Heterogeneous catalysis is an area of active research, because many industrially relevant reactions
involve gaseous reactants and are accelerated by solid phase catalysts. In recent years, activity in the
field has become more intense due to the development of surface science and simulation techniques
that allow for acquiring deeper insight into these catalysts, with the goal of producing more active,
cheaper and less toxic catalytic materials.
One particularly crucial case study for heterogeneous catalysis is the synthesis of methanol from
synthesis gas, composed of H2, CO and CO2. The reaction is catalyzed by a mixture of Cu and ZnO
nanoparticles with Al2O3 as a support material. This process is important not only due to methanol’s
many uses as a solvent, raw material for organic synthesis, and possible energy and carbon capture material, but also as an example for many other metal/metal oxide catalysts. A plethora of experimental
studies are available for this catalyst, as well as for simpler model systems of Cu clusters supported on
ZnO surfaces. Unfortunately, there is still a lack of theoretical studies that can support these experi-
mental results by providing an atom-by-atom representation of the system.
This scarcity of atomic level simulations is due to the absence of fast but ab-initio level accurate
potentials that would allow for reaching larger systems and longer simulated time scales. A promising
possibility to bridge this gap in potentials is the rise of machine learning potentials, which utilize the
tools of machine learning to reproduce the potential energy surface of a system under study, as sampled
by an expensive electronic structure reference method of choice. One early and fruitful example of
such machine learning force fields are neural network potentials, as initially developed by Behler and
Parrinello.
In this thesis, a neural network potential of the Behler-Parrinello type has been constructed for
ternary Cu/Zn/O systems, focusing on supported Cu clusters on the ZnO(10-10) surface, as a model for
the industrial catalyst. This potential was subsequently utilized to perform a number of simulations.
Small supported Cu clusters between 4 and 10 atoms were optimized with a genetic algorithm, and
a number of structural trends observed. These clusters revealed the first hints of the structure of the
Cu/ZnO interface, where Cu prefers to interact with the support through configurations in the continuum between Cu(110) and Cu(111). Simulated annealing runs for Cu clusters between 200 and 500
atoms reinforced this observation, with these larger clusters also adopting this sort of interface with the
support. Additionally, in these simulations the effect of strain induced by the support can be observed,
with deviations from ideal lattice constants reaching the top of all of the clusters. To further investigate
the influence of strain in this system, large coincident surfaces of Cu were deposited on ZnO supports.
Due to the lattice mismatch present between the two materials, this requires straining the Cu overlayer.
This analysis confirmed once again that Cu(110) and Cu(111) are the most stable surfaces when de-
posited on ZnO(10-10). During this thesis a number of new algorithm and programs were developed.
Of particular interest is the bin and hash algorithm, which was designed to aid in the construction and
curating of reference sets for the neural network potential, and can also be used to evaluate the quality
of atomic descriptor sets.2021-10-0
