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Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents

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Abstract

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. Descriptors include both ion and solvent properties, such as the radius of metal ions or the atomic charge of solvent molecules. Our results clearly indicate that the ionic radius and atomic charge of the oxygen atom that is connected to the metal ion are the most important descriptors. Good prediction accuracy for Ecoord of 0.127 eV was obtained using ES-LiR, meaning that we can predict Ecoord for any alkali ion without performing quantum chemistry calculations for ion–solvent pairs. Further improvement in the prediction accuracy was made by applying the exhaustive search with Gaussian process, which yields 0.016 eV for the prediction accuracy of Ecoord.

Graphical abstract: Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents

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Publication details

The article was received on 30 Jun 2019, accepted on 17 Nov 2019 and first published on 18 Nov 2019


Article type: Paper
DOI: 10.1039/C9CP03679B
Phys. Chem. Chem. Phys., 2019, Advance Article

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    Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents

    A. Ishikawa, K. Sodeyama, Y. Igarashi, T. Nakayama, Y. Tateyama and M. Okada, Phys. Chem. Chem. Phys., 2019, Advance Article , DOI: 10.1039/C9CP03679B

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