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OBELiX: a curated dataset of crystal structures and experimentally measured ionic conductivities for lithium solid-state electrolytes

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DOIResolve DOI: https://doi.org/10.1039/D5DD00441A
AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0003-1074-7805; Search for: 1, 2ORCID identifier: https://orcid.org/0009-0003-7167-4731; Search for: 1, 3ORCID identifier: https://orcid.org/0000-0002-5672-5700; Search for: 4, 5; Search for: 1, 6; Search for: 7ORCID identifier: https://orcid.org/0000-0001-7870-9448; Search for: 7ORCID identifier: https://orcid.org/0000-0001-8619-9441; Search for: 7; Search for: 2ORCID identifier: https://orcid.org/0000-0003-0865-8096; Search for: 1, 3ORCID identifier: https://orcid.org/0000-0002-9322-3515; Search for: 7ORCID identifier: https://orcid.org/0000-0002-7663-2421; Search for: 1, 3ORCID identifier: https://orcid.org/0000-0002-5473-4507; Search for: 5ORCID identifier: https://orcid.org/0000-0001-9300-6898
Affiliation
  1. Mila, Montreal, Canada
  2. McGill University
  3. University of Montreal
  4. University of Ottawa
  5. National Research Council Canada. Quantum and Nanotechnologies
  6. Technical University of Kenya
  7. National Research Council Canada. Digital Technologies
FunderSearch for: National Research Council Canada; Search for: Canada First Research Excellence Fund
FormatText, Article
Abstract
Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future thanks to their higher theoretical energy density and improved safety. However, their adoption is currently hindered by imperfect electrode–electrolyte interfaces and a lower effective ionic conductivity, a quantity that governs charge and discharge rates. Identifying highly ion-conductive materials using conventional theoretical calculations and experimental validation is both time-consuming and resource-intensive. While machine learning holds the promise to expedite this process, relevant ionic conductivity and structural data is scarce. Here, we present OBELiX, a database of ∼600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature and curated by domain experts. Each material is described by their measured composition, space group and lattice parameters. A full-crystal description in the form of a crystallographic information file (CIF) is provided for ∼320 structures for which atomic positions were available. We discuss various statistics and features of the dataset and provide training and testing splits carefully designed to avoid data leakage. Finally, we benchmark seven existing ML models on the task of predicting ionic conductivity and discuss their performance. The goal of this work is to facilitate the use of machine learning for solid-state electrolyte materials discovery.
Publication date
PublisherRoyal Society of Chemistry
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In
  • Digital Discovery 5, no. 2 (16 January 2026): 910–918.
LanguageEnglish
Peer reviewedYes
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Record identifier4dcb4c14-e9e4-4dbd-af9e-bb308e63c213
Record created2026-02-18
Record modified2026-03-05

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