Sphere Encapsulated Monte Carlo: Obtaining Minimum Energy Configurations of Large Aromatic Systems.

We introduce a simple global optimization approach that is able to find minimum energy configurations of clusters containing aromatic molecules. The translational and rotational perturbations required in Monte Carlo-based methods often lead to unrealistic configurations within which two or more molecular rings intersect, causing many of the computational steps to be rejected and the optimization process to be inefficient. Here we develop a modification of the basin-hopping global optimization procedure tailored to tackle problems with intersecting molecular rings. Termed the Sphere Encapsulated Monte Carlo (SEMC) method, this method introduces sphere-based rearrangement and minimization steps at each iteration, and its performance is shown through the exploration of potential energy landscapes of polycyclic aromatic hydrocarbon (PAH) clusters, systems of interest in combustion and astrophysics research. The SEMC method provides clusters that are accurate to 5% mean difference of the minimum energy at a 10-fold speed up compared to previous work using advanced molecular dynamics simulations. Importantly, the SEMC method captures key structural characteristics and molecular size partitioning trends as measured by the molecular radial distances and coordination numbers. The advantages of the SEMC method are further highlighted in its application to previously unstudied heterogeneous PAH clusters

Unconstrained Global Optimization of Molecules on Surfaces: From globally optimized structures to scanning-probe data

The adsorption of molecules on a surface plays a vital role in heterogeneous catalysis. For a proper unterstanding of the reaction mechanisms involved, the adsorption ge ometry of the molecules on the surface needs to be known. So far, experimental data from tunneling microscopes and spectroscopy, such as STM and IRAS are the main ways to obtain such knowledge. Due to the vast search space of adsorption geometries, especially for oligomers, optimizations using ab initio methods can be used to confirm the experimental data only if good initial guesses are available. Global optimization can serve two purposes in these situations. On the one hand it allows for a thorough investigation of the given search space, which can provide good initial guesses for subsequent high-level structural refinements. On the other hand, given a known reaction mechanism, it could also be used to find catalysts that influence e.g. the relevant bonds. With respect to this idea the topic of this thesis is to find a local optimization method cheap enough such that the total computational cost of global optimization does not exceed availability and yet good enough that the results are meaningful to the problem at hand. With this in mind multiple force field and semiempirical methods have been tested and evaluated mainly on benzene, acetophenone and ethyl pyruvate on Pt(111) surfaces. Some other adsorbates have also been tested shortly. In addition to these global optimization results, DFT geometry optimizations of ethyl pyruvate on Pt(111) have been performed and the structures of the best adsorption geometry from global optimization and from DFT are compared. Furthermore, from the DFT data STM images have been calculated that are compared to experimental results. The theoretical and experimental STM images agree well

Low-dimensional Material: Structure-property Relationship and Applications in Energy and Environmental Engineering

