High-entropy alloys are solid solutions of multiple principal elements, capable of reaching composition and feature regimes inaccessible for dilute materials. Discovering those with valuable properties, however, relies on serendipity, as thermodynamic alloy design rules alone often fail in high-dimensional composition spaces. Here, we propose an active-learning strategy to accelerate the design of novel 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. Our study thus opens a new pathway for the fast and automated discovery of high-entropy alloys with optimal thermal, magnetic and electrical properties.
@article{arxiv.2202.13753,
title = {Machine learning-enabled high-entropy alloy discovery},
author = {Ziyuan Rao and PoYen Tung and Ruiwen Xie and Ye Wei and Hongbin Zhang and Alberto Ferrari and T. P. C. Klaver and Fritz Körmann and Prithiv Thoudden Sukumar and Alisson Kwiatkowski da Silva and Yao Chen and Zhiming Li and Dirk Ponge and Jörg Neugebauer and Oliver Gutfleisch and Stefan Bauer and Dierk Raabe},
journal= {arXiv preprint arXiv:2202.13753},
year = {2022}
}