Segmentation for Learning Adsorption Patterns and Residence-Time Kinetics on Amorphous Surfaces.

ABSTRACT: Heterogeneous surfaces such as amorphous silica are characterized by highly heterogeneous local atomic environments that govern the adsorption of gas molecules through spatial arrangements. These surfaces exhibit properties that are particularly relevant for adsorption and catalytic applications. Here, we investigate CO₂ adsorption landscapes, captured by CO₂ density maps, which display complex patterns requiring machine learning (ML) segmentation for systematic analysis. We present an optimized segmentation protocol based on a modified Random Forest (RF) classifier designed to control the morphology and spatial extent of the segmented regions via feature smoothing and standardized training parameters. While broadly applicable for specific modeling goals and propertie

SUBMITTER: Turchi M 

PROVIDER: S-EPMC12570132 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature