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Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast to solid materials, disordered liquid solution properties have been less studied by data-driven information techniques. Here, we examined the estimation accuracy and efficiency of three information techniques, multiple linear regression (MLR), least absolute shrinkage and selection operator (LASSO), and exhaustive search with linear regression (ES-LiR), by using coordination energy and melting point as test liquid properties. We then confirmed that ES-LiR gives the most accurate estimation among the techniques. We also found that ES-LiR can provide the relationship between the “prediction accuracy” and “calculation cost” of the properties via a weight diagram of descriptors. This technique makes it possible to choose the balance of the “accuracy” and “cost” when the search of a huge amount of new materials was carried out.

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Keyword: molecules, Li-ion battery, quantum chemistry calculations, materials informatics, Gaussian09, organic solvents

Date published: 2018-06-14

Publisher: Royal Society of Chemistry

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Manuscript type: Author's version (Submitted manuscript)

MDR DOI: https://doi.org/10.48505/nims.1436

First published URL: https://doi.org/10.1039/c7cp08280k

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Updated at: 2024-02-08 17:54:30 +0900

Published on MDR: 2021-08-19 22:30:05 +0900