  We have developed a machine learning-based interatomic potential (MLIP) for
the quaternary MoNbTaW (R4) and quinary MoNbTaTiW (R5) high entropy alloys
(HEAs). MLIPs enabled accurate high throughput calculations of 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. The MLIPs are also utilized for high
throughput optimization 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. MLIP
predicted hardness and bulk modulus of equimolar R4 and R5 HEAs are validated
using experimentally measured Vickers hardness and modulus. This approach opens
a new avenue for employing MLIPs for HEA candidate optimization.
