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We demonstrate how machine-learning based interatomic potentials can be usedto model guest atoms in host structures. Specifically, we generate Gaussianapproximation potential (GAP) models for the interaction of lithium atoms withgraphene, graphite, and disordered carbon nanostructures, based on referencedensity-functional theory (DFT) data. Rather than treating the full Li--Csystem, we demonstrate how the energy and force differences arising from Liintercalation can be modeled and then added to a (prexisting and unmodified)GAP model of pure elemental carbon. Furthermore, we show the benefit of usingan explicit pair potential fit to capture "effective" Li--Li interactions, toimprove the performance of the GAP model. This provides proof-of-concept formodeling guest atoms in host frameworks with machine-learning based potentials,and in the longer run is promising for carrying out detailed atomistic studiesof battery materials.

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures