Premium
In silico Design, Virtual Screening and Synthesis of Novel Electrolytic Solvents
Author(s) -
Marcou G.,
Flamme B.,
Beck G.,
Chagnes A.,
Mokshyna O.,
Horvath D.,
Varnek A.
Publication year - 2019
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201900014
Subject(s) - quantitative structure–activity relationship , virtual screening , in silico , electrolyte , computer science , boiling point , ionic bonding , ionic liquid , software , chemistry , combinatorial chemistry , data mining , ion , computational chemistry , machine learning , programming language , molecular dynamics , organic chemistry , biochemistry , electrode , gene , catalysis
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.