2020/02/17 by Fusataka Kuniyoshi, Kohei Makino, Kuniyoshi, Fusataka +5 · 2 citations
Materials Science · Computer Science · #Machine Learning in Materials Science #Topic Modeling #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2002.07339
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.