Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model

作者：Sanghoon Lee, Kevin Cruse, Viktoriia Baibakova, Gerbrand Ceder, Anubhav Jain · 发表于：Scientific Data · 年份：2025 · DOI：10.1038/s41597-025-06222-y · 被引用次数：3 · 研究领域：Machine Learning in Materials Science、Thermal Expansion and Ionic Conductivity、Inorganic Chemistry and Materials

Solid-state synthesis is widely used to obtain various inorganic materials, such as battery materials and bulk thermoelectrics. Despite its prevalence, the process remains challenging due to the lack of a general theory and well-understood underlying reaction mechanisms. While prior works have successfully extracted structured datasets from literature, they often neglect product phase purity or yield. In this work, we construct a solid-state synthesis dataset consisting of 80,806 syntheses extracted with a large language model (LLM), including 18,869 reactions with impurity phase(s). Our dataset not only validates expected thermodynamic trends for impurity phase formation but also identifies challenging cases where impurity phases emerge even when the target phase is significantly more stable.