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Journal Article

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

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Raabe,  Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Sustainable Synthesis of Materials, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Mianroodi,  Jaber Rezaei
Computational Sustainable Metallurgy, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Neugebauer,  Jörg
Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-000D-5A1B-1
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.