Learning Adsorption Patterns on Amorphous Surfaces.

ABSTRACT: The physicochemical heterogeneity found on amorphous surfaces leads to a complex interaction of adsorbate molecules with topological and undercoordinated defects, which enhance the adsorption capacity and can participate in catalytic reactions. The identification and analysis of the adsorption structure observed on amorphous surfaces require novel tools that allow the segmentation of the surfaces into complex-shaped regions that contrast with the periodic patterns found on crystalline surfaces. We propose a Random Forest (RF) classifier that segments the surface into regions that can then be further analyzed and classified to reveal the dynamics of the interaction with the adsorbate. The RF segmentation is applied to the surface density map of the adsorbed molecules and employs multiple fe

SUBMITTER: Turchi M 

PROVIDER: S-EPMC11391580 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature