Korea-Japan Joint Symposium: Functional Polymers for Energy Application (English)
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Exploration of Li<sup>+</sup>-conducting polymer composites using data science
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Using data science, we explored better structures and processes of lithium-ion conducting polymers and composites for solid-state batteries. A machine learning model was made to predict conductivities from polymer structures. Its accurate yet fast prediction afforded the virtual screening of new molecular species. We noticed that the composites of aromatic polymers, electron acceptor molecules, and lithium salts could provide good conductivities over 10-4 S/cm at room temperature. Actual chemical experiments validated the prediction. Nevertheless, the composites were sensitive to their preparation processes, which motivated us to develop a process informatics system to record and analyze experimental steps and results. The data-driven approach helped us to understand the essential experimental protocols and mechanisms for higher ionic conductivity.