  It is critical that machine learning (ML) model predictions be trustworthy
for high-throughput catalyst discovery approaches. Uncertainty quantification
(UQ) methods allow estimation of the trustworthiness of an ML model, but these
methods have not been well explored in the field of heterogeneous catalysis.
Herein, we investigate different UQ methods applied to a crystal graph
convolutional neural network (CGCNN) to predict adsorption energies of
molecules on alloys from the Open Catalyst 2020 (OC20) dataset, the largest
existing heterogeneous catalyst dataset. We apply three UQ methods to the
adsorption energy predictions, namely k-fold ensembling, Monte Carlo dropout,
and evidential regression. The effectiveness of each UQ method is assessed
based on accuracy, sharpness, dispersion, calibration, and tightness.
Evidential regression is demonstrated to be a powerful approach for rapidly
obtaining tunable, competitively trustworthy UQ estimates for heterogeneous
catalysis applications when using neural networks. Recalibration of model
uncertainties is shown to be essential in practical screening applications of
catalysts using uncertainties.
