Neural Network Potential Simulations of Copper Supported on Zinc Oxide Surfaces

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

Grand Canonical Investigation of the Quasi Liquid Layer of Ice: Is It Liquid?

In this study, the solid-vapor equilibrium and the quasi liquid layer (QLL) of ice Ih exposing the basal and primary prismatic faces were explored by means of grand canonical molecular dynamics simulations with the monatomic mW potential. For this model, the solid-vapor equilibrium was found to follow the Clausius-Clapeyron relation in the range examined, from 250 to 270 K, with a δHsub of 50 kJ/mol in excellent agreement with the experimental value. The phase diagram of the mW model was constructed for the low pressure region around the triple point. The analysis of the crystallization dynamics during condensation and evaporation revealed that, for the basal face, both processes are highly activated, and in particular cubic ice is formed during condensation, producing stacking-disordered ice. The basal and primary prismatic surfaces of ice Ih were investigated at different temperatures and at their corresponding equilibrium vapor pressures. Our results show that the region known as QLL can be interpreted as the outermost layers of the solid where a partial melting takes place. Solid islands in the nanometer length scale are surrounded by interconnected liquid areas, generating a bidimensional nanophase segregation that spans throughout the entire width of the outermost layer even at 250 K. Two approaches were adopted to quantify the QLL and discussed in light of their ability to reflect this nanophase segregation phenomena. Our results in the μVT ensemble were compared with NPT and NVT simulations for two system sizes. No significant differences were found between the results as a consequence of model system size or of the working ensemble. Nevertheless, certain advantages of performing μVT simulations in order to reproduce the experimental situation are highlighted. On the one hand, the QLL thickness measured out of equilibrium might be affected because of crystallization being slower than condensation. On the other, preliminary simulations of AFM indentation experiments show that the tip can induce capillary condensation over the ice surface, enlarging the apparent QLL.Fil: Pickering, Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Paleico, Martín Leandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Pérez Sirkin, Yamila Anahí. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Scherlis Perel, Damian Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; ArgentinaFil: Factorovich, Matias Hector. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química, Física de los Materiales, Medioambiente y Energía. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química, Física de los Materiales, Medioambiente y Energía; Argentina. Comisión Nacional de Energía Atómica. Centro Atómico Constituyentes; Argentin