To predict the energy barrier for a given reaction, the graph neural network PaiNN is used. Figure shows our workflow: the inputs for the model are the atom positions and elements in the educt configuration. The start and end positions of the transferred hydrogen atom are encoded as two unique elements to define the reaction direction (end position calculated based on geometric rules). After two message passing iterations, the invariant node features of the hydrogen and of the pseudo-atom are concatenated and fed into a dense neural network, consisting of two layers with 128 nodes each, using the swish activation function, followed by one output node with linear activation. The GNN is trained to minimize the mean absolute error using the Adam optimizer with learning rate decay and early stopping. Hyperparameters are optimized using a Bayesian optimizer as implemented in the Keras Tuner package. To increase accuracy and obtain a measure of uncertainty, an ensemble of ten models with random initializations is trained. The models are validated using 10% of the training data, each model using a random training/validation split.