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

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 properties of interest, here, the method is tailored to identify high-density regions that dominate heterogeneous adsorption dynamics. For these regions, we extract residence-time statistics that deviate from exponential behavior, revealing multiple time scales associated with distinct surface defects on amorphous surfaces. The extracted kinetics provide essential information for coarse-grained models of adsorption on disordered surfaces. Such models, parametrized using atomistic simulations, enable the prediction of macroscopically measurable adsorption and desorption rates, which can be directly compared with experiments also under conditions not limited by mass transfer.