Journal article
Joshua A. Rackers, Lucas Tecot, M. Geiger, T. Smidt
Machine Learning: Science and Technology, 2022
Machine Learning: Science and Technology, 2022
Semantic Scholar
ArXiv
DBLP
DOI
Cite
Cite
APA
Click to copy
Rackers, J. A., Tecot, L., Geiger, M., & Smidt, T. (2022). A recipe for cracking the quantum scaling limit with machine learned electron densities. Machine Learning: Science and Technology.
Chicago/Turabian
Click to copy
Rackers, Joshua A., Lucas Tecot, M. Geiger, and T. Smidt. “A Recipe for Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.” Machine Learning: Science and Technology (2022).
MLA
Click to copy
Rackers, Joshua A., et al. “A Recipe for Cracking the Quantum Scaling Limit with Machine Learned Electron Densities.” Machine Learning: Science and Technology, 2022.
BibTeX Click to copy
@article{joshua2022a,
title = {A recipe for cracking the quantum scaling limit with machine learned electron densities},
year = {2022},
journal = {Machine Learning: Science and Technology},
author = {Rackers, Joshua A. and Tecot, Lucas and Geiger, M. and Smidt, T.}
}
Abstract
A long-standing goal of science is to accurately simulate large molecular systems using quantum mechanics. The poor scaling of current quantum chemistry algorithms on classical computers, however, imposes an effective limit of about a few dozen atoms on traditional electronic structure calculations. We present a machine learning (ML) method to break through this scaling limit for electron densities. We show that Euclidean neural networks can be trained to predict molecular electron densities from limited data. By learning the electron density, the model can be trained on small systems and make accurate predictions on large ones. In the context of water clusters, we show that an ML model trained on clusters of just 12 molecules contains all the information needed to make accurate electron density predictions on cluster sizes of 50 or more, beyond the scaling limit of current quantum chemistry methods.
