Efficient small-cell sampling for machine-learning potentials of multi-principal element alloys

Abstract

Multi-principal element alloys (MPEAs) exhibit exceptional properties but face significant challenges in developing accurate machine-learning potentials (MLPs) due to their vast compositional and configurational complexity. Here, we introduce an efficient small-cell sampling (SCS) method, which allows for generating diverse and representative training datasets for MPEAs using only small-cell structures with just one and two elements, thereby bypassing the computational overhead of iterative active learning cycles and large-cell density functional theory calculations. The efficacy of the method is carefully validated through principal component analysis, extrapolation grades evaluation, and root-mean-square errors and physical properties assessment on the TiZrHfCuNi system. Further demonstrations on TiZrVMo, CoCrFeMnNi, and AlTiZrNbHfTa systems accurately reproduce complex phenomena including phase transitions, chemical orderings, and thermodynamic properties. This work establishes an efficient one-shot protocol for constructing high-quality training datasets across multiple elements, laying a solid foundation for developing universal MLPs for MPEAs.