Breaking equiatomic constraints: knowledge-enhanced AI framework for function-oriented single-phase high-entropy alloy design

High-entropy alloys exhibit exceptional performance under extreme environments; however, conventional equiatomic or near-equiatomic design paradigms restrict exploration of single-phase solid-solution spaces, leaving many non-equiatomic compositions unexplored. Here, we propose a knowledge-enhanced AI framework integrating physics-guided machine learning with large language model (LLM) agents to explore compositions beyond equiatomic constraints. Machine learning models trained on physics-based descriptors achieve F1-scores of 0.96 and 0.94 for face-centered cubic (FCC) and body-centered cubic (BCC) phase prediction, respectively. Two substantially non-equiatomic alloys—Al28Cr22Fe26Ni15Mo9 (BCC) and Co23Cr15Fe25Ni31Mo6 (FCC)—experimentally confirm reliable phase prediction within the investigated compositional domain. LLM-guided reasoning on oxidation mechanisms further enables the design of Co22Cr24Fe20Ni25Mo5Mn4, which exhibits a steady-state oxidation rate constant of 1.41 × 10−8 mgn cm−2n h−1 (50–100 h, n = 45.35) at 900 °C, indicative of self-limiting oxidation kinetics. This superior performance is attributed to the formation of a stable Cr2O3/(Mn,Fe)Cr2O4 bilayer oxide scale operating via a synergistic barrier–buffer protection mechanism. This study presents a data-efficient, AI-assisted methodology for intelligent HEA design in high-dimensional compositional spaces.