Development Of New Algorithms For Exploring The Potential Energy Landscape Of Chemical Reactions

The research presented in this dissertation is divided into 5 chapters. In Chapter 2, a method for reducing the number of coordinates required to accurately reproduce a known chemical reaction pathway by applying principal component analysis to a number of geometries along the pathway (expressed in either Cartesian coordinates or redundant internal coordinates) is described and applied to 9 example reactions. Chapter 3 introduces new methods for estimating the structure of and optimizing transition states by utilizing information about the atomic bonding in the reactants and products. These methods are then benchmarked against a standard transition state optimization approach utilizing a test set of 20 reactions, with energies computed at both semiempirical and density functional theory levels of theory. Chapter 4 is a collection of 3 new, alternative approaches (Flowchart Hessian updating, Scaled Rational Function Optimization, Quasi-Rotation coordinate propagation), to handling aspects of a typical Quasi-Newton minimization. These new approaches are then compared to their standard counterparts by optimizing a set of 20 molecules using either ab initio or density functional theory potential energy surfaces. The final two chapters of this thesis focus on the development of a new path optimization framework, the Variational Reaction Coordinate (VRC) method. This framework seeks to improve upon the “chain of states” methods, which minimize the energy of a series of structures while using constraints, fictitious forces or reparameterization schemes to maintain the distribution of points along the path. In the VRC method, a functional representing the energy of the entire reaction is minimized by varying the expansion coefficients of a continuous function used to represent the reaction path. In Chapter 5, an algorithm is outlined along with the discussion and application of constraints and coupling terms that may be used to improve the efficiency and reliability of the method, with analytical test surfaces used to demonstrate the method’s performance. Chapter 6 focuses on the inclusion of redundant internal coordinates and methods for approximating the potential energy surface into the VRC framework, in order to reduce the per-iteration computational cost of the VRC method to something comparable to the “chain of states” approaches so that it may be practically applied to the study of reactions using high accuracy density functional theory and ab initio potential energy surfaces

Implementation of a Z-matrix approach within the SIESTA periodic boudnary conditions code and its application to surface adsorption

We implement a flexible Z-matrix approach in the density functional theory (DFT) periodic boundary conditions code, SIESTA. This allows a mixture of Z-matrix and Cartesian coordinates to be used for geometry specification and optimisation. In addition, geometry constraints in the form of fixed coordinates and fixed linear relationships between coordinates can be specified. A Z-matrix approach in condensed phase calculations can be advantageous, for example in studying molecular adsorption onto a surface, both in terms of flexibility and efficiency.We demonstrate our implementation for the case of thiol adsorption on the Au(111) surface

Preconditioners for the geometry optimisation and saddle point search of molecular systems

A class of preconditioners is introduced to enhance geometry optimisation and transition state search of molecular systems. