Atomistic and data-driven insights into the local slip resistances in random refractory multi-principal element alloys

Abstract

Refractory multi-principal element alloys (RMPEAs) have attracted growing interest for their exceptional high-temperature strength, yet their complex compositions hinder a mechanistic understanding of plastic deformation. Here, we perform atomistic simulations to determine local slip resistances (LSRs) of edge and screw dislocations on primary BCC slip planes in 12 equal-molar RMPEAs. Machine learning is employed to uncover relationships between LSR and underlying material properties, enabling systematic assessment of compositional effects on dislocation behavior. Based on these insights, we develop a thermally activated, dislocation-based model to predict macroscopic yield stress. We find that increasing the fraction of hexagonal close-packed elements above 50% significantly reduces unstable stacking fault energy, ideal shear strength, and screw LSR across all slip planes. Higher elastic anisotropy further lowers these quantities, while lattice distortion modifies relative slip resistances between dislocation characters and slip systems. By combining an autoencoder with a random forest model, we identify elastic constants and lattice distortion as the dominant factors controlling LSR. The resulting framework accurately predicts tensile yield stress in BCC RMPEAs and provides guidance for alloy design.