Cited by 244Machine Learning in Materials ScienceAdvanced Battery Materials and TechnologiesAdvancements in Battery Materials

Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms

Fujimura, Koji, Seko, Atsuto, Koyama, Yukinori, Kuwabara, Akihide, Kishida, Ippei, Shitara, Kazuki, Fisher, Craig A. J., Moriwake, Hiroki +1 more·2013·Advanced Energy Materials

Abstract

A method for efficiently screening a wide compositional and structural phase space of LISICON-type superionic conductors is presented that utilizes a machine-learning technique for combining theoretical and experimental datasets. By iteratively performing systematic sets of first-principles calculations and focused experiments, it is shown how the materials design process can be greatly accelerated, suggesting potentially superior candidate lithium superionic conductors.

Integrity signals

Registry

Crossref · 10.1002/aenm.201300060

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41 of 41 references verified0 citation errors · 0 not checkable

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  • Other work seeking to improve battery storage technologies has combined first-principles physics calculations with support-vector regression to design conducting solids for lithium-ion batteries.

    “Other work seeking to improve battery storage technologies has combined first-principles physics calculations with support-vector regression to design conducting solids for lithium-ion batteries [257].”

    From Tackling climate change with machine learning (2023) · cites this paper as [257]

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Authors
Fujimura, K., Seko, A., Koyama, Y., Kuwabara, A., Kishida, I., Shitara, K., Fisher, C. A. J., Moriwake, H., & Tanaka, I.
Affiliations
  • Kyoto University (JP)
  • Japan Fine Ceramics Center (JP)
  • Osaka City University (JP)
Corresponding author
Tanaka, Isao
Venue
Advanced Energy Materials
Publisher
Wiley
Published
2013-04-05
Volume
3
Issue
8
Pages
980-985
ISSN
  • 1614-6832 (ISSN-L)
  • 1614-6840
OpenAlex
W2105284227
Cited by
244
References
41
Citations by year
2026: 20 · 2025: 19 · 2024: 21 · 2023: 19 · 2022: 31 · 2021: 36 · 2020: 25 · 2019: 19 · 2018: 18 · 2017: 11 · 2016: 7 · 2015: 11 · 2014: 6 · 2013: 1
FWCI
3.76
Type
journal article
Language
en
Open access
closed
Locations
Advanced Energy Materials (publishedVersion)
Funders
  • Ministry of Education, Culture, Sports, Science and Technology
  • Japan Society for the Promotion of Science (23246111, 24656370, 23686090)
Topics
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Battery Materials and Technologies
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advancements in Battery Materials
Keywords
lithium superionic conductors, materials design, machine learning, first-principles calculations, lithium-ion batteries

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