Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature
This work addresses the need for automated extraction of synthesis processes in inorganic materials chemistry, which is incremental as it applies existing methods to a new domain-specific corpus.
The researchers tackled the problem of extracting synthesis processes for all-solid-state batteries from scientific literature by creating a corpus from 243 papers and developing an automated machine reading system, achieving macro-averaged F1 scores of 0.826 for entity detection and 0.887 for relation extraction.
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.