Efficient grand canonical global optimization with on-the-fly-trained machine-learning interatomic potentials

The characterization of nanostructured materials under reactive environments is challenging due to the complexity of the structural motifs involved and their chemical transformations. Global optimization approaches allow predicting stable structures for targeted materials, but addressing the configurational and compositional search spaces is both computationally demanding and inefficient, especially when first-principles calculations are required. In this work, we implement and evaluate a computationally efficient grand canonical global optimization algorithm able to identify stable structures and chemical states of targeted systems under given reaction conditions (e.g., reactant pressure and temperature). The algorithm leverages an on-the-fly trained machine-learning interatomic potential based on sparse Gaussian process regression and the smooth overlap of atomic positions descriptor to reduce the number of first-principles energy evaluations carried out during global optimization searches. The ab initio thermodynamics framework is incorporated to approximate the Gibbs energy of evaluated candidates, performing environment-aware optimizations over multiple stoichiometries. We demonstrate the computational performance of this approach and its ability to reproduce some literature examples.