@inproceedings{
koker2023higher,
title={Higher Order Equivariant Graph Neural Networks for Charge Density Prediction},
author={Teddy Koker and Keegan Quigley and Lin Li},
booktitle={NeurIPS 2023 AI for Science Workshop},
year={2023},
url={https://openreview.net/forum?id=Ap4rSgB3O1}
}
The calculation of electron density distribution in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge in the field of material science. This work introduces ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. Unlike existing methods, ChargE3Net achieves equivariance through the use of higher-order tensor representations, and directly predicts the charge density at a set of desired locations. We demonstrate the effectiveness of ChargE3Net on large and diverse sets of molecules and materials, where it achieves state-of-the-art performance over existing methods, and scales to larger systems than what is feasible to compute with density functional theory. Through additional experimentation, we demonstrate the effect of introducing higher-order equivariant representations, and why they yield performance improvements in the charge density prediction setting.
Higher Order Equivariant Graph Neural Networks for Charge Density Prediction
Teddy Koker Keegan Quigley Lin Li