In this work, we evaluated six different ML methods for this application: logistic regression, decision trees, 44 linear support vectors, radial basis support vectors, 46 multi-layer perceptrons, and random forests. All ML algorithms were implemented using the Python library Scikit-learn. We chose 10-fold cross validation to evaluate and report model accuracy in this work. Although ML has been applied in a wide variety of studies, spanning organic synthesis, materials discovery, and catalysis, this study differs from many of these approaches in that the models we describe in this work are trained to make predictions for each atom rather than the entire molecule. As the features and targets are specific to the atoms in each catalyst, we split the full dataset into training and testing data by molecule during 10-fold cross validation. This avoids scenarios in which a single catalyst's sites are split between the training and validation data, where information about the overall activity of a catalyst could leak from the training data into predictions on the validation data. This distinction is critical due to our aforementioned focus on atomic-level prediction, rather than molecular prediction.