From Phase Prediction to Phase Design: A ReAct Agent Framework for High-Entropy Alloy Discovery
Discovering high-entropy alloy (HEA) compositions that reliably form a target crystal phase is a high-dimensional inverse design problem that conventional trial-and-error experimentation and forward-only machine learning models cannot efficiently solve. Here we present a ReAct (Reasoning + Acting) LLM agent that autonomously proposes, validates, and iteratively refines HEA compositions by querying a calibrated XGBoost surrogate trained on 4,753 experimental records across four phases (FCC, BCC, BCC+FCC, BCC+IM), achieving 94.66\% accuracy (F1 macro = 0.896). Against Bayesian optimisation (BO) and random search baselines, the full-prompt agent achieves descriptor-space rediscovery rates of...
| Authors | Iman Peivaste, Salim Belouettar |
|---|---|
| Primary category | cond-mat.mtrl-sci |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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