We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We performed quantum chemistry calculations on the coordination energy (Ecoord) of five alkali metal ions (Li, Na, K, Rb, and Cs) to electrolyte solvent, which is intimately related to ion transfer at the electrolyte/electrode interface. Three regression methods, namely, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), were employed to find the relationship between Ecoord and descriptors.
ISHIKAWA, Atsushi; SODEYAMA, Keitaro; IGARASHI, Yasuhiko; NAKAYAMA, Tomofumi; TATEYAMA, Yoshitaka; OKADA, Masato. "Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents". Physical Chemistry Chemical Physics. 21, no. . . (2019): https://doi.org/10.1039/C9CP03679B
quantum chemistry, electrolyte, battery,
Curator: shurafa