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...

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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:
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6839620/
http://dx.doi.org/10.1002/advs.201970125
Descripción
Sumario: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]

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