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The synthesis process is essential for achieving computational experimentdesign in the field of inorganic materials chemistry. In this work, we presenta novel corpus of the synthesis process for all-solid-state batteries and anautomated machine reading system for extracting the synthesis processes buriedin the scientific literature. We define the representation of the synthesisprocesses using flow graphs, and create a corpus from the experimental sectionsof 243 papers. The automated machine-reading system is developed by a deeplearning-based sequence tagger and simple heuristic rule-based relationextractor. Our experimental results demonstrate that the sequence tagger withthe optimal setting can detect the entities with a macro-averaged F1 score of0.826, while the rule-based relation extractor can achieve high performancewith a macro-averaged F1 score of 0.887.

Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature