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

2024/06/01 by Bumju Kwak, Kwak, Bumju, Jeonghee Jo +1
Chemistry · Neuroscience · Materials Science · #Molecular spectroscopy and chirality #Photoreceptor and optogenetics research #Machine Learning in Materials Science

paper · pdf · doi:10.48550/arxiv.2406.00441

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 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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