Liquid electrolyte informatics using an exhaustive search with linear regression

MDR Open Deposited

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.

First published at
Resource type
Data origin
  • organic solvents
Date published
  • 14/06/2018
Rights statement
Licensed Date
  • 07/04/2020
Manuscript type
  • Version of record (Published version)
Last modified
  • 30/06/2021
Other Date
Additional metadata