Download all files (2.93MB)

コレクション

引用

説明:

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.

データの性質:

権利情報:

キーワード: molecules, Li-ion battery, quantum chemistry calculations, materials informatics, Gaussian09, organic solvents

刊行年月日: 2018-06-14

出版者: Royal Society of Chemistry

掲載誌:

研究助成金:

原稿種別: 査読前原稿 (Author's original)

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

公開URL: https://doi.org/10.1039/c7cp08280k

関連資料:

その他の識別子:

連絡先:

更新時刻: 2024-02-08 17:54:30 +0900

MDRでの公開時刻: 2021-08-19 22:30:05 +0900