DataVXAdvanced SciencePaper
journal article·2019-11Open access

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

Lauri Himanen, Amber Geurts, Adam Stuart Foster, Patrick Rinke

Advanced Science· Volume 6· Issue 21
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Source abstract

Abstract

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 1900808, Patrick Rinke and co-worker review the current state of data-driven materials science.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Machine Learning in Materials Science

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Affiliations

Department of Applied Physics Aalto University P.O. Box 11100 00076 Aalto,Espoo Finland

Department of Management Studies Aalto University P.O. Box 11100 00076 Aalto,Espoo Finland

TNO, Netherlands Organization for Applied Scientific Research Expertise Center for Strategy and Policy Anna van Beurenplein 1 DA 2595 The Hague Netherlands

Graduate School Materials Science in Mainz Staudinger Weg 9 55128 Mainz Germany

WPI Nano Life Science Institute (WPI‐NanoLSI) Kanazawa University Kakuma‐machi Kanazawa 920‐1192 Japan

Theoretical Chemistry and Catalysis Research Centre Technische Universität München Lichtenbergstr. 4 D‐85747 Garching Germany

References

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