Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks
<|reference_start|>Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks: Recent SO(3)-equivariant models embedded a molecule as a set of single atoms fixed in the three-dimensional space, which is analogous to a ball-and-stick view. This perspective provides a concise view of atom arrangements, however, the surrounding electron density cannot be represented and its polarization effects may be underestimated. To overcome this limitation, we propose \textit{Neural Polarization}, a novel method extending equivariant network by embedding each atom as a pair of fixed and moving points. Motivated by density functional theory, Neural Polarization represents molecules as a space-filling view which includes an electron density, in contrast with a ball-and-stick view. Neural Polarization can flexibly be applied to most type of existing equivariant models. We showed that Neural Polarization can improve prediction performances of existing models over a wide range of targets. Finally, we verified that our method can improve the expressiveness and equivariance in terms of mathematical aspects.<|reference_end|>
@article{kwak2024neural,
    title={Neural Polarization: Toward Electron Density for Molecules by Extending
  Equivariant Networks},
    author={Bumju Kwak and Jeonghee Jo},
    journal={arXiv preprint arXiv:2406.00441},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.00441},
    primaryClass={physics.chem-ph cs.AI cs.LG}
}