most citedComparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries

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cond-mat.mtrl-sci2025

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

Nada Alghamdi, Paolo de Angelis, Pietro Asinari +1

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 m…

cond-mat.mtrl-sci2025★ 1 cited

Comparing fine-tuning strategies of MACE machine learning force field for modeling Li-ion diffusion in LiF for batteries

Nada Alghamdi, Paolo de Angelis, Pietro Asinari +1

Machine learning force fields (MLFFs) are transforming materials science and engineering by enabling the study of complex phenomena, such as those critical to battery operation. In…