Improved Prediction of Nanoalloy Structures by the Explicit Inclusion of Adsorbates in Cluster Expansions

Density functional theory (DFT) is widely used to predict the properties of materials, but its direct application to nanomaterials of experimentally relevant size can be prohibitively expensive. It has previously been demonstrated that this problem can be addressed through the generation of cluster expansion models trained on DFT calculations. Here, we evaluate the use of the cluster expansion method to calculate the structures of bimetallic Pt–Cu nanoparticles of varying sizes and compositions and in different chemical environments. The predicted surface composition, shape, and lattice parameters of the alloy nanoparticles are found to be in good agreement with experimental characterization. We demonstrate that, to account for adsorbate-induced surface segregation, the best agreement for surface composition can be achieved by constructing a novel cluster expansion for alloy nanoparticles of varying shapes and sizes that explicitly includes adsorbed oxygen.