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

## TL;DR

This paper introduces a machine learning method to analyze CO2 adsorption patterns on amorphous surfaces and extract their kinetics for modeling.

## Contribution

An optimized Random Forest segmentation protocol for analyzing adsorption patterns and extracting non-exponential residence-time kinetics.

## Key findings

- CO2 density maps on amorphous surfaces show complex adsorption patterns requiring ML segmentation.
- High-density regions identified reveal multiple time scales linked to surface defects.
- Extracted kinetics enable coarse-grained models for predicting adsorption/desorption rates.

## 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 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.