Machine Learning Interatomic Potential for High-Throughput Screening of High-Entropy Alloys

A Pandey and J Gigax and R Pokharel, JOM, 74, 2908-2920 (2022).

DOI: 10.1007/s11837-022-05306-z

We have developed a machine learning-based interatomic potential (MLIP) for the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high-entropy alloys (HEAs). The MLIP enabled accurate high-throughput calculations of the elastic and mechanical properties of various non-equimolar R4 and R5 alloys, which are otherwise very time-consuming calculations when performed using density functional theory (DFT). We demonstrate that the MLIP predicted properties compare well with the DFT results on various test cases, and are consistent with the available experimental data. MLIPs are also utilized for high-throughput screening of non-equimolar R4 candidates by guided iterative tuning of R4 compositions, to discover candidate materials with promising hardness-ductility combinations. We also used this approach to study the effect of Ti concentration on the elastic and mechanical properties of R4, by statistically averaging the properties of over 100 random structures. The MLIPs predicted the hardness and bulk modulus of equimolar R4 and R5 HEAs which were validated using experimentally measured Vicker's hardness and modulus. This approach opens a new avenue for employing MLIPs for screening HEA candidates.

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