# Liquid electrolyte informatics using an exhaustive search with linear regression

https://mdr.nims.go.jp/datasets/f036da11-e329-46f1-bcb5-498ff5f11169

## File

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## Id

f036da11-e329-46f1-bcb5-498ff5f11169

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2021-08-19T06:10:54.210903Z

## Updated at

2024-02-08T08:54:30.463432Z

## Published at

2021-08-19T13:30:05.884452Z

## Doi

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

## First published url

https://doi.org/10.1039/c7cp08280k

## Date published

2018-06-14

## Recorded date published

2018

## Resource type

dataset

## Manuscript type

authors_original

## Collection



## Title

- title: Liquid electrolyte informatics using an exhaustive search with linear regression
  title_type: original
  lang: en

## Description

- description: 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.
  description_type: abstract
  lang: en

## Creator

- name: SODEYAMA, Keitaro
  role: author
  orcid: https://orcid.org/0000-0002-9228-0729
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- 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
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- 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: molecules
  schema: not_defined
- subject: Li-ion battery
  schema: not_defined
- subject: quantum chemistry calculations
  schema: not_defined
- subject: materials informatics
  schema: not_defined
- subject: Gaussian09
  schema: not_defined
- subject: organic solvents
  schema: not_defined

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## Specimen



## Chemical composition



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## Fileset

- id: afc4614c-2709-46c8-a8ff-6240d06789fe
  filename: c7cp08280k.pdf
  content_type: application/pdf
  size: 2308032
  md5: 96554504d986615aa57237f94cc2123f
- id: c0254176-a78b-42d8-9315-15b5a97fca36
  filename: c7cp08280k1.pdf
  content_type: application/pdf
  size: 1088916
  md5: 67a6c63f5b88b7068350dd578428f1e4
- id: b1897b15-b618-4ab0-a453-546ea3c57f47
  filename: gaussian_forUpload.csv
  content_type: text/csv
  size: 6541
  md5: c64436fce43fbf020f9aa78ab9e2c792

## Thumbnail

fileset_id: afc4614c-2709-46c8-a8ff-6240d06789fe
filename: c7cp08280k.pdf