Zhuoran Qiao, Matthew Welborn, Animashree Anandkumar, Frederick R. Manby, Thomas F. Miller III (2020)
Highlighted by Jan Jensen
Highlighted by Jan Jensen
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.
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