| dc.contributor.advisor | Wodtke, Alec M. Prof. Dr. | |
| dc.contributor.author | Wille, Sebastian | |
| dc.date.accessioned | 2022-08-01T12:51:49Z | |
| dc.date.available | 2022-08-08T00:50:12Z | |
| dc.date.issued | 2022-08-01 | |
| dc.identifier.uri | http://resolver.sub.uni-goettingen.de/purl?ediss-11858/14190 | |
| dc.identifier.uri | http://dx.doi.org/10.53846/goediss-9386 | |
| dc.language.iso | eng | de |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject.ddc | 540 | de |
| dc.title | Computational Description of Chemical Bond Formation Using Neural-Network Potentials: H-Atom Scattering from Graphene | de |
| dc.type | doctoralThesis | de |
| dc.contributor.referee | Wodtke, Alec M. Prof. Dr. | |
| dc.date.examination | 2022-06-29 | de |
| dc.description.abstracteng | When a hydrogen or a deuterium atom is interacting with a graphene surface during scattering
events, its energy loss determines if the projectile will be adsorbed or scattered. The energy
loss highly depends on the initial conditions of the projectile and the surface. Unlike for metal
and insulator surfaces, a barrier to C–H bond formation is involved, which is the movement
of a single carbon atom out of the surface plane (puckering) and by that rehybridization of
the C-atom from sp2 to sp3. In the experiments, a bimodal branching of the translational
energy and scattering angle distribution is observed. One signal corresponds to a quasi-elastic
channel, where the impinging atom loses only a minor fraction of its initial kinetic energy. The
location of this channels’ maximum intensity can be reasonably well estimated with a simple
binary collision model. The second signal corresponds to inelastic scattering events, where the
projectile loses a large fraction of its initial kinetic energy. Previous studies using a reactive
empirical bond order potential are in good agreement with incidence kinetic energies of 1 eV,
but could not reproduce the distribution of scattering with 2 eV incidence kinetic energy at
given incidence conditions.
Machine learning potentials are best known to bridge the gap between accuracy and performance.
To study the bond formation of H- and D-atoms with graphene with high dimensional
neural network potentials, I implemented the existing description of neural network potentials
in our developed molecular dynamics program and generated a high-dimensional neural network
for H-atoms at a free-standing graphene surface. The performance of this new potential to
accurately describe the experiment is much better than the previously reported reactive bond
order potential. The neural network potential is able to capture the correct branching of the
two channels observed in the experiment and can even reproduce subtle differences seen in
energy loss distributions for different hydrogen isotopes. But still, systematic differences can
be seen, especially for small incidence polar angles, where out-of-plane scattering is more likely.
New experiments for a H-atom scattering along the surface normal indicate that nuclear
quantum effects might play a more important role in the scattering dynamics than previously
anticipated. Therefore, classical and path-integral based simulations are compared to fullquantum
mechanical simulations based on wave-package propagation. Classical simulations
are incapable to predict the correct energy loss and scattering distributions from experiment
for higher incidence kinetic energies of the projectile. Path-integral based simulations improve
the description, but only taking all quantum effects into account offers the best description of
H-atom scattering from graphene with normal incidence direction. | de |
| dc.contributor.coReferee | Behler, Jörg Prof. Dr. | |
| dc.contributor.thirdReferee | Mata, Ricardo A. Prof. Dr. | |
| dc.contributor.thirdReferee | Ropers, Claus Prof. Dr. | |
| dc.contributor.thirdReferee | Geil, Burkhard Prof. Dr. | |
| dc.contributor.thirdReferee | Suhm, Martin A. Prof. Dr. | |
| dc.subject.eng | Atomic Scattering | de |
| dc.subject.eng | Machine Learning | de |
| dc.subject.eng | Molecular Dynamics | de |
| dc.identifier.urn | urn:nbn:de:gbv:7-ediss-14190-6 | |
| dc.affiliation.institute | Fakultät für Chemie | de |
| dc.subject.gokfull | Chemie (PPN62138352X) | de |
| dc.description.embargoed | 2022-08-08 | de |
| dc.identifier.ppn | 1813201242 | |
| dc.notes.confirmationsent | Confirmation sent 2022-08-01T13:15:01 | de |