Chemical Graph Theory: Introduction and Fundamentals

By D. Bonchev. 1991

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1. Keith T. Butler
2. Aron Walsh
3. Hyeoncheol Cho
4. Insung S. Choi
5. D. Davies
6. Hugh M. Cartwright
7. Olexandr Isayev
8. Nicholas J. Schork

Citations in the corpus

• Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, background): “GCN takes a molecular graph,([11]) through direct substitution of the molecular structure with a connected simple graph, as the input and applies the convolution on each node and its neighborhoods.”
• Machine learning for molecular and materials science (2018, background): “20 In the solid-state, the conventional description of crystal structures by translation vectors and fractional coordinates of the atoms is not appropriate for ML, since a lattice can be represented in an infinite number of ways by choosing a different coordinate system.”