Accelerated Discovery of Refractory High-Entropy Alloys via Interpretable Machine Learning

Due to the outstanding thermal stability, inherent high melting points, and elevated temperature strengths, refractory high-entropy alloys (RHEAs) have been widely used for extreme environments in aerospace, nuclear energy, and advanced propulsion systems. Herein, we present an integrated design and simulation framework for RHEAs, combining machine learning potentials, supervised regression models, and multiobjective optimization algorithms. Utilizing a universal neuroevolution potential version 1 (UNEP-v1), the framework significantly enhances the accuracy of atomic-scale simulation while substantially reducing computational cost. High-throughput molecular dynamics simulations generate melting points and ultimate tensile strengths at 1000 K for various alloy compositions. Supervised regression models enable a rapid performance prediction. Integrating Shapley Additive exPlanations, Partial Dependence Plots, Accumulated Local Effects, and Individual Conditional Expectation analysis can provide a comprehensive interpretability toolkit. Validation of the proposed method in the TiVCrZrMo alloy system demonstrates its efficacy in designing high-strength, high-temperature resistant alloys. We not only develop a precise and interpretable predictive modeling paradigm but also establish procedural frameworks, promoting the integration of atomic-scale simulations with data-driven approaches for RHEAs in extreme environments.