Data-driven design of high entropy alloys using the voxelized atomic structure framework

Many modern science and technology applications require the development of new multifunctional materials with advanced, tailorable properties. The large design spaces of materials like high entropy alloys (HEAs) make existing physics-based high-throughput materials design approaches intractable, leading to the adoption of machine learning approaches. In this study, we use the recently developed Voxelized Atomic Structure (VASt) framework to model thermo-mechanical properties of MoNbTaTiVWZr-containing HEAs. We demonstrate that the VASt framework is capable of efficiently and accurately predicting complex properties that describe the material response to perturbations of the atomic structure using only the charge density field of the ground state structure. The resulting VASt structure-property relationship is then used to correlate practical HEA design space parameters with properties indicating strength, ductility, thermal response, and anisotropy. The correlations are used to identify new rules that can help guide the design of new HEAs with high strength, high ductility, and low thermal expansion.