ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales
Year: 2024
Type: article
Abstract: High Resolution Image Download MS PowerPoint Slide We combine automatic process exploration with an iteratively trained machine-learning interatomic potential to systematically identify elementary processes occurring during the initial oxidation of a Pd step edge. Corresponding process lists are a prerequisite to overcome prevalent predictive-quality microkinetic modeling approaches which consider only a minimum number of hand-selected and thus typically intuitive processes. The exploration readily generates close to 3000 inequivalent elementary processes and thus unveils a complexity far beyond current microkinetic modeling capabilities. Among these processes are numerous low-barrier processes involving the collective motion of several atoms that enable a facile O-mediated restructuring of the Pd step edge through the motion of larger Pd x O y units. The concomitant interconversion happens on time scales comparable to those of molecular processes of heterogeneous oxidation catalysis. This suggests a dynamic aspect of the operando evolution of the working interface reminiscent of the fluxionality discussed in nanocluster catalysis.
Source: ACS Catalysis
Institution: Fritz Haber Institute of the Max Planck Society
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
Funder: Alexander von Humboldt-Stiftung
OpenAlex ID: W4405644769