Issue 3, 2023

Toward a new definition of surface energy for late transition metals

Abstract

Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined density functional theory (DFT) calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model predicting the average surface energy of metal structures independently of their shape. The metals considered are W, Ru, Co, Ir, Ni, Pd, Pt, Cu, Ag and Au. Lorentzian trends derived from the DFT data proved effective at predicting the surface energy of metallic surfaces but not for metal clusters. We used machine-learning protocols to build an algorithm that improves the Lorentzian trend's accuracy and is able to predict the surface energies of metal surfaces of any crystal structure, i.e., face-centred cubic, hexagonal close-packed, and body-centred cubic, but also of nanostructures and sub-nanometer clusters. The machine-learning neural network takes easy-to-compute geometric features to predict metallic moieties surface energies with a mean absolute error of 0.091 J m−2 and an R2 score of 0.97.

Graphical abstract: Toward a new definition of surface energy for late transition metals

Supplementary files

Article information

Article type
Paper
Submitted
30 авг 2022
Accepted
12 дек 2022
First published
12 дек 2022
This article is Open Access
Creative Commons BY license

Phys. Chem. Chem. Phys., 2023,25, 1977-1986

Toward a new definition of surface energy for late transition metals

A. Boucher, G. Jones and A. Roldan, Phys. Chem. Chem. Phys., 2023, 25, 1977 DOI: 10.1039/D2CP04024G

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