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  Accelerating the design of compositionally complex materials via physics-informed artificial intelligence

Raabe, D., Mianroodi, J. R., & Neugebauer, J. (2023). Accelerating the design of compositionally complex materials via physics-informed artificial intelligence. Nature Computational Science, 3(3), 198-209. doi:10.1038/s43588-023-00412-7.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000D-5A1B-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000D-5A1C-0
Genre: Journal Article

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 Creators:
Raabe, Dierk1, 2, Author           
Mianroodi, Jaber Rezaei3, Author           
Neugebauer, Jörg4, Author           
Affiliations:
1Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
2Sustainable Synthesis of Materials, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3289784              
3Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3243050              
4Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              

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 Abstract: The chemical space for designing materials is practically infinite. This makes disruptive progress by traditional physics-based modeling alone challenging. Yet, training data for identifying composition–structure–property relations by artificial intelligence are sparse. We discuss opportunities to discover new chemically complex materials by hybrid methods where physics laws are combined with artificial intelligence.

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Language(s): eng - English
 Dates: 2023-03-31
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s43588-023-00412-7
 Degree: -

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Project name : BIGmax research network
Grant ID : -
Funding program : Financial support by the BIGmax research network of the Max-Planck Society (https://www.bigmax.mpg.de/).
Funding organization : Max-Planck Society

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Title: Nature Computational Science
  Abbreviation : Nat Comput Sci
Source Genre: Journal
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Publ. Info: London, UK : Nature Research
Pages: - Volume / Issue: 3 (3) Sequence Number: - Start / End Page: 198 - 209 Identifier: ISSN: 2662-8457
CoNE: https://pure.mpg.de/cone/journals/resource/2662-8457