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

https://mdr.nims.go.jp/datasets/dba43e9b-fd4a-466f-8e2e-27cec40e9daa

## File

- [PCCP_10.1039_c9cp03679b.csv](https://mdr.nims.go.jp/filesets/3a460c6f-cb4d-4e0e-a0a2-b8cc3df92122/download) ([Detail](https://mdr.nims.go.jp/filesets/3a460c6f-cb4d-4e0e-a0a2-b8cc3df92122.md))
- [article.pdf](https://mdr.nims.go.jp/filesets/0158c2c0-7f38-48cc-93d8-3c3ad82f6e6d/download) ([Detail](https://mdr.nims.go.jp/filesets/0158c2c0-7f38-48cc-93d8-3c3ad82f6e6d.md))

## Id

dba43e9b-fd4a-466f-8e2e-27cec40e9daa

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-10-13T13:30:19.510529Z

## Updated at

2024-01-05T13:13:39.521463Z

## Published at

2021-11-16T10:31:33.588559Z

## Doi

https://doi.org/10.48505/nims.3057

## First published url

https://doi.org/10.1039/C9CP03679B

## Date published

2019-11-18

## Recorded date published

2019-12-11

## Resource type

dataset

## Manuscript type

authors_original

## Collection



## Title

- title: Machine learning prediction of coordination energies for alkali group elements
    in battery electrolyte solvents
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: en

## Creator

- name: ISHIKAWA, Atsushi
  role: author
  orcid: https://orcid.org/0000-0001-6908-831X
- name: SODEYAMA, Keitaro
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
- name: IGARASHI, Yasuhiko
  role: author
  orcid: https://orcid.org/0000-0003-1042-6657
- name: NAKAYAMA, Tomofumi
  role: author
  orcid: https://orcid.org/0000-0003-1240-3571
- name: TATEYAMA, Yoshitaka
  role: author
  orcid: https://orcid.org/0000-0002-5532-6134
- name: OKADA, Masato
  role: author
  orcid: https://orcid.org/0000-0002-9040-8784

## Contact agent



## Publisher

organization: Royal Society of Chemistry

## Managing organization



## Keyword

- subject: quantum chemistry
  schema: not_defined
- subject: electrolyte
  schema: not_defined
- subject: battery
  schema: not_defined

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## Fileset

- id: 3a460c6f-cb4d-4e0e-a0a2-b8cc3df92122
  filename: PCCP_10.1039_c9cp03679b.csv
  content_type: text/csv
  size: 39117
  md5: 0c25dbe9f0ca18711299d331777699b5
- id: '0158c2c0-7f38-48cc-93d8-3c3ad82f6e6d'
  filename: article.pdf
  content_type: application/pdf
  size: 2444994
  md5: 9b15904e021fb53b427456a5792c74fb

## Thumbnail

fileset_id: 3a460c6f-cb4d-4e0e-a0a2-b8cc3df92122
filename: PCCP_10.1039_c9cp03679b.csv