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

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.