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

• A small-data machine learning framework was proposed to aid high-entropy alloy design. • R 2 of the approach is 0.854 while maintaining high interpretability and efficiency. • Compressive yield strength, fracture elongation are suggested as key factors. 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 (R 2 ) of 0.854 while upholding robust performance, interpretability and computational efficiency. Fracture elongation ( ε t ) and compressive yield strength ( σ cys ) were identified as critical features, with σ cys positively influencing performance while both ε t 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 ε t and σ cys were designed and tested. Ti 30 V 30 Ta 30 Zr 10 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.