Size-Extensivity of Machine Learning Potentials for Molecules

Size-extensivity is an important concept for quantum chemistry methods to ensure properties such as total energies scale proportionally with the system size. Machine learning potentials have been shown to be an efficient method to construct accurate potential energy surfaces for molecules and extended systems. Transferability of these potentials have been studied generally on molecules that are of similar size with the ones in the training set. In this study, we explored both neural network and Gaussian process regression based potentials with a variety of descriptors and compared the accuracy of these potentials as the system size increased. We studied alkanes, molecular clusters, and polycyclic aromatic hydrocarbons and identified techniques to satisfy size-extensivity.