In the past several decades, low-dimensional materials (0D materials, 1D materials and 2D materials) have attracted much interest from both the experimental and theoretical points of view. Because of the quantum confinement effect, low-dimensional materials have exhibited a kaleidoscope of fascinating phenomena and unusual physical and chemical properties, shedding light on many novel applications. Despite the enormous success has been achieved in the research of low-dimensional materials, there are three fundamental challenges of research in low-dimensional materials: 1) Develop new computational tools to accurately describe the properties of low-dimensional materials with low computational cost. 2) Predict and synthesize new low-dimensional materials with novel properties. 3) Reveal new phenomenon induced by the interaction between low-dimensional materials and the surrounding environment. In this thesis, atomistic modelling tools have been applied to address these challenges. We first developed ReaxFF parameters for phosphorus and hydrogen to give an accurate description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. The potential parameters were obtained by conducting global optimization with respect to a set of reference data generated by extensive ab initio calculations. We extended ReaxFF by adding a 60° correction term which significantly improved the description of phosphorus clusters. Emphasis was placed on the mechanical response of black phosphorene with different types of defects. Compared to the nonreactive SW potential of phosphorene, ReaxFF for P/H systems provides a significant improvement in describing the mechanical properties of the pristine and defected black phosphorene, as well as the thermal stability of phosphorene nanotubes. A counterintuitive phenomenon was observed that single vacancies weaken the black phosphorene more than double vacancies with higher formation energy. Our results also showed that the mechanical response of black phosphorene is more sensitive to defects in the zigzag direction than that in the armchair direction. Since ReaxFF allows straightforward extensions to the heterogeneous systems, such as oxides, nitrides, the proposed ReaxFF parameters for P/H systems also underpinned the reactive force field description of heterogeneous P systems, including P-containing 2D van der Waals heterostructures, oxides, etc. Based on the evolutionary algorithm driven structural search, we proposed a new stable trisulfur dinitride (S3N2) 2D crystal that is a covalent network composed solely of S-N σ bonds. S3N2 crystal is dynamically, thermally and chemically stable as confirmed by the computed phonon spectrum and ab initio molecular dynamics simulations. GW calculations showed that the 2D S3N2 crystal is a wide, direct band-gap (3.92 eV) semiconductor with a small hole effective mass. The anisotropic optical response of 2D S3N2 crystal was revealed by GW-BSE calculations. Our result not only marked the prediction of the first 2D crystal composed of nitrogen and sulfur, but also underpinned potential innovations in 2D electronics, optoelectronics, etc. Inspired by the discovery of S3N2 2D crystal, we proposed a new 2D crystal, diphosphorus trisulfide (P2S3), based on the extensive evolutionary algorithm driven structural search. The 2D P2S3 crystal was confirmed to be dynamically, thermally and chemically stable by the computed phonon spectrum and ab initio molecular dynamics simulations. This 2D crystalline phase of P2S3 corresponds to the global minimum in the Born-Oppenheimer surface of the phosphorus sulfide monolayers with 2:3 stoichiometry. It is a wide band gap (4.55 eV) semiconductor with P-S σ bonds. The electronic properties of P2S3 structure can be tuned by stacking into multilayer P2S3 structures, forming P2S3 nanoribbons or rolling into P2S3 nanotubes, expanding its potential applications for the emerging field of 2D electronics. Then we showed that the hydrolysis reaction is strongly affected by relative humidity. The hydrolysis of CO32- with n = 1-8 water molecules was investigated by ab initio method. For n = 1-5 water molecules, all the reactants follow a stepwise pathway to the transition state. For n = 6-8 water molecules, all the reactants undergo a direct proton transfer to the transition state with overall lower activation free energy. The activation free energy of the reaction is dramatically reduced from 10.4 to 2.4 kcal/mol as the number of water molecules increases from 1 to 6. Meanwhile, the degree of the hydrolysis of CO32- is significantly increased compared to the bulk water solution scenario. The incomplete hydration shells facilitate the hydrolysis of CO32- with few water molecules to be not only thermodynamically favorable but also kinetically favorable. We showed that the chemical kinetics is not likely to constrain the speed of CO2 air capture driven by the humidity-swing. Instead, the pore-diffusion of ions is expected to be the time-limiting step in the humidity driven CO2 air capture. The effect of humidity on the speed of CO2 air capture was studied by conducting CO2 absorption experiment using IER with a high ratio of CO32- to H2O molecules. Our result is able to provide valuable insights to designing efficient CO2 air-capture sorbents. Lastly, the self-assembly mechanism of one-end-open carbon nanotubes (CNTs) suspended in an aqueous solution was studied by molecular dynamics simulations. It was shown that two one-end-open CNTs with different diameters can coaxially self-assemble into a nanocapsule. The nanocapsules formed were stable in aqueous solution under ambient conditions, and the pressure inside the nanocapsule was much higher than the ambient pressure due to the van der Waals interactions between two parts of the nanocapsule. The effects of the normalized radius difference, normalized inter-tube distance and aspect ratio of the CNT pairs were systematically explored. The electric field response of nanocapsules was studied with ab initio molecular dynamics simulations, which showed that nanocapsules can be opened by applying an external electric field, due to the polarization of carbon atoms. This discovery not only shed light on a simple yet robust nanocapsule self-assembly mechanism, but also underpinned potential innovations in drug delivery, nano-reactors, etc

