Wednesday, July 29, 2020

OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Features

Figure 4 from the paper. (c) the authors 2020.

This method takes information from a GFN1-xTB calculation as input to a graph-convolution (GC) NN to predict the difference between DFT and GFN1-xTB total energies. In conventional GC the molecule is typically represented by an adjacency matrix (a binary matrix where 1 indicates a bond) and a list of atomic and bond features, such as nuclear charges and bond orders, associated with each node and edge. This approach uses the diagonal and off-diagonal elements of matrices such as Fock, overlap, and density matrices from a GFN1-xTB calculation as node and edge features, respectively. 

The model gets state-of-the-art accuracies for QM9 total energies and the same model also gets excellent results for conformational energies from a different data set. Basically DFT level accuracy at semiempirical cost (it's not clear to me how it can be faster than the underlying GFN1-xTB calculation, but that might be down to different implementation of the GFN1-xTB method).

It's not clear to me weather the method can be used to optimise geometries, and thereby correct any deficiency in GFN1-xTB structures, and it's also not clear whether the code will be made available.

No comments:

Post a Comment