E3STO: Orbital Inspired SE(3)-Equivariant Molecular Representation for Electron Density Prediction

TL;DR

This paper introduces E3STO, an SE(3)-equivariant neural network inspired by Slater-Type Orbitals, achieving state-of-the-art accuracy in predicting molecular electron densities efficiently, which advances quantum chemistry applications.

Contribution

The paper presents a novel orbital-inspired SE(3)-equivariant architecture for molecular electron density prediction, offering a new functional form for learned orbital-like representations.

Findings

Achieves 30-70% improvement in electron density prediction accuracy.

Outperforms existing methods on Molecular Dynamics datasets.

Provides an efficient alternative to expensive DFT calculations.

Abstract

Electron density prediction stands as a cornerstone challenge in molecular systems, pivotal for various applications such as understanding molecular interactions and conducting precise quantum mechanical calculations. However, the scaling of density functional theory (DFT) calculations is prohibitively expensive. Machine learning methods provide an alternative, offering efficiency and accuracy. We introduce a novel SE(3)-equivariant architecture, drawing inspiration from Slater-Type Orbitals (STO), to learn representations of molecular electronic structures. Our approach offers an alternative functional form for learned orbital-like molecular representation. We showcase the effectiveness of our method by achieving SOTA prediction accuracy of molecular electron density with 30-70\% improvement over other work on Molecular Dynamics data.