Latent Ewald summation for machine learning of long-range interactions
<|reference_start|>Latent Ewald summation for machine learning of long-range interactions: Machine learning interatomic potentials (MLIPs) often neglect long-range interactions, such as electrostatic and dispersion forces. In this work, we introduce a straightforward and efficient method to account for long-range interactions by learning a latent variable from local atomic descriptors and applying an Ewald summation to this variable. We demonstrate that in systems including charged, polar, or apolar molecular dimers, bulk water, and water-vapor interface, standard short-ranged MLIPs can lead to unphysical predictions even when employing message passing. The long-range models effectively eliminate these artifacts, with only about twice the computational cost of short-range MLIPs.<|reference_end|>
@article{cheng2024latent,
    title={Latent Ewald summation for machine learning of long-range interactions},
    author={Bingqing Cheng},
    journal={arXiv preprint arXiv:2408.15165},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15165},
    primaryClass={cs.LG cond-mat.mtrl-sci physics.chem-ph physics.comp-ph}
}