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.
@article{arxiv.2511.22504,
title = {Screening novel cathode materials from the Energy-GNoME database using MACE machine learning force field and DFT},
author = {Nada Alghamdi and Paolo de Angelis and Pietro Asinari and Eliodoro Chiavazzo},
journal= {arXiv preprint arXiv:2511.22504},
year = {2026}
}