A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification
Author:
Affiliation:
1. Department of Chemistry
2. KAIST
3. Daejeon 34141
4. Republic of Korea
5. Department of Statistics
6. Seoul National University
7. Seoul 08826
8. KI for Artificial Intelligence
Abstract
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.
Funder
National Research Foundation of Korea
Publisher
Royal Society of Chemistry (RSC)
Subject
General Chemistry
Link
http://pubs.rsc.org/en/content/articlepdf/2019/SC/C9SC01992H
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