Author Description

First, we develop a multiscale modeling framework for supported single atom and subnanometer cluster catalysts. The framework integrates a comprehensive toolset including density functional theory (DFT) calculations performed by collaborators, genetic algorithm-based structure optimization, machine learning, equilibrium-based Metropolis Monte Carlo, and kinetic Monte Carlo (KMC) simulations. We choose Pd single atoms and subnanometer clusters of a few atoms (size, n = 1-55) on CeO2(111) in a CO atmosphere as a case study. We first investigate the structures of Pdn clusters and CO adsorption energies on various sites using DFT. DFT supplies high-quality first-principles data to train machine learning Hamiltonians, which represent efficient structure-to-energy mappings. Combined with the Hamiltonians, Monte-Carlo-based structure optimization algorithms, such as a cluster genetic algorithm, determine low energy structures. Active learning improves the model accuracy by passing the predicted structures to DFT and using the structure-energy DFT data to train the Hamiltonians iteratively. KMC simulations track the structure evolution of the catalysts against the real-time and predict the time scales of elementary events under the working conditions. The framework elucidates the stability, structures, and dynamics of supported metal clusters that are bare or exposed to an adsorbate used for characterization, e.g., CO in infrared spectroscopy. The methodology can be applied to any metal/support system.