Accelerating the global search of adsorbate molecule positions using machine-learning interatomic potentials with active learning

We present an algorithm for accelerating the search of a molecule's adsorption sites based on global optimization of surface adsorbate geometries. Our approach uses a machine-learning interatomic potential (moment tensor potential) to approximate the potential energy surface and an active learning algorithm for the automatic construction of an optimal training dataset. To validate our methodology, we compare the results across various well-known catalytic systems with surfaces of different crystallographic orientations and adsorbate geometries, including CO/Pd(111), NO/Pd(100), NH₃/Cu(100), C₆H₆/Ag(111), and CH₂CO/Rh(211). In all the cases, we observed an agreement of our results with the literature.