MGNN: Moment Graph Neural Network for Universal Molecular Potentials

TL;DR

MGNN is a rotation-invariant graph neural network that leverages moment representations of 3D molecular graphs, achieving state-of-the-art results in molecular property prediction and dynamic simulations.

Contribution

Introduces MGNN, a novel rotation-invariant message passing neural network utilizing moment representations for improved molecular modeling.

Findings

Achieves state-of-the-art performance on QM9 and MD17 datasets.

Accurately predicts properties of amorphous electrolytes.

Enhances molecular spectra simulation workflows.

Abstract

The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neural networks has marked a transformative era in graph-based learning, particularly in the realm of predicting chemical properties and expediting molecular dynamics studies. We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. MGNN demonstrates new state-of-the-art performance over contemporary methods on benchmark datasets such as QM9 and the revised MD17. The prowess of MGNN also extends to dynamic simulations, accurately predicting the structural and kinetic properties of complex systems such as amorphous electrolytes, with results that closely align with those from ab-initio simulations. The application of MGNN to the simulation of molecular spectra exemplifies its potential to significantly enhance the computational workflow, offering a promising alternative to traditional electronic structure methods