Machine Learning-Enabled Prediction and Mechanistic Analysis of Compressive Yield Strength–Hardness Correlation in High-Entropy Alloys

High-entropy alloys (HEAs) represent a paradigm-shifting material system offering vast compositional space for tailoring mechanical properties. The yield strength and hardness are critical performance metrics, yet their interrelationships in diverse HEAs remain incompletely understood, partly due to data limitations. This work employs an integrated machine learning framework to investigate the compressive yield strength (σ_y) and hardness (HV) correlation across a dataset of cast HEAs. Random forest models are successfully developed for phase structure classification (accuracy = 92%), hardness prediction (test R² = 0.90), and yield strength prediction (test R² = 0.91), enabling data imputation to expand the analysis dataset. Correlation analysis on the expanded dataset reveals a general positive trend between σ_y and HV (overall Pearson r = 0.75) but highlights a strong dependence on the predicted phase structure. The single-phase BCC alloys exhibit the strongest linear correlation between σ_y and HV (r = 0.88), whereas the single-phase FCC alloys show a weaker linear dependence (r = 0.59), and multiphase alloy systems display varied behavior. The specific ranges of compositional parameters (highly negative mixing enthalpy ΔH, low atomic size difference δ, high mixing entropy ΔS, and intermediate-to-high valence electron concentration VEC) are associated with a stronger σ_y-HV correlation, potentially linked to the formation of stable solid solutions. Furthermore, artificial neural network modeling confirms the varying complexity of the σ_y-HV relationship across different phases, outperforming simple models for some multiphase systems. This work provides robust predictive models for HEA properties and advances the fundamental understanding of the composition- and phase-dependent coupling between yield strength and hardness, aiding rational HEA design.