In silico Design, Virtual Screening and Synthesis of Novel Electrolytic Solvents

We report the building, validation and release of QSPR (Quantitative Structure Property Relationship) models aiming to guide the design of new solvents for the next generation of Li-ion batteries. The dataset compiled from the literature included oxidation potentials (E_ox ), specific ionic conductivities (κ), melting points (T_m ) and boiling points (T_b ) for 103 electrolytes. Each of the resulting consensus models assembled 9-19 individual Support Vector Machine models built on different sets of ISIDA fragment descriptors.(1) They were implemented in the ISIDA/Predictor software. Developed models were used to screen a virtual library of 9965 esters and sulfones. The most promising compounds prioritized according to theoretically estimated properties were synthesized and experimentally tested.