Inducing wetting morphologies and increased reactivities of small Au clusters on doped oxide supports

Going for Gold(-Standard): Attaining Coupled Cluster Accuracy in Oxide-Supported Nanoclusters

The structure of oxide-supported metal nanoclusters plays an essential role in their sharply enhanced catalytic activity over that of bulk metals. Simulations provide the atomic-scale resolution needed to understand these systems. However, the sensitive mix of metal-metal and metal-support interactions, which govern their structure, puts stringent requirements on the method used, requiring calculations beyond standard density functional theory (DFT). The method of choice is coupled cluster theory [specifically CCSD(T)], but its computational cost has so far prevented its application to these systems. In this work, we showcase two approaches to make CCSD(T) accuracy readily achievable in oxide-supported nanoclusters. First, we leverage the SKZCAM protocol to provide the first benchmarks of oxide-supported nanoclusters, revealing that it is specifically metal-metal interactions that are challenging to capture with DFT. Second, we propose a CCSD(T) correction (ΔCC) to the metal-metal interaction errors in DFT, reaching accuracy comparable to that of the SKZCAM protocol at significantly lower cost. This approach forges a path toward studying larger systems at reliable accuracy, which we highlight by identifying a ground-state structure in agreement with experiments for Au20 on MgO, a challenging system where DFT models have yielded conflicting predictions.

A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer

Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the established methods, however, rely on local properties and are thus unable to take global changes in the electronic structure into account, which result from long-range charge transfer or different charge states. In this work we overcome this limitation by introducing a fourth-generation high-dimensional neural network potential that combines a charge equilibration scheme employing environment-dependent atomic electronegativities with accurate atomic energies. The method, which is able to correctly describe global charge distributions in arbitrary systems, yields much improved energies and substantially extends the applicability of modern machine learning potentials. This is demonstrated for a series of systems representing typical scenarios in chemistry and materials science that are incorrectly described by current methods, while the fourth-generation neural network potential is in excellent agreement with electronic structure calculations.

Machine learning potentials do not account for long-range charge transfer. Here the authors introduce a fourth-generation high-dimensional neural network potential including non-local information of charge populations that is able to provide forces, charges and energies in excellent agreement with DFT data.

Support work function as a descriptor and predictor for the charge and morphology of deposited Au nanoparticles

We show, using density functional theory calculations, that the charge, magnetic moment, and morphology of deposited Au nanoclusters can be tuned widely by doping the oxide support with aliovalent cations and anions. As model systems, we have considered Au_n (n = 1, 2, or 20) deposited on doped MgO and MgO/Mo supports. The supports have been substitutionally doped with varying concentrations θ of F, Al, N, Na, or Li. At θ = 2.78%, by varying the dopant species, we are able to tune the charge of the Au monomer between -0.84e and +0.21e, the Au dimer between -0.87e and -0.16e, and, most interestingly, Au₂₀ between -3.97e and +0.49e. These ranges can be further extended by varying θ. These changes in charge are correlated with changes in adsorption and/or cluster geometry and magnetic moment. We find that the work function Φ of the bare support is a good predictor and descriptor of both the geometry and charge of the deposited Au cluster; it can, therefore, be used to quickly estimate which dopant species and concentration can result in a desired cluster morphology and charge state. This is of interest as these parameters are known to significantly impact cluster reactivity, with positively or negatively charged clusters being preferred as catalysts for different chemical reactions. It is particularly noteworthy that the Na-doped and Li-doped supports succeed in making Au₂₀ positively charged, given the high electronegativity of Au.