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

2025-02-20

AI Summary

Low ionic conductivity of solid-state electrolytes and scarcity of high-quality experimental data severely hinder machine learning (ML)-driven materials discovery. To address this, we introduce OBELiX—the first expert-curated database of lithium-based solid-state electrolytes—containing room-temperature measured ionic conductivities, chemical compositions, space groups, lattice parameters, and 320 complete CIF crystal structure files for 600 experimentally synthesized materials. OBELiX represents the first standardized integration of high-fidelity conductivity measurements with full crystallographic structural information, employing a strict no-data-leakage strategy for train/test split. Using OBELiX, we benchmark seven ML models—including graph neural networks (GNNs), random forests (RF), and support vector regression (SVR)—and find GNNs achieve the best predictive performance (MAE ≈ 0.35 log(S/cm)). OBELiX fills a critical data gap in ML research on solid-state electrolytes and establishes a reliable foundation for high-throughput screening of next-generation battery materials.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Natural Language Processing: Information Extraction

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• Identify highly ion-conductive materials
• Curate dataset for machine learning
• Benchmark ML models for conductivity prediction

Innovation

• Curated database for solid electrolytes
• Machine learning for ionic conductivity prediction
• Crystallographic information file integration