34 citations · 34 across the 2 of their papers we have counts for
2 papers
cs.CE2022
Graph neural networks to learn joint representations of disjoint molecular graphs
Chen Shao, Zhou Chen, Pascal Friederich
Graph neural networks are widely used to learn global representations of graphs, which are then used for regression or classification tasks. Typically, the graphs in such data sets…
physics.chem-ph2022★ 34 cited
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design n…