Predicting the elastic properties of high-entropy alloys
Scientific Achievement
Neutron diffraction provided experimental validation of machine learning aided first principles predictions of the elastic properties of a severely lattice-distorted high entropy alloy (HEA), Al0.3CoCrFeNi.
Significance and Impact
Multi-element HEAs offer great flexibility to exhibit outstanding engineering properties. This study validates new computational tools that have the potential to identify the useful HEAs with much improved efficiency.
Research Details
- Theoretical predictions of the HEA’s elastic properties were produced by first-principles calculations coupled with machine learning.
- The properties were measured via in-situ
"First-principles and machine learning predictions of elasticity in severely lattice-distorted high-entropy alloys with experimental validation,"
G. Kim, H. Diao, Chanho Lee, A.T. Samaei, T. Phan, M. de Jong, K. An, D. Ma, P.K. Liaw, W. Chen,
Acta Materialia, 181, 124-138 (2019), https://doi.org/10.1016/j.actamat.2019.09.026