A machine learning framework for discovering high entropy alloys phase formation drivers.

In the past years, high entropy alloys (HEAs) witnessed great interest because of their superior properties. Phase prediction using machine learning (ML) methods was one of the main research themes in HEAs in the past three years. Although various ML-based phase prediction works exhibited high accuracy, only a few studied the variables that drive the phase formation in HEAs. Those (the previously mentioned work) did that by incorporating domain knowledge in the feature engineering part of the ML framework. In this work, we tackle this problem from a different direction by predicting the phase of HEAs, based only on the concentration of the alloy constituent elements. Then, pruned tree models and linear correlation are used to develop simple primitive prediction rules that are used with self-organizing maps (SOMs) and constructed Euclidean spaces to formulate the problem of discovering the phase formation drivers as an optimization problem. In addition, genetic algorithm (GA) optimization results reveal that the phase formation is affected by the electron affinity, molar volume, and resistivity of the constituent elements. Moreover, one of the primitive prediction rules reveals that the FCC phase formation in the AlCoCrFeNiTiCu family of high entropy alloys can be predicted with 87% accuracy by only knowing the concentration of Al and Cu.