Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
Year: 2024
Type: preprint
Abstract: We present an algorithm for accelerating the search of molecule's adsorption site 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), NH$_3$/Cu(100), C$_6$H$_6$/Ag(111), and CH$_2$CO/Rh(211). In the all cases, we observed an agreement of our results with the literature.
Source: arXiv (Cornell University)
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405902116