Automated Materials Data and Knowledge Extraction using Machine Learning and Large Language Models

原文：Automated Materials Data and Knowledge Extraction using Machine Learning and Large Language Models

作者：Aravindan Kamatchi Sundaram, B. Pabitramohan Prusty, Rohit Batra

来源：arXiv cs.AI（人工智能）

正文

Condensed Matter > Materials Science

arXiv:2610.29895v1 (cond-mat)

[Submitted on 21 Oct 2026]

Title:Automated Materials Data and Knowledge Extraction using Machine Learning and Large Language Models

Authors:Aravindan Kamatchi Sundaram, B. Pabitramohan Prusty, Rohit Batra

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Abstract:Over the past decade, substantial efforts have been devoted to developing machine learning (ML) methods for extracting materials data and, where possible, materials knowledge directly from the scientific literature. This review traces the evolution of these methods from heuristic, rule-based pipelines through natural language processing (NLP) models and transformer-based encoders to, lastly, large language models (LLMs) and agentic systems capable of extracting materials information across multiple modalities, including text, tables, and figures. Beside construction of large, queryable materials databases and knowledge graphs that support downstream materials discovery, we discuss how these advances have enabled new capabilities, such as, generic question answering across materials science, AI co-scientists and chatbots for research assistance, and closed-loop optimization and discovery when integrated with autonomous laboratories. We critically assess the datasets generated using these approaches, their extraction accuracy and computational costs, and the respective strengths and limitations of different methodologies, and even provide practical guidelines for developing more reliable and scalable materials data extraction pipelines. Finally, we identify key open challenges, including the extraction of full materials structure-property-processing-performance tetrahedron at the sample level, the scarcity of reliable benchmark datasets for systematic evaluation, and the resolution of non-standard materials terminology across different document modalities.

Comments:

25 pages, 5 figures

Subjects:

Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI)

Cite as:

arXiv:2610.29895 [cond-mat.mtrl-sci]

(or

arXiv:2610.29895v1 [cond-mat.mtrl-sci] for this version)

https://doi.org/10.48550/arXiv.2610.29895

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arXiv-issued DOI via DataCite (pending registration)

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cond-mat.mtrl-sci · 人工智能

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