Smart design tungsten-weighted refractory high entropy alloys (WW-RHEAs) under dynamic loading

Tungsten-weighted refractory high-entropy alloys (WW-RHEAs) are refractory high-entropy alloys containing W as a major constituent to achieve high density. Through composition tuning, these alloys can simultaneously satisfy the requirements of high density, high strength, and plasticity in dynamic impact environments. The development of WW-RHEAs faces critical challenges, including insufficient interpretability of data-driven machine learning models for predicting the unique dynamic behavior of such alloys, as well as the multi-objective optimization problem arising from the persistent strength-ductility trade-off. To address these challenges, this study constructed an intelligent design framework, termed machine learning-based multi-objective strategy (MMOS), which integrates the philosophy of being knowledge-enabled and data-driven. By integrating both physical parameters and composition as feature variables, the designed “composition and knowledge” fusion model demonstrated strong performance in predicting dynamic yield strength and fracture strain, achieving test the coefficient of determination values of 0.96 for strength and 0.87 for fracture strain. Through three iterative optimization rounds, each performing 250,000 objective evaluations on hypothetical compositions, the framework efficiently navigated the compositional space. The Pareto-optimal as-cast alloy W30Mo30Fe20Ni20 in the W-Mo-Fe-Ni system was identified by the optimization framework as a promising candidate and experimentally characterized by split Hopkinson pressure bar experiments. This alloy exhibits a dynamic compressive yield strength of 2,174 MPa and a maximum compressive strain of 25% attained before unloading at a strain rate of 2,054 s-1 under high-strain-rate dynamic compression, demonstrating an attractive combination of strength and ductility in the dynamic compression regime. The results demonstrate the effectiveness of MMOS in efficiently exploring the composition space while incorporating practical fabricability considerations, and highlight its knowledge-enabled capability in enhancing the interpretability of predictive models.