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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

An Agentic Framework for Extracting Structured Knowledge from Materials Science Literature

Author(s)

Hasan Muhammad Sayeed, Casey Clark, Taylor Sparks

On-Site Speaker (Planned)

Hasan Muhammad Sayeed

Abstract Scope

We present a multi-agent LLM system for transforming unstructured materials science literature into structured datasets. Leveraging GPT-based models with tool-calling capabilities, the system extracts key information such as compositions, processing conditions, characterization methods, and properties from scientific PDFs. The extraction process is enhanced by a sequence of reasoning agents that dynamically tailor prompts, assess extraction quality, and consolidate results. A simple web interface enables users to upload documents, select models, and export outputs in CSV format for downstream analysis or machine learning workflows. By combining large language models with lightweight agentic reasoning, this framework enables scalable, accurate data extraction from literature while reducing manual effort. This work aims to accelerate materials informatics pipelines by bridging the gap between textual knowledge and structured data formats.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Extraction and Processing, Other

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