With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop various approaches of using rule-based models and physics-based simulations to train ChemNet, a transferable and generalizable...
Creator:
Goh, Garrett (Pacific Northwest National Laboratory)
Created:
2018-03-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
