Generalized Bondnet: A Generalized Graph Neural Network Framework to Predict Reaction Thermodynamics and Kinetics in Developing Reaction Networks and High-Throughput Screening Runs

We have extended previous work in the Persson group on graph neural networks for predicting reaction thermodynamics to predict reaction kinetics via a transfer learning approach. This approach also leverages new quantum mechanical descriptors such as QTAIM and NBO to get better predictions of reaction properties. This improved network will be introduced into a high-throughput framework for constructing reaction networks in UV-lithography, cyclical polymerization and battery SEI contexts. New developments also see 4x improvements in training time as well for training on immense datasets.