Design of High Entropy Alloys using Machine Learning and Ab-initio Molecular Dynamics

A material design strategy is proposed combining machine learning models with optimization algorithms to guide the design of novel High-Entropy Alloys (HEAs) optimized for High-Temperature strength and Density. The data used to train machine learning model was generated using Ab-initio Molecular Dynamics, and contained composition of the alloys, their mechanical properties, and relevant chemical descriptors previously identified in literature. This Rapid Alloy Design strategy can be used to optimize the properties of other multi-component alloys such as Bulk Metallic Glasses and Nickel Superalloys.