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Cited by 448Machine Learning in Materials ScienceAdvanced Battery Materials and TechnologiesAdvancements in Battery Materials

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Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

Sendek, Austin D., Yang, Qian, Cubuk, Ekin D., Duerloo, Karel-Alexander N., Cui, Yi, Reed, Evan J.·2017·Energy & Environmental Science
DOI 10.1039/c6ee02697d
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Abstract

We leverage available data and statistical learning methods to perform a holistic screening for promising new solid electrolytes.

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Crossref · 10.1039/c6ee02697d

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  • The authors find 528 promising Li-ion conductors among GNoME discoveries, a 25 times increase compared with the original study.

    “Similarly, following a holistic screening approach with filters such as exclusion of transition metals or by lithium fraction, we find 528 promising Li-ion conductors among GNoME discoveries, a 25 times increase compared with the original study46.”

    From Scaling deep learning for materials discovery (2023) · cites this paper as [46]

  • The estimated number of viable Li-ion conductors reported in the main part of the paper is derived using the methodology in ref. 46 in a high-throughput fashion.

    “The estimated number of viable Li-ion conductors reported in the main part of this paper is derived using the methodology in ref. 46 in a high-throughput fashion.”

    From Scaling deep learning for materials discovery (2023) · cites this paper as [46]

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Cited by

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Metadata

Authors
Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J.
Affiliations
Stanford University (US)
Corresponding author
Reed, Evan J.
ORCID
Venue
Energy & Environmental Science
Publisher
Royal Society of Chemistry (RSC)
Published
2017
Volume
10
Issue
1
Pages
306-320
ISSN
  • 1754-5692 (ISSN-L)
  • 1754-5706
DOI
10.1039/c6ee02697d
OpenAlex
W2559612475
Cited by
448
References
3
Citations by year
2026: 67 · 2025: 53 · 2024: 59 · 2023: 52 · 2022: 54 · 2021: 49 · 2020: 47 · 2019: 28 · 2018: 32 · 2017: 7
FWCI
8.60
Type
journal article
Language
en
Open access
closed
Locations
Energy & Environmental Science (publishedVersion)
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
solid electrolytes, lithium-ion conductors, materials screening, computational materials design, statistical learning, high-throughput screening, ionic conductivity

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