properties of inorganic crystals
title_fullStr	Universal fragment descriptors for predicting properties of inorganic crystals
title_full_unstemmed	Universal fragment descriptors for predicting properties of inorganic crystals
title_sort	universal fragment descriptors for predicting properties of inorganic crystals
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/e51aabf9bad84fe6a8a7bf233cac04c5
work_keys_str_mv	AT olexandrisayev universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals AT coreyoses universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals AT cormactoher universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals AT ericgossett universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals AT stefanocurtarolo universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals AT alexandertropsha universalfragmentdescriptorsforpredictingpropertiesofinorganiccrystals
_version_	1718386145853702144

Universal fragment descriptors for predicting properties of inorganic crystals

Similar Items