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

Arjun Sundaram, B. Pabitramohan Prusty, Rohit Batra · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.29895 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

過去10年間にわたって、機械学習(ML)方法を開発して材料データを抽出し、可能な限り、材料知識を科学文献から直接抽出するためのデータ抽出のための実質的な努力が行われました。このレビューは、これらの方法の進化を、ヘウリスティック、ルールベースのパイプラインから、自然言語処理(NLP)モデルやトランスフォーマーベースのエンコーダを経て、最後に、大規模な言語モデル(LLMs)と、テキスト、テーブル、および数字を含む複数の方法で材料情報を抽出する能力のあるエージェントシステムに追跡します。大規模で検索可能な材料データベースと知識グラフの構築に加えて、我々は、これらの進歩がどのように、

Machine translation (Meta M2M100); the English original follows.

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.

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Topics: Computer science, Terminology, Artificial intelligence, Machine learning, Knowledge extraction, Information extraction

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