Dataset of Solution-based Inorganic Materials Synthesis Procedures Extracted from the Scientific Literature

In this work, we applied advanced machine learning and natural language processing techniques to construct a dataset of 35,675 solution-based synthesis procedures extracted from the scientific literature. Each procedure contains essential synthesis information including the precursors and target materials, their quantities, and the synthesis actions and corresponding attributes. Every procedure is also augmented with the reaction formula. Through this work, we are making freely available the first large dataset of solution-based inorganic materials synthesis procedures.本研究采用先进机器学习与自然语言处理技术，构建了一套从科学文献中提取的、包含35675条液相合成流程的数据集。每条流程均涵盖核心合成信息，包括前驱体与目标材料、二者的用量，以及合成操作与对应属性。每条流程还补充了反应方程式。本研究公开了全球首个大规模液相无机材料合成流程数据集，供所有人免费获取。