Cracking the Quantum Scaling Limit with Machine Learned Electron Densities

TL;DR

This paper introduces a machine learning approach using Euclidean Neural Networks to predict electron densities, enabling quantum chemistry calculations on systems with thousands of atoms, surpassing traditional computational limits.

Contribution

The authors develop a novel ML method that predicts electron densities with high accuracy, allowing scalable quantum chemistry calculations for large molecular systems.

Findings

ML model predicts electron densities with high fidelity

Breaks traditional quantum scaling limits

Enables accurate calculations on systems with thousands of atoms

Abstract

A long-standing goal of science is to accurately solve the Schr\"odinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy.