A Data-driven Framework to Accelerate the Discovery of Hybrid Cathode Materials for Metal-based Batteries
Ahmed H. Biby, Benjamin S. Rich, and Charles B. Musgrave

TL;DR
This paper introduces a data-driven, chemistry-agnostic framework for discovering hybrid cathode materials in metal-based batteries, successfully identifying a high-energy-density candidate surpassing existing materials.
Contribution
The authors present a novel inverse design framework that systematically explores hybrid cathode material space, evaluates stability, and identifies promising candidates for high-performance batteries.
Findings
Identified LiCr₄GaS₈-Li₂S as a promising hybrid cathode with 1,424 Wh/kg energy density.
Framework effectively explores vast material space for targeted battery design objectives.
Discovered stable, high-energy hybrid cathodes with minimal volume change and enhanced lifespan potential.
Abstract
Selecting materials for hybrid cathodes for batteries, which combine intercalation and conversion materials, has gained interest due to their unique synergistic properties, which are not achievable by homogeneous materials. Here, we present a data-driven, chemistry-agnostic, inverse material design framework to discover hybrid cathode materials (HCMs) for metal-based batteries. This framework systematically explores the material space for any working ion, evaluates the stability of candidates, and identifies the growth modes and adsorption of components for a stable hybrid cathode. To demonstrate the framework's application, we conducted a case study aimed at discovering HCMs with an average gravimetric energy density that exceeds the widely used high-energy NMC333 cathode material. The framework identified LiCrGaS-LiS as a promising HCM, achieving an average energy density…
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Taxonomy
TopicsRecycling and Waste Management Techniques · Machine Learning in Materials Science · Electron and X-Ray Spectroscopy Techniques
