One such biased sampling methodology was recently proposed and used in a computational screening study of adsorptive separation of xenon from krypton. Simon et al. used a machine learning model to screen over 670,000 structures based on geometrical descriptors and one energy descriptor that mainly explains the accuracy of the final model. To calculate this descriptor, the authors used the average of the interaction energy over the Voronoi network to account for the exponential contribution of the energy in the Henry constant.