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Found 5 papers, 2 papers with code
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.
Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT).
Mean-field theory of graph neural networks in graph partitioning
A theoretical performance analysis of the graph neural network (GNN) is presented.
Dual Convolutional Neural Network for Graph of Graphs Link Prediction
Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.
