N-2 dissociation on W(110): An ab initio molecular dynamics study on the effect of phonons

Seventh Framework Programme (FP7

Ab initio molecular dynamics calculations on reactions of molecules with metal surfaces

Reactions on metal surfaces are of scientific interest due to the tremendous relevance of heterogeneous catalysis. Single crystal surfaces under controlled physical conditions are generally employed as a model for the real catalysts, with the aim of improving the fundamental understanding of the adsorption of molecules on metals. In this field, computer simulations have a high potential to help with interpreting experiments as they can provide an atomic-scale movie of a chemical process. The aim of this thesis has been to apply the ab initio molecular dynamics (AIMD) technique to the study of reactions on metal surfaces. The use of AIMD bypasses the need of pre-computing and fitting a potential energy surface, since the forces acting on the nuclei are calculated `on-the-fly' at each time step of the dynamics. The advantage is that statistically accurate reaction probabilities for small molecules on metal surfaces can be calculated including surface temperature effects and lattice recoil without introducing a priori dynamical approximations on the molecular degrees of freedom. Observables derived from the reaction probability, such as the sticking coefficient, the vibrational efficacy, and the rotational alignment parameter, have been calculated and compared to available experimental data for H2+Cu(111), N2+W(110) and CH4+Pt(111).UBL - phd migration 201

N2 dissociation on W(110): An ab initio molecular dynamics study on the effect of phonons

Theoretical Chemistr

Application of van der Waals functionals to the calculation of dissociative adsorption of N2 on W(110) for static and dynamic systems

Theoretical Chemistr

The effect of the perturber population on subhalo measurements in strong gravitational lenses

Analyses of extended arcs in strong gravitational lensing images to date have constrained the properties of dark matter by measuring the parameters of one or two individual subhaloes. However, since such analyses are reliant on likelihood-based methods like Markov-chain Monte Carlo or nested sampling, they require various compromises to the realism of lensing models for the sake of computational tractability, such as ignoring the numerous other subhaloes and line-of-sight haloes in the system, assuming a particular form for the source model and requiring the noise to have a known likelihood function. Here, we show that a simulation-based inference method called truncated marginal neural ratio estimation (TMNRE) makes it possible to relax these requirements by training neural networks to directly compute marginal posteriors for subhalo parameters from lensing images. By performing a set of inference tasks on mock data, we verify the accuracy of TMNRE and show it can compute posteriors for subhalo parameters marginalized over populations of hundreds of substructures, as well as lens and source uncertainties. We also find that the multilayer perceptron (MLP) mixer network works far better for such tasks than the convolutional architectures explored in other lensing analyses. Furthermore, we show that since TMNRE learns a posterior function it enables direct statistical checks that would be extremely expensive with likelihood-based methods. Our results show that TMNRE is well-suited for analysing complex lensing data, and that the full subhalo and line-of-sight halo population must be included when measuring the properties of individual dark matter substructures with this technique

Methane Dissociation on Pt(111): Searching for a Specific Reaction Parameter Density Functional

Theoretical Chemistr

Modeling surface motion effects in N2 dissociation on W(110): Ab Initio Molecular Dynamics Calculations and Generalized Langevin Oscillator Model

Theoretical Chemistr

Dissociative chemisorption of methane on Ni(111) using a chemically accurate fifteen dimensional potential energy surface

Theoretical Chemistr