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arXiv · 2511.22504

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

Nada Alghamdi·Paolo de Angelis·Pietro Asinari·Eliodoro Chiavazzo
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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.

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Nada Alghamdi, Paolo de Angelis, Pietro Asinari, Eliodoro Chiavazzo. 2025-11-27. Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT. https://arxiv.org/abs/2511.22504

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