摘要
High-entropy alloys (HEAs) exhibit exceptional mechanical strength, thermal stability, and corrosion resistance; however, their vast compositional design space and the limited availability of structured composition–property data make systematic design and screening challenging. To address this challenge, this study proposes a reflection-enhanced, LLM-assisted literature-to-design framework for first-pass HEA screening and surrogate-guided inverse design. The workflow combines a dual-LLM extraction–reflection module for literature mining and data validation with composition-based surrogate modeling, genetic algorithm-driven candidate generation, and experimental validation in an integrated decision-support pipeline. The dual-LLM module first extracts structured composition–property data from unstructured scientific publications, achieving a precision of 0.943 on a benchmark derived from 25 articles and 397 entries. Using the curated dataset, an XGBoost surrogate model was trained to predict Young’s modulus from elemental composition, yielding a 10-fold cross-validated R2 of 0.640, an RMSE of 15.977 GPa, and an MAE of 10.985 GPa. The trained surrogate model was then coupled with a genetic algorithm as a constraint-aware candidate-generation procedure to identify candidate HEA compositions within target Young’s modulus ranges. Experimental validation on eight fabricated alloys showed good agreement for samples in well-represented regions of the training space, whereas larger deviations were observed in orientation- and microstructure-sensitive cases, highlighting the applicability boundary of the present composition-only surrogate model. These results provide a practical and transferable workflow for first-pass HEA screening and inverse design while clarifying where additional microstructural descriptors are needed for more reliable property prediction.
•A reflection-enhanced LLM workflow enables literature-to-design HEA screening.•Dual-agent LLM extraction enables structured alloy knowledge acquisition.•XGBoost predicts Young’s modulus from elemental composition for screening.•Genetic algorithm search identifies candidate alloys in target ranges.•Experimental validation supports screening and reveals applicability limits.