Machine learning–enabled high-entropy alloy discovery

Rao, Ziyuan; Tung, Po-Yen; Xie, Ruiwen; Wei, Ye; Zhang, Hongbin; Ferrari, Alberto; Klaver, T. P. C.; Körmann, Fritz; Prithiv, Thoudden Sukumar; Kwiatkowski da Silva, Alisson; Chen, Yao; Li, Zhiming; Ponge, Dirk; Neugebauer, Jörg; Gutfleisch, Oliver; Bauer , Stefan; Raabe, Dierk

doi:10.1126/science.abo4940

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Machine learning–enabled high-entropy alloy discovery

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Rao, Ziyuan
De magnete - Designing Magnetism on the atomic scale, MPG Group, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
High-Entropy Alloys, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Tung, Po-Yen
Materials Science of Mechanical Contacts, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Department of Earth Sciences, University of Cambridge, Cambridge, UK;

/persons/resource/persons224643

Wei, Ye
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons125232

Körmann, Fritz
Complex Concentrated Alloys, Project Groups, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Prithiv, Thoudden Sukumar
Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons136727

Kwiatkowski da Silva, Alisson
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

Chen, Yao
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
School of Civil Engineering, Southeast University, Nanjing, China;

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Li, Zhiming
High-Entropy Alloys, Project Groups, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083 China;

/persons/resource/persons125320

Ponge, Dirk
Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

/persons/resource/persons125293

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

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Gutfleisch, Oliver
De magnete - Designing Magnetism on the atomic scale, MPG Group, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;
Functional Materials, Materials Science, Technical University of Darmstadt, 64287 Darmstadt, Germany;

/persons/resource/persons125330

Raabe, Dierk
Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society;

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Citation

Rao, Z., Tung, P.-Y., Xie, R., Wei, Y., Zhang, H., Ferrari, A., et al. (2022). Machine learning–enabled high-entropy alloy discovery. Science, 378(6615), 78-85. doi:10.1126/science.abo4940.

Cite as: https://hdl.handle.net/21.11116/0000-000B-73C4-6

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.