# Descriptor-Driven Prediction of Adsorption Energy of Oxygenates on Metal Dioxide Surfaces

## TL;DR

This paper identifies key factors that predict how strongly oxygenates bind to metal dioxide surfaces, helping to design better catalysts.

## Contribution

A new predictive model for adsorption energy based on oxygen effective charge and metal electron affinity is developed.

## Key findings

- Adsorption energy depends on the effective charge of oxygen atoms in oxygenates.
- Metal dioxide surfaces with lower electron affinity provide stronger adsorption stability.
- A descriptor-based model accurately predicts adsorption strength across different surfaces.

## Abstract

Adsorption is a critical factor in heterogeneous catalysis,
as
the interaction between adsorbate and adsorbent significantly impacts
catalytic efficiency and selectivity. In this study, we utilized density
functional theory (DFT) to comprehensively analyze the adsorption
behavior of various oxygenates on the surfaces of metal dioxide (MO2) catalysts. Our findings reveal a strong dependence of adsorption
energy (Ead) on two primary descriptors:
the effective charge (eeff) of oxygen
atoms in oxygenates and the electron affinity (EA) of the surface
metal atoms in MO2. We observed that oxygenates with more
negative eeff exhibit stronger adsorption,
while MO2 with lower EA offer greater adsorption stability.
Using these two descriptors, a predictive Ead scaling relationship was developed and validated across different
MO2 surfaces. This descriptor-based model establishes an
efficient framework for accurately predicting adsorption strength
and offers valuable theoretical insights for designing and screening
MO2 catalysts with optimized adsorption properties.