Achieving such high accuracy with only using 120 structures for training the model demonstrates the advantage of incorporating the physics of the problem in the machine learning model. In addition, we estimate the uncertainty of the machine learning predictions using the variance in predictions of an ensemble of machine learning models trained on different Ave. Atomic Mass training sets (see method section for details). This enables us using our machine learning models to predict the heat capacity of porous material databases with a controlled error and confidence. Figure shows that indeed this approach effectively identifies those structures that the model has wrong predictions as uncertain.