# Beyond scaling relations for the description of catalytic materials

## TL;DR

This paper introduces a novel compressed sensing approach that predicts adsorption energies on complex catalyst surfaces using simple surface properties, outperforming traditional scaling relations.

## Contribution

It develops a general, accurate method for predicting adsorption energies across various catalysts using non-linear descriptors derived from DFT calculations.

## Key findings

- Outperforms previous scaling relations in accuracy.
- Works well across pure and alloy metal surfaces.
- Predicts stable surface geometries from a single DFT calculation.

## Abstract

Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors whose predictive power extends over a wide range of adsorbates, multi-metallic transition metal surfaces and facets. The descriptors are expressed as non-linear functions of intrinsic properties of the clean catalyst surface, e.g. coordination numbers, d-band moments and density of states at the Fermi level. From a single density-functional theory calculation of these properties, we predict adsorption energies at all potential surface sites, and thereby also the most stable geometry. Compared to previous approaches such as scaling relations, we find our approach to be both more general and more accurate for the prediction of adsorption energies on alloys with mixed-metal surfaces, already when based on training data including only pure metals. This accuracy can be systematically improved by adding also alloy adsorption energies to the training data.