On the equivalence of molecular graph convolution and molecular wave function with poor basis set

In this study, we demonstrate that the linear combination of atomic orbitals (LCAO), an approximation of quantum physics introduced by Pauling and Lennard-Jones in the 1920s, corresponds to graph convolutional networks (GCNs) for molecules. However, GCNs involve unnecessary nonlinearity and deep architecture. We also verify that molecular GCNs are based on a poor basis function set compared with the standard one used in theoretical calculations or quantum chemical simulations. From these observations, we describe the quantum deep field (QDF), a machine learning (ML) model based on an underlying quantum physics, in particular the density functional theory (DFT). We believe that the QDF model can be easily understood because it can be regarded as a single linear layer GCN. Moreover, it uses two vanilla feedforward neural networks to learn an energy functional and a Hohenberg-Kohn map that have nonlinearities inherent in quantum physics and the DFT. For molecular energy prediction tasks, we demonstrated the viability of an "extrapolation," in which we trained a QDF model with small molecules, tested it with large molecules, and achieved high extrapolation performance. This will lead to reliable and practical applications for discovering effective materials. The implementation is available at https://github.com/masashitsubaki/ QuantumDeepField_molecule . Energy functional in Eq.( 10 ) Energy Molecule Atomic basis function Molecular orbital Electron density External potential Hohenberg-Kohn map in Eq.( 13 ) LCAO in Eq.( 8 ) Learning a DNN (constraint) Learning a DNN (predict) Gaussian in Eq.( 11 ) Squared sum in Eq.( 12 ) GTO in Eq.( 7 ) 2 Background: molecular GCN and LCAO 2.1 Molecular GCN. A molecule is defined as , where a m is the mth atom (e.g., H and O), R m is the 3D coordinate of a m , and M is the number of atoms in M. We consider a graph representation of M, in which the node is