Machine learning driven insights into yield strength of multi-principal element alloys

Abstract In this work, we present a machine learning (ML) model for predicting the compressive yield strength of multi-principal element alloys (MPEAs). The model uses physical and thermodynamic alloy descriptors that capture various aspects of the alloying process such as lattice distortion, chemical interaction and intermetallic formation, and was used to explore yield strength variations in a variety of pseudo-ternary alloy systems viz. Zr x -(NbTa) y -(MoW) 1− x − y , W x -Ti y -(HfNbTaZr) 1− x − y and (MoNb) x -(AlCr) y -(FeNi) 1− x − y wherein it was validated along continuous composition pathways through comparison with experimentally observed yield strength values. The Compositional-Stimulus and Model-Response framework was used to decode the decision-making process of the ML model for obtaining alloy specific insights. Further, the model predictions were compared with hardness and phase predictions from other pre-trained ML models to establish consistency between different ML models. The model enables high-throughput screening for targeted yield strength and identifies key features for capturing yield strength variations in MPEAs. Graphical abstract