Ziyuan Rao

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 [1], new Invar alloys with improved and tailor-made properties have been developed for specific engineering applications [2]. 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 [3]. 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 [4].

C.É. Guillaume
Recherches sur les aciers au nickel. Dilatations aux temperatures elevees; resistance electrique,
C. R. Acad. Sci. 125 (235) (1897) 18.
M. Shiga,
Invar alloys,
Curr. Opin. Solid State Mater. Science 1 (3) (1996) 340–348.
Z. Rao , D. Ponge , F. Körmann , Y. Ikeda , O. Schneeweiss , M. Friák , J. Neugebauer , D. Raabe , Z. Li
Invar effects in FeNiCo medium entropy alloys: From an Invar treasure map to alloy design
Intermetallics 111 (2019) 106520 .
Z. Rao, et al.
Machine learning–enabled high-entropy alloy discovery
Science, 2022, 378(6615): 78-85.
Go to Editor View