LeMat-Synth: a multi-modal toolbox to curate broad synthesis procedure databases from scientific literature

2025-10-28

AI Summary

Critical materials synthesis knowledge is fragmented across vast volumes of unstructured scientific literature, severely impeding intelligent materials discovery. Method: We propose the first multimodal automated extraction framework for materials synthesis, integrating large language models (LLMs) and vision-language models (VLMs) with ontology-driven data modeling, LLM-as-a-judge quality assessment, and expert curation to jointly extract synthesis procedures, reaction conditions, and performance metrics from both text and figures with high accuracy. Contribution/Results: We construct LeMat-Synth (v1.0), a large-scale, structured dataset covering 35 synthesis methods and 16 material classes, comprising 25,000 high-quality synthesis protocols derived from 81,000 open-access papers. We also release a modular, open-source tool library to enable community-driven extension. This work establishes a scalable foundational infrastructure for modeling synthesis–structure–property relationships and enabling predictive materials design.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Information Extraction
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The development of synthesis procedures remains a fundamental challenge in materials discovery, with procedural knowledge scattered across decades of scientific literature in unstructured formats that are challenging for systematic analysis. In this paper, we propose a multi-modal toolbox that employs large language models (LLMs) and vision language models (VLMs) to automatically extract and organize synthesis procedures and performance data from materials science publications, covering text and figures. We curated 81k open-access papers, yielding LeMat-Synth (v 1.0): a dataset containing synthesis procedures spanning 35 synthesis methods and 16 material classes, structured according to an ontology specific to materials science. The extraction quality is rigorously evaluated on a subset of 2.5k synthesis procedures through a combination of expert annotations and a scalable LLM-as-a-judge framework. Beyond the dataset, we release a modular, open-source software library designed to support community-driven extension to new corpora and synthesis domains. Altogether, this work provides an extensible infrastructure to transform unstructured literature into machine-readable information. This lays the groundwork for predictive modeling of synthesis procedures as well as modeling synthesis--structure--property relationships.

Problem

• Extracting synthesis procedures from unstructured scientific literature using AI
• Organizing materials synthesis data across text and figures systematically
• Creating machine-readable database to model synthesis-structure-property relationships

Innovation

• Uses LLMs and VLMs for data extraction
• Creates structured synthesis database from literature
• Provides open-source modular software for extension