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.

翻译：合成程序的开发仍然是材料发现中的一项基础性挑战，其程序性知识分散在数十年的科学文献中，以非结构化格式存在，难以进行系统性分析。本文提出了一种多模态工具箱，利用大型语言模型（LLMs）和视觉语言模型（VLMs）自动从材料科学出版物（涵盖文本和图表）中提取并组织合成程序与性能数据。我们整理了8.1万篇开放获取论文，构建了LeMat-Synth（v1.0）数据集：该数据集包含涵盖35种合成方法和16种材料类别的合成程序，并按照材料科学特定本体进行结构化。通过结合专家标注和可扩展的LLM-as-a-judge框架，我们在2.5千个合成程序子集上对提取质量进行了严格评估。除数据集外，我们还发布了一个模块化、开源的软件库，旨在支持社区驱动地扩展到新语料库和合成领域。总体而言，这项工作提供了一个可扩展的基础设施，将非结构化文献转化为机器可读信息，为合成程序的预测建模以及合成-结构-性能关系建模奠定了基础。