Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning
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/Cu(100), CH/Ag(111), and CHCO/Rh(211). In the all cases, we observed an agreement of our results with the literature.
Cite
@article{arxiv.2412.19162,
title = {Accelerating global search of adsorbate molecule position using machine-learning interatomic potentials with active learning},
author = {Olga Klimanova and Nikita Rybin and Alexander Shapeev},
journal= {arXiv preprint arXiv:2412.19162},
year = {2024}
}
Comments
arXiv admin note: text overlap with arXiv:2410.03484