The synthesis process is essential for achieving computational experiment design in the field of inorganic materials chemistry. In this work, we present a novel corpus of the synthesis process for all-solid-state batteries and an automated machine reading system for extracting the synthesis processes buried in the scientific literature. We define the representation of the synthesis processes using flow graphs, and create a corpus from the experimental sections of 243 papers. The automated machine-reading system is developed by a deep learning-based sequence tagger and simple heuristic rule-based relation extractor. Our experimental results demonstrate that the sequence tagger with the optimal setting can detect the entities with a macro-averaged F1 score of 0.826, while the rule-based relation extractor can achieve high performance with a macro-averaged F1 score of 0.887.

本文提出了一种基于深度学习的序列标记器和简单启发式规则的关系抽取器的自动化机器阅读系统，通过流程图定义合成过程的表示，并从243篇论文实验部分创建一个关于全固态电池的合成过程的新语料库。我们的实验结果表明，序列标记器可以检测实体，宏平均F1分数为0.826，同时基于规则的关系抽取器的宏平均F1分数为0.887。

从科技文献中注释并提取全固态电池的合成过程