Machine learning guided prediction of dynamic energy release in high-entropy alloys

Abstract

High-entropy alloy (HEA) type energetic structural materials (ESMs) offer exceptional strength, adequate ductility and reactivity upon dynamic loading, thus demonstrating great potentials in pyrotechnic applications. However, the main factors governing their energetic performance remain elusive, primarily attributable to the intricate mechanical-thermal-chemical coupling effects and the inherent challenges of HEA design. To address this, we propose a small-data machine learning framework designed to predict the energetic performance of HEA-type ESMs, employing support vector regression, leave-one-out cross-validation, and principal component analysis (PCA) to effectively manage a small, unevenly distributed, and highly dimensional dataset. Notably, the framework achieved a coefficient of determination (R2) of 0.854 while upholding robust performance, interpretability and computational efficiency. Fracture elongation (εt) and compressive yield strength (σcys) were identified as critical features, with σcys positively influencing performance while both εt and unit theoretical heat of combustion (UTHC) demonstrated negative effect. Guided by the framework, a series of novel Ti-V-Ta-Zr alloys with the comparable UTHC, velocity (v) and weight (m) but tailored εt and σcys were designed and tested. Ti30V30Ta30Zr10 alloy exhibited a commendable balance of mechanical properties and the smallest mean particle size, aligning with the model predictions and suggesting more thorough energy release during ballistic experiments.