Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids
This provides a fast and accurate method for predicting electron density in materials science, which is crucial for energy and functional materials applications, though it is incremental as it builds on existing graph neural network techniques.
The authors tackled the problem of predicting electron density for materials using a machine learning framework based on equivariant graph neural networks, achieving accuracy beyond state-of-the-art and computation times orders of magnitude faster than density functional theory across molecules, liquids, and solids.
Electron density $ρ(\vec{r})$ is the fundamental variable in the calculation of ground state energy with density functional theory (DFT). Beyond total energy, features and changes in $ρ(\vec{r})$ distributions are often used to capture critical physicochemical phenomena in functional materials. We present a machine learning framework for the prediction of $ρ(\vec{r})$. The model is based on equivariant graph neural networks and the electron density is predicted at special query point vertices that are part of the message passing graph, but only receive messages. The model is tested across multiple data sets of molecules (QM9), liquid ethylene carbonate electrolyte (EC) and LixNiyMnzCo(1-y-z)O2 lithium ion battery cathodes (NMC). For QM9 molecules, the accuracy of the proposed model exceeds typical variability in $ρ(\vec{r})$ obtained from DFT done with different exchange-correlation functionals. The accuracy on all three datasets is beyond state of the art and the computation time is orders of magnitude faster than DFT.