Extracting Chemical Information from Scientific Literature Using Text Mining: Building an Ionic Conductivity Database for Solid-State Electrolytes

Abstract

Recently, as the demand for electric vehicles has rapidly grown, concerns regarding the safety of liquid electrolytes used as battery materials have increased. Rechargeable batteries made of liquid electrolytes pose a risk of fire and may explode due to the decomposition reaction of the electrolyte. Accordingly, interest in solid-state electrolytes (SSEs), which have greater stability than liquid electrolytes, is increasing, and research into finding stable SSEs with high ionic conductivity is actively being conducted. Consequently, it is essential to obtain a large amount of material data to explore new SSEs. However, the data collection process is highly repetitive and time-consuming. Therefore, the goal of this study is to automatically extract the ionic conductivities of SSEs from published literature using text-mining algorithms and use this information to construct a materials database. The extraction procedure includes document processing, natural language preprocessing, phase parsing, relation extraction, and data post processing. For performance verification, the ionic conductivities are extracted from 38 studies, and the accuracy of the proposed model is confirmed by comparing extracted conductivities with the actual ones. In previous research, 93% of battery-related records were unable to distinguish between ionic and electrical conductivities. However, by applying the proposed model, the proportion of undistinguished records was successfully reduced from 93 to 24.3%. Finally, the ionic conductivity database was constructed by extracting the ionic conductivity from 3258 papers, and the battery database was reconstructed by adding eight pieces of representative structural information.