Z. Li1, R. Xie2, H. Zhang2, F. Körmann1, J. Neugebauer1, D. Raabe1
1Max-Planck-Institut für Eisenforschung, Düsseldorf, Germany; 2TU Darmstadt
After the first discovery of the Fe64Ni36 Invar alloy in 1896 , new Invar alloys with improved and tailor-made properties have been developed for specific engineering applications . However, Invar alloys are still not fully optimized in terms of several additional features such as strength, toughness, costs, mass density and corrosion resistance. In this context, the concept of HEAs/MEAs provides a great chance for the understanding of Invar effects and further designing of novel Invar alloys. In our work, we integrated machine learning with density-functional theory and experiments to investigate the Invar effect in HEAs/MEAs. An Invar alloy search map is constructed based on the present results and available literature data to visualize the relationships among saturation magnetization, Curie temperature and thermal expansion coefficient for a wide range of Invar alloys . With an active learning loop, 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 .