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

TL;DR

OBELiX is a curated dataset of around 600 lithium solid-state electrolyte materials with experimental ionic conductivities, designed to accelerate machine learning-driven discovery of high-conductivity materials.

Contribution

The paper introduces OBELiX, a comprehensive, curated database of ionic conductivities and crystal structures for lithium solid electrolytes, enabling ML applications in materials discovery.

Findings

Benchmarking of seven ML models on ionic conductivity prediction.

Dataset includes detailed structural and compositional data.

Analysis of dataset statistics and features.

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 database of $\sim$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 $\sim$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.