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  Machine learning–enabled high-entropy alloy discovery

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.

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Rao, Ziyuan1, 2, Autor           
Tung, Po-Yen3, 4, Autor           
Xie, Ruiwen5, Autor
Wei, Ye6, Autor           
Zhang, Hongbin5, Autor
Ferrari, Alberto7, Autor           
Klaver, T. P. C.7, Autor
Körmann, Fritz8, Autor           
Prithiv, Thoudden Sukumar9, Autor           
Kwiatkowski da Silva, Alisson6, Autor           
Chen, Yao6, 10, Autor
Li, Zhiming11, 12, Autor           
Ponge, Dirk9, Autor           
Neugebauer, Jörg13, Autor           
Gutfleisch, Oliver1, 14, Autor           
Bauer , Stefan15, Autor
Raabe, Dierk6, Autor           
Affiliations:
1De magnete - Designing Magnetism on the atomic scale, MPG Group, Interdepartmental and Partner Groups, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3260224              
2High-Entropy Alloys, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3010672              
3Materials Science of Mechanical Contacts, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_2324693              
4Department of Earth Sciences, University of Cambridge, Cambridge, UK, ou_persistent22              
5Institut für Materialwissenschaft, Technische Universität Darmstadt, Darmstadt, Germany, ou_persistent22              
6Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863381              
7Materials Science and Engineering, Delft University of Technology, Delft, 2628CD, The Netherlands, ou_persistent22              
8Complex Concentrated Alloys, Project Groups, Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3291775              
9Mechanism-based Alloy Design, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863383              
10School of Civil Engineering, Southeast University, Nanjing, China, ou_persistent22              
11High-Entropy Alloys, Project Groups, Microstructure Physics and Alloy Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_3010672              
12State Key Laboratory of Powder Metallurgy, Central South University, Changsha, 410083 China, ou_persistent22              
13Computational Materials Design, Max-Planck-Institut für Eisenforschung GmbH, Max Planck Society, ou_1863337              
14Functional Materials, Materials Science, Technical University of Darmstadt, 64287 Darmstadt, Germany, ou_persistent22              
15KTH Royal Institute of Technology, Stockholm, Sweden, ou_persistent22              

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 Zusammenfassung: 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.

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Sprache(n): eng - English
 Datum: 2022-10-06
 Publikationsstatus: Erschienen
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 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.1126/science.abo4940
 Art des Abschluß: -

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Titel: Science
  Kurztitel : Science
Genre der Quelle: Zeitschrift
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Ort, Verlag, Ausgabe: Washington, D.C. : American Association for the Advancement of Science
Seiten: - Band / Heft: 378 (6615) Artikelnummer: - Start- / Endseite: 78 - 85 Identifikator: ISSN: 0036-8075
CoNE: https://pure.mpg.de/cone/journals/resource/991042748276600_1