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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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DOITrouver le DOI : https://doi.org/10.1039/D5DD00441A
AuteurRechercher : 1Identifiant ORCID : https://orcid.org/0000-0003-1074-7805; Rechercher : 1, 2Identifiant ORCID : https://orcid.org/0009-0003-7167-4731; Rechercher : 1, 3Identifiant ORCID : https://orcid.org/0000-0002-5672-5700; Rechercher : 4, 5; Rechercher : 1, 6; Rechercher : 7Identifiant ORCID : https://orcid.org/0000-0001-7870-9448; Rechercher : 7Identifiant ORCID : https://orcid.org/0000-0001-8619-9441; Rechercher : 7; Rechercher : 2Identifiant ORCID : https://orcid.org/0000-0003-0865-8096; Rechercher : 1, 3Identifiant ORCID : https://orcid.org/0000-0002-9322-3515; Rechercher : 7Identifiant ORCID : https://orcid.org/0000-0002-7663-2421; Rechercher : 1, 3Identifiant ORCID : https://orcid.org/0000-0002-5473-4507; Rechercher : 5Identifiant ORCID : https://orcid.org/0000-0001-9300-6898
Affiliation
  1. Mila, Montréal, Canada
  2. Université McGill
  3. Université de Montréal
  4. Université d'Ottawa
  5. Conseil national de recherches Canada. Quantique et nanotechnologies
  6. Technical University of Kenya
  7. Conseil national de recherches Canada. Technologies numériques
Bailleur de fondsRechercher : Institut de valorisation des données; Rechercher : Canada First Research Excellence Fund
FormatTexte, Article
Résumé
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.
Date de publication
Maison d’éditionRoyal Society of Chemistry
Licence
Dans
  • Digital Discovery 5, nº 2 (16 janvier 2026) : 910–918.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement4dcb4c14-e9e4-4dbd-af9e-bb308e63c213
Enregistrement créé2026-02-18
Enregistrement modifié2026-03-05

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