From the practical perspective of virtual catalyst screening, it is desirable that the models can provide uncertainty estimation for their predictions, enabling researchers to evaluate the reliability of predictions and assign experimental effort more efficiently. To this end, an ensemble of independent AdsMT replicates is trained to estimate the uncertainty from the variance of individual models' predictions, which is a widely recognized method for effective uncertainty quantification. (Methods) . The AdsMT ensemble's predictions were ranked based on their uncertainty estimations (Supplementary Note 4), and the correlation between uncertainty and prediction MAE was investigated. As depicted in Supplementary Fig. predictions with lower uncertainty tend to have lower MAEs across the three GMAE datasets.