Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature

TL;DR

This paper presents a text-mining approach to identify and analyze precursor similarities in solid-state synthesis literature, enabling better understanding and prediction of materials synthesis processes.

Contribution

Developed a two-step chemical named entity recognition model and a hierarchical clustering method to quantify precursor similarities from scientific texts.

Findings

Chemical similarity of precursors can be extracted from literature.

Hierarchical clustering reveals precursor relationships.

Quantifying precursor similarity aids in predictive synthesis modeling.

Abstract

Collecting and analyzing the vast amount of information available in the solid-state chemistry literature may accelerate our understanding of materials synthesis. However, one major problem is the difficulty of identifying which materials from a synthesis paragraph are precursors or are target materials. In this study, we developed a two-step Chemical Named Entity Recognition (CNER) model to identify precursors and targets, based on information from the context around material entities. Using the extracted data, we conducted a meta-analysis to study the similarities and differences between precursors in the context of solid-state synthesis. To quantify precursor similarity, we built a substitution model to calculate the viability of substituting one precursor with another while retaining the target. From a hierarchical clustering of the precursors, we demonstrate that "chemical similarity" of precursors can be extracted from text data. Quantifying the similarity of precursors helps provide a foundation for suggesting candidate reactants in a predictive synthesis model.