Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT

TL;DR

This study introduces a multi-fidelity screening protocol combining machine learning force fields and DFT to identify promising novel cathode materials for next-generation post-lithium batteries, streamlining the discovery process.

Contribution

It develops and validates a high-throughput screening approach using MACE MLFF models for cathode material discovery, integrating DFT refinements and database comparisons.

Findings

Validated a robust screening protocol for cathode materials.

Identified promising cathode candidates for Na, K, Mg, and Ca batteries.

Compared MACE predictions with existing database figures of merit.

Abstract

The development of new battery materials, particularly novel cathode chemistries, is essential for enabling next generation energy storage technologies. In this work, we employ a multi-fidelity screening protocol combining the Energy-GNoME confident criteria, foundational MACE machine-learning force fields (MLFF), and physically motivated heuristic filters to identify novel intercalation cathodes for post-lithium batteries, namely: Na-, K-, Mg-, and Ca-ion batteries. Foundational MACE models are used to efficiently asses dynamical stability, thermodynamical stability, average voltage, and theoretical specific energy, enabling a rapid screening of candidates. For the most promising cathodes, voltage predictions are refined using DFT+U calculations. This work delivers three key outcomes: i) establishing and validating a robust high-throughput screening approach for cathode materials with foundational MLFF models; ii) suggestions for cathode candidates for the development of next-generation of batteries; iii) a fair comparison between the MACE predictions and the readily available figures of merit reported in the Energy-GNoME database on the examined materials.