Towards constant potential modeling of CO-CO coupling at liquid water-Cu(100) interfaces

We have studied electrochemical CO-CO coupling in explicit electrolyte with density functional theory, molecular dynamics, and metadynamics. We considered both the CO-CO coupling reaction and the charging process required to keep the potential constant. The charging process consists of transferring explicit cations from the electrolyte and electrons from the potentiostat to the interface. Under constant charge conditions (non-constant electrostatic potential), the CO-CO coupling reaction energies are relative insensitive to the charge state at the interface and the electrolyte composition and the reaction occurs with co-adsorption of water. Under constant potential conditions, the CO-CO coupling reaction is stabilized at lower potentials because of charging and the reaction is influenced by the electrolyte composition. Here we have collected the data from the eight AIMD metadynamics simulations conducted in the study. Each AIMD data tar.gz file contains the VASP input files (INCAR, KPOINTS, POTCAR, ICONST), the VASP output files for the full AIMD run (OUTCAR, PENALTYPOT), python scripts that have been used to analyze the AIMD run, data files made by those scripts (dat), a folder (0/) used to set up single point calculations of workfunction and Bader chages at 0.125 ps intervals along the AIMD trajectory, and one folder (*.000ps) containing an example of a workfunction and Bader charge calculation.

Identifier
Source https://archive.materialscloud.org/record/2021.18
Metadata Access https://archive.materialscloud.org/xml?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai:materialscloud.org:719
Provenance
Creator Kristoffersen, Henrik H.; Chan, Karen
Publisher Materials Cloud
Publication Year 2021
Rights info:eu-repo/semantics/openAccess; Creative Commons Attribution Share Alike 4.0 International https://creativecommons.org/licenses/by-sa/4.0/legalcode
OpenAccess true
Contact archive(at)materialscloud.org
Representation
Language English
Resource Type Dataset
Discipline Materials Science and Engineering