OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes

• URL: http://arxiv.org/abs/2502.14234v1
• Date: Thu, 20 Feb 2025 03:59:35 GMT
• Title: OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
• Authors: Félix Therrien, Jamal Abou Haibeh, Divya Sharma, Rhiannon Hendley, Alex Hernández-García, Sun Sun, Alain Tchagang, Jiang Su, Samuel Huberman, Yoshua Bengio, Hongyu Guo, Homin Shin,
• Abstract summary: Solid-state electrolyte batteries are expected to replace liquid electrolyte lithium-ion batteries in the near future. Finding highly ion-conductive materials is time-consuming and resource-intensive. OBELiX is a database of synthesized solid electrolyte materials and experimentally measured room temperature ionic conductivities.
• Score: 44.16223940507546
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• 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 their 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 domain-expert-curated database of $\sim$600 synthesized solid electrolyte materials and their experimentally measured room temperature ionic conductivities gathered from literature. 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 that 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.

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