Machine learning–enabled high-entropy alloy discovery

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…

• IM
  International Max Planck Research School for Environmental, Cellular and Molecular Microbiology
• DF
  Deutsche Forschungsgemeinschaft

  Awards: ERC-CoG-SHINE-771602, 405553726, TRR 270, 405553726-TRR 270, 771602
• M
  Max-Planck-Gesellschaft
• TU
  Technische Universität Darmstadt
• UD
  Universität Duisburg-Essen
• IM
  International Max Planck Research School for Advanced Methods in Process and Systems Engineering
• MF
  Max-Planck-Institut für Eisenforschung