Global\nOptimization of Molybdenum Subnanoclusters\non Graphene: A Consistent Approach toward Catalytic Applications

The development of\nnovel subnanometer clusters (SNCs) catalysts\nwith superior catalytic performance depends on the precise control\nof clusters’ atomistic sizes, shapes, and accurate deposition\nonto surfaces. The intrinsic complexity of the adsorption process\ncomplicates the ability to achieve an atomistic understanding of the\nmost relevant structure–reactivity relationships hampering\nthe rational design of novel catalytic materials. In most cases, existing\ncomputational approaches rely on just a few structures to draw conclusions\non clusters’ reactivity thereby neglecting the complexity of\nthe existing energy landscapes thus leading to insufficient sampling\nand, most likely, unreliable predictions. Moreover, modeling of the\nactual experimental procedure that is responsible for the deposition\nof SNCs on surfaces is often not done even though in some cases this\nprocedure may enhance the significance of certain (e.g., metastable)\nadsorption geometries. This study proposes a novel systematic approach\nthat utilizes global search techniques, specifically, the particle\nswarm optimization (PSO) method, in conjunction with ab initio calculations,\nto simulate all stages in the beam experiments, from predicting the\nmost relevant SNCs structures in the beam and on a surface, to their\nreactivity. To illustrate the main steps of our approach, we consider\nthe deposition of Molybdenum SNC of 6 Mo atoms on a free-standing\ngraphene surface, as well as their catalytic properties with respect\nto the CO molecule dissociation reaction. Even though our calculations\nare not exhaustive and serve only to produce an illustration of the\nmethod, they are still able to provide insight into the complicated\nenergy landscape of Mo SNCs on graphene demonstrating the catalytic\nactivity of Mo SNCs and the importance of performing statistical sampling\nof available configurations. This study establishes a reliable procedure\nfor performing theoretical rational design predictions.