A Bayesian Graph Convolutional Network for Reliable Prediction of Molecular Properties with Uncertainty Quantification

Deep neural networks have been increasingly used in various chemical fields. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis.