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