Machine learning-enabled high-entropy alloy discovery

High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, as 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 2 high-entropy Invar alloys with extremely low thermal expansion coefficients around 2×10-6 K-1 at 300 K. We believe this to be a different pathway for the fast and automated discovery of high-entropy alloys with attractive optimal thermal, magnetic and electrical properties.High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and property regimes inaccessible for dilute materials. Discovering those with valuable properties, however, too often relies on serendipity, as 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 2 high-entropy Invar alloys with extremely low thermal expansion coefficients around 2×10-6 K-1 at 300 K. We believe this to be a different pathway for the fast and automated discovery of high-entropy alloys with attractive optimal thermal, magnetic and electrical properties