Modelling the self-assembly and structure of carbonaceous nanoparticles

The self-assembly and structure of carbonaceous particles are investigated using molecular modelling methods. This provides a deeper understanding of molecular interactions relevant to pollutant formation and growth in combustion processes and other carbon-based applications. The existing soot particle model, a cluster containing planar pericondensed polycyclic aromatic hydrocarbons (PAHs), is extended to include PAHs of varying sizes. The resulting nanostructures show that the classic core-shell morphology reported experimentally for mature soot particles is not energetically feasible if only considering physical interactions between PAHs. It is proposed that young soot particles present the inverse molecular size partitioning. A detailed survey of the surface properties of heterogeneous PAH clusters is conducted, identifying composition-, size- and temperature-dependent behaviours. A novel stochastic global optimisation method, the Sphere Encapsulated Monte Carlo method, is also developed to allow minimum energy structures of large aromatic systems to be determined at considerably less computational expense than existing methods. The properties of curved PAH molecules are then investigated, and it is hypothesised that their enhanced electronic interactions could play a role in soot particle nucleation. A new intermolecular potential, curPAHIP, is developed to allow the simulation of curved PAHs. Subsequent dynamic clustering studies show that there is a significant increase in particle formation for systems containing curved PAHs and cations, suggesting the importance of these interactions in combustion processes. Further work investigates the structure of clusters containing curved PAHs, and the corresponding influence of cluster size, molecule size and curvature, molecular ratio, and presence of ions. This work develops computational tools useful for examining large systems of aromatic molecules as well as those containing curved species. Detailed studies on nanoparticle nucleation, structure, and surface properties provide valuable information on self-assembly processes crucial to understanding the production and properties of carbonaceous nanoparticles.The Cambridge Trust and King's College, Cambridg

Global optimization of material properties : clusters, solar cells and metal surfaces

Different global optimization tasks have been treated within this thesis. Using an analytic modified embedded atom method (MEAM), a structural-energetic global optimization of lithium and sodium clusters has been performed. With the Aufbau-Abbau procedure we identified up to six most stable isomers for each cluster size N within the size range 2 <= N <= 150, which was followed by a detailed energetic and structural analysis of the obtained Li and Na isomers. For N <= 5 the MEAM partly yields results which are unusual for model potentials, such as planar or linear cluster geometries. Besides the structural optimization of clusters within continuous search spaces, also global property optimizations within discrete search spaces have been performed. Employing a genetic algorithm, a part of our inverse design concept, we optimized organic molecules with respect to their usage within solar cells. Occasionally chemical intuition may help to predict and to understand the substution patterns of the molecules that may be beneficial for solar energy harvesting. Moreover, we extended our inverse design approach to the optimization of the adsorption properties of metal surfaces. The implementation of this project was challenging and associated with several problems. However, also here interesting results could be obtained, which can serve as starting point for further investigations.In dieser Arbeit werden verschiedene globale Optimierungsprobleme behandelt. Unter Verwendung einer analytisch modi fizierten Embedded-Atom-Methode (MEAM), wurden strukturell-energetische globale Optimierungen von Lithium- und Natriumclustern durchgeführt. Für jede Clustergröße N im Bereich 2 <= N <= 150 identi fizierten wir mittels des Aufbau-Abbau-Verfahrens bis zu sechs der stabilsten Isomere, woran sich eine detaillierte energetische und strukturelle Analyse der erhaltenen Li- und Na-Isomere anschloss. Für N <= 5 liefert die MEAM zum Teil, für Modellpotentiale, untypische Ergebnisse, wie flache oder lineare Clustergeometrien. Neben der strukturellen Optimierung von Clustern innerhalb kontinuierlicher Suchräume, wurden auch globale Optimierungen von Materialeigenschaften in diskreten Suchräumen durchgeführt. Unter Verwendung eines genetischen Algorithmus, ein Bestandteil unseres Inverse-Design-Konzeptes, optimierten wir organische Moleküle hinsichtlich ihres Einsatzes in Solarzellen. Chemische Intuition kann vereinzelt hilfreich sein, die für die Nutzung von Sonnenenergie vorteilhaften Substitutionsmuster der Moleküle vorherzusagen und zu verstehen. Zudem erweiterten wir unseren Inverse-Design-Ansatz um die Optimierung der Adsorptionseigenschaften von Metalloberflächen. Die Umsetzung dieses Vorhabens war herausfordernd und mit einigen Problemen verbunden. Jedoch konnten auch hier interessante Ergebnisse erhalten werden, die als Basis weiterer Studien dienen können