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

Nature Computational Science volume 3, pages 198–209 (2023)Cite this article

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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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Fig. 1: Chemical complexity influences microstructure complexity.
Fig. 2: Combinations of AI and physics-based models for the simulation of compositionally complex materials.
Fig. 3: Active learning in materials science.
Fig. 4: Different applications of data-driven and physics-informed AI for the modeling of compositionally complex materials.

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Acknowledgements

The authors are grateful for financial support by the BIGmax research network of the Max-Planck Society (https://www.bigmax.mpg.de/).

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    Dierk Raabe, Jaber Rezaei Mianroodi & Jörg Neugebauer

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

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