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The accuracy of the training data limits the accuracy of bulk properties frommachine-learned potentials. For example, hybrid functionals orwave-function-based quantum chemical methods are readily available for clusterdata but effectively out-of-scope for periodic structures. We show that local,atom-centred descriptors for machine-learned potentials enable the predictionof bulk properties from cluster model training data, agreeing reasonably wellwith predictions from bulk training data. We demonstrate such transferabilityby studying structural and dynamical properties of bulk liquid water withdensity functional theory and have found an excellent agreement withexperimental as well as theoretical counterparts.

Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water