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

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.

References in corpus (23)

• Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons
• Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
• E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials
• Machine Learning of Accurate Energy-Conserving Molecular Force Fields
• By-passing the Kohn-Sham equations with machine learning
• Machine Learning of Molecular Electronic Properties in Chemical Compound Space
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
• SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects
• Prediction model of band-gap for AX binary compounds by combination of density functional theory calculations and machine learning techniques
• MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields
• Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation
• sGDML: Constructing Accurate and Data Efficient Molecular Force Fields Using Machine Learning
• Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
• Cormorant: Covariant Molecular Neural Networks
• Deep Learning and Density Functional Theory
• Machine learning and density functional theory
• Universal Machine Learning for the Response of Atomistic Systems to External Fields
• Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs
• Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
• So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems
• DeepDFT: Neural Message Passing Network for Accurate Charge Density Prediction
• Beyond MD17: the reactive xxMD dataset
• VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification