1 code implementation • NeurIPS 2020 • Masashi Tsubaki, Teruyasu Mizoguchi
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.
1 code implementation • 16 Nov 2020 • Masashi Tsubaki, Teruyasu Mizoguchi
Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT).
no code implementations • NeurIPS 2018 • Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi
A theoretical performance analysis of the graph neural network (GNN) is presented.
no code implementations • 4 Oct 2018 • Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
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.