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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Process-aware Extraction of Composition–processing–property Records from Materials Literature Using Large Language Models
Author(s) Haoyuan Sun
On-Site Speaker (Planned) Haoyuan Sun
Abstract Scope Extracting structured composition-processing-property (CPP) data from materials literature remains challenging, especially for processing extraction, which requires reconstruction of sequential histories and matching of multiple routes. Using steel literature as a case study, we present a process-aware extraction method empowered by large language models. The method retains entity extraction of compositions, properties, and test conditions, while reconstructing processing routes as ordered stepwise histories. A unified material tuple links extracted entities with corresponding processing routes to construct complete sample-resolved records. By combining supervised fine-tuning, task-specific prompting, and text-evidence grounding, the method improves the F1 score of sequential processing extraction from 35.6% to 93.7%, while maintaining F1 scores above 98% for entity extraction. Designed semantic filtering sheme further reduces hallucination by extracting experimentally relevant text, tables, and figure captions from full text articles. With minimal prompt adaptation, the method transfers to refractory high entropy alloys, demonstrating transferable sequential extraction capability beyond steel literature.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Machine Learning, Other

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