** Reference to paper:

https://doi.org/10.1021/acs.jpclett.3c03408

** DOI:

10.1021/acs.jpclett.3c03408

** Title:

Accurate Reaction Probabilities for Translational Energies on Both Sides of the Barrier of Dissociative Chemisorption on Metal Surfaces

** Authors:

Gerrits, Nick; Jackson, Bret; Bogaerts, Annemie

** Contact e-mail:

n.gerrits@lic.leidenuniv.nl

** Abstract:

Molecular dynamics simulations are essential for a better understanding of dissociative chemisorption on metal surfaces, which is often the rate-controlling step in heterogeneous and plasma catalysis.
The workhorse quasi-classical trajectory approach ubiquitous in molecular dynamics is able to accurately predict reactivity only for high translational and low vibrational energies.
In contrast, catalytically relevant conditions generally involve low translational and elevated vibrational energies.
Existing quantum dynamics approaches are intractable or approximate as a result of the large number of degrees of freedom present in molecule–metal surface reactions.
Here, we extend a ring polymer molecular dynamics approach to fully include, for the first time, the degrees of freedom of a moving metal surface. 
With this approach, experimental sticking probabilities for the dissociative chemisorption of methane on Pt(111) are reproduced for a large range of translational and vibrational energies by including nuclear quantum effects and employing full-dimensional simulations.

** Description per file:
QCT initial condition generation can be found in, e.g., the archive of the 2018 CHD3 + Cu paper (Gerrits et al. JCP 2018).
QCT MD can be found in the CHD3 + Cu(111) HDNNP (JPCL) archive.

** Folder Figure01:
plotsticking.py - Generates the plot
sticking_data.py - Contains the sticking probability data
stickingprobability.pdf

** Folder FigureS01:
errorfraction.pdf
errorfraction_plot.py

** Folder FigureS02:
draw_elbow.py
elbow.pdf
energy.dat
energy_DFT_elbow.dat
phi_scan.py - Utility to generate MEP

** Folder FigureS03:
plotvdwwell.py
vdwwell.pdf

** Folder FigureS04:
plotsticking_AIMD.py - Requires sticking_data.py
stickingprobability_AIMD.pdf

** Folder FigureS05:
plotsticking_beta.py - Requires sticking_data.py
stickingprobability_beta.pdf

** Folder RuNNer:
Contains the files to evaluate the HDNNP with RuNNer (or n2p2, although that will require a slight modification to input.nn w.r.t. the selected rng method)
input.data.gz - Contains all DFT training/testing data
mode2.out - Output file of the last training iteration of the HDNNP

** Folder RPMD:
NVE - Contains the setup to run NVE simulations - surface and molecular configurations need to be generated
NVT - Contains the setup to run NVT simulations for the surface, for the molecule a similar setup can be used