Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks

• URL: http://arxiv.org/abs/2406.00441v1
• Date: Sat, 1 Jun 2024 13:39:27 GMT
• Title: Neural Polarization: Toward Electron Density for Molecules by Extending Equivariant Networks
• Authors: Bumju Kwak, Jeonghee Jo,
• Abstract summary: We propose textitNeural 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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