A High-Speed Screening Method by Combining a High-Throughput Method and a Machine-Learning Algorithm for Developing Novel Organic Electrolytes in Rechargeable Batteries

Recent attention to the development of high-voltage lithium-ion batteries has been growing for electric vehicles applications. The electrochemical stability, i.e., redox potentials, of organic compounds is an important property for designing novel electrolytes. However, the redox potentials have not been generally provided in existing large-sized organic compounds databases and therefore the development of novel electrolytes based on them has been restricted. In this study, we suggest fast computational methods for redox potentials to screen out organic electrolytes from the compounds database and to build a redox potentials map on the database. We also show some screening results in cyclic organic molecules and analyze the relation between redox potentials and molecular structures.