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Identification of Potential Solid-State Li-Ion Conductors with Semi-Supervised Learning

Laskowski, Forrest A. L. and McHaffie, Daniel B. and See, Kimberly A. (2022) Identification of Potential Solid-State Li-Ion Conductors with Semi-Supervised Learning. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220711-653076000

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Abstract

Despite ongoing efforts to identify high-performance electrolytes for solid-state Li-ion batteries, thousands of prospective Li-containing structures remain unexplored. Here, we employ a semi-supervised learning approach to expedite identification of ionic conductors. We screen 180 unique descriptor representations and use agglomerative clustering to cluster ~26,000 Li-containing structures. The clusters are then labeled with experimental ionic conductivity data to assess the fitness of the descriptors. By inspecting clusters containing the highest conductivity labels, we identify 212 promising structures that are further screened using bond valence site energy and nudged elastic band calculations. Li3BS3 is identified as a potential high-conductivity material and selected for experimental characterization. With sufficient defect engineering, we show that Li₃BS₃ is a superionic conductor with room temperature ionic conductivity greater than 1 mS cm⁻¹. While the semi-supervised method shows promise for identification of superionic conductors, the results illustrate a continued need for descriptors that explicitly encode for defects.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.26434/chemrxiv-2022-2m3qbDOIDiscussion Paper
https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/62c76c4e6383267b0044a272/original/supporting-information-for-identification-of-potential-solid-state-li-ion-conductors-with-semi-supervised-learning.pdfPublisherSupporting Information
https://github.com/FALL-ML/materialsdiscoveryRelated ItemSemi-supervised material discovery repository
ORCID:
AuthorORCID
Laskowski, Forrest A. L.0000-0001-8909-483X
See, Kimberly A.0000-0002-0133-9693
Additional Information:The content is available under CC BY NC ND 4.0 License. F.A.L.L acknowledges the support of the Arnold and Mabel Beckman Foundation via a 2020 Arnold O. Beckman Postdoctoral Fellowship in Chemical Sciences. F.A.L.L would also like to thank Andrew J. Martinolich for his guidance and insightful scientific input. The NEB computations presented here were conducted in the Resnick High Performance Computing Center, a facility supported by Resnick Sustainability Institute at the California Institute of Technology. Author Contributions. The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. The authors declare no competing financial interest. Data Availability. The data that support the findings of this study are available from the corresponding author upon reasonable request. The author(s) have declared they have no conflict of interest with regard to this content.
Group:Resnick Sustainability Institute
Funders:
Funding AgencyGrant Number
Arnold and Mabel Beckman FoundationUNSPECIFIED
Resnick Sustainability InstituteUNSPECIFIED
Subject Keywords:solid state electrolytes; semi-supervised learning; li₃bs₃; superionic conductor
DOI:10.26434/chemrxiv-2022-2m3qb
Record Number:CaltechAUTHORS:20220711-653076000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220711-653076000
Official Citation:Laskowski FAL, McHaffie DB, See KA. Identification of Potential Solid-State Li-Ion Conductors with Semi-Supervised Learning. ChemRxiv. 2022. doi:10.26434/chemrxiv-2022-2m3qb
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115471
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:12 Jul 2022 14:10
Last Modified:12 Jul 2022 14:10

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