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

作     者：He, Tanjin Sun, Wenhao Huo, Haoyan Kononova, Olga Rong, Ziqin Tshitoyan, Vahe Botari, Tiago Ceder, Gerbrand 

作者机构：Department of Materials Science and Engineering UC Berkeley BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Google LLC 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Institute of Mathematics and Computer Sciences University of São Paulo São Carlos SP13566-590 Brazil 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2020年

核心收录：

主　　题：Chemical analysis 

摘      要：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. Copyright © 2020, The Authors. All rights reserved.