5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)

Data‐driven science is heralded as the new paradigm in materials science. Data infrastructures store vast amounts of materials data. Machine learning algorithms systematically extract knowledge from materials data streams to discover new materials for future technologies and the well‐being of societ...

Descripción completa

Detalles Bibliográficos
Autores principales: Himanen, Lauri, Geurts, Amber, Foster, Adam Stuart, Rinke, Patrick
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Cover Picture
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839620/
http://dx.doi.org/10.1002/advs.201970125
_version_ 1783467465047539712
author Himanen, Lauri
Geurts, Amber
Foster, Adam Stuart
Rinke, Patrick
author_facet Himanen, Lauri
Geurts, Amber
Foster, Adam Stuart
Rinke, Patrick
author_sort Himanen, Lauri
collection PubMed
description Data‐driven science is heralded as the new paradigm in materials science. Data infrastructures store vast amounts of materials data. Machine learning algorithms systematically extract knowledge from materials data streams to discover new materials for future technologies and the well‐being of society. In article number https://doi.org/10.1002/advs.201900808, Patrick Rinke and co‐worker review the current state of data‐driven materials science. [Image: see text]
format Online
Article
Text
id pubmed-6839620
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-68396202019-11-14 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019) Himanen, Lauri Geurts, Amber Foster, Adam Stuart Rinke, Patrick Adv Sci (Weinh) Cover Picture Data‐driven science is heralded as the new paradigm in materials science. Data infrastructures store vast amounts of materials data. Machine learning algorithms systematically extract knowledge from materials data streams to discover new materials for future technologies and the well‐being of society. In article number https://doi.org/10.1002/advs.201900808, Patrick Rinke and co‐worker review the current state of data‐driven materials science. [Image: see text] John Wiley and Sons Inc. 2019-11-08 /pmc/articles/PMC6839620/ http://dx.doi.org/10.1002/advs.201970125 Text en © 2019 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Cover Picture
Himanen, Lauri
Geurts, Amber
Foster, Adam Stuart
Rinke, Patrick
5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title_full 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title_fullStr 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title_full_unstemmed 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title_short 5th Anniversary Article: Data‐Driven Materials Science: Status, Challenges, and Perspectives (Adv. Sci. 21/2019)
title_sort 5th anniversary article: data‐driven materials science: status, challenges, and perspectives (adv. sci. 21/2019)
topic Cover Picture
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839620/
http://dx.doi.org/10.1002/advs.201970125
work_keys_str_mv AT himanenlauri 5thanniversaryarticledatadrivenmaterialssciencestatuschallengesandperspectivesadvsci212019
AT geurtsamber 5thanniversaryarticledatadrivenmaterialssciencestatuschallengesandperspectivesadvsci212019
AT fosteradamstuart 5thanniversaryarticledatadrivenmaterialssciencestatuschallengesandperspectivesadvsci212019
AT rinkepatrick 5thanniversaryarticledatadrivenmaterialssciencestatuschallengesandperspectivesadvsci212019

Ejemplares similares