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Open Access Publications from the University of California

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

  • Author(s): He, T
  • Sun, W
  • Huo, H
  • Kononova, O
  • Rong, Z
  • Tshitoyan, V
  • Botari, T
  • Ceder, G
  • et al.

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 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 the "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.

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