Predicting Mechanical Properties of AlCoCrFeNi High‐Entropy Alloys Using Machine Learning and Molecular Dynamics

The study of mechanical properties in high‐entropy alloys (HEAs) using conventional methods faces significant challenges due to their complex composition, which substantially increases experimental difficulties and costs. Moreover, data acquisition, processing, and analysis in experimental approaches are often time‐consuming, severely limiting rapid evaluation of numerous HEAs. Machine learning (ML) offers a solution by leveraging its powerful data processing and pattern recognition capabilities to mine deep relationships between composition and mechanical properties from complex datasets. This study integrates molecular dynamics simulations with ML to predict Young's modulus ( E ) and ultimate tensile strength (UTS) of AlCoCrFeNi HEAs, analyzing their microstructural evolution and phase transformation behaviors under varying temperatures and strain rates. Six algorithms were compared, revealing the superior performance of ensemble methods: LightGBM achieved an R ² of 0.991 for predicting E , while CatBoost attained an R ² of 0.975 for UTS. This work provides novel approaches for HEA property prediction and valuable insights for modeling complex systems in materials science.