Print Email Facebook Twitter Machine learning-enabled high-entropy alloy discovery Title Machine learning-enabled high-entropy alloy discovery Author Rao, Ziyuan (Max-Planck-Institut für Eisenforschung) Tung, Po Yen (Max-Planck-Institut für Eisenforschung; University of Cambridge) Xie, Ruiwen (Technische Universität Darmstadt) Wei, Ye (Max-Planck-Institut für Eisenforschung) Ferrari, A. (TU Delft Team Marcel Sluiter) Klaver, T.P.C. (TU Delft Team Marcel Sluiter) Körmann, F.H.W. (TU Delft Team Marcel Sluiter; Max-Planck-Institut für Eisenforschung) Li, Zhiming (Max-Planck-Institut für Eisenforschung; Central South University China) Bauer, Stefan (KTH Royal Institute of Technology) Raabe, Dierk (Max-Planck-Institut für Eisenforschung) Date 2022 Abstract High-entropy alloys are solid solutions of multiple principal elements that are capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, because thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. We propose an active learning strategy to accelerate the design of high-entropy Invar alloys in a practically infinite compositional space based on very sparse data. Our approach works as a closed-loop, integrating machine learning with density-functional theory, thermodynamic calculations, and experiments. After processing and characterizing 17 new alloys out of millions of possible compositions, we identified two high-entropy Invar alloys with extremely low thermal expansion coefficients around 2 × 10-6 per degree kelvin at 300 kelvin. We believe this to be a suitable pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic, and electrical properties. To reference this document use: http://resolver.tudelft.nl/uuid:8576ecef-2e12-44c4-93d2-2819cd82d188 DOI https://doi.org/10.1126/science.abo4940 Embargo date 2023-04-06 ISSN 0036-8075 Source Science, 378 (6615), 78-85 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type journal article Rights © 2022 Ziyuan Rao, Po Yen Tung, Ruiwen Xie, Ye Wei, A. Ferrari, T.P.C. Klaver, F.H.W. Körmann, Zhiming Li, Stefan Bauer, Dierk Raabe, More Authors Files PDF science.abo4940.pdf 1.5 MB Close viewer /islandora/object/uuid:8576ecef-2e12-44c4-93d2-2819cd82d188/datastream/OBJ/view