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Calculating polarizabilities of large clusters with first-principlestechniques is challenging because of the unfavorable scaling of computationalcost with cluster size. To address this challenge, we demonstrate thatpolarizabilities of large hydrogenated silicon clusters containing thousands ofatoms can be efficiently calculated with machine learning methods.Specifically, we construct machine learning models based on the smooth overlapof atomic positions (SOAP) descriptor and train the models using a database ofcalculated random-phase approximation polarizabilities for clusters containingup to 110 silicon atoms. We first demonstrate the ability of the machinelearning models to fit the data and then assess their ability to predictcluster polarizabilities using k-fold cross validation. Finally, we study themachine learning predictions for clusters that are too large for explicitfirst-principles calculations and find that they accurately describe thedependence of the polarizabilities on the ratio of hydrogen to silicon atomsand also predict a bulk limit that is in good agreement with previous studies.

Predicting polarizabilities of silicon clusters using local chemical environments