从科技文献中挖掘固相合成前驱体的相似性
Similarity of Precursors in Solid-state Synthesis as Text-Mined from Scientific Literature
摘要：
收集和分析固态化学文献中的大量信息可以加速我们对材料合成的理解。然而，一个主要问题是难以确定综述段落中的哪些材料是前体或目标材料。在这项研究中，我们开发了一个两步的化学命名实体识别（CNER）模型，以确定前体和目标，根据周围的材料实体的上下文信息。使用提取的数据，我们进行了荟萃分析，以研究在固态合成的背景下前体之间的相似性和差异。为了量化前体相似性，我们建立了一个替代模型来计算用一种前体替代另一种前体同时保留靶标的可行性。从一个层次聚类的前兆，我们证明了“化学相似性”的前兆可以从文本数据中提取。量化前体的相似性有助于为预测合成模型中的候选反应物提供基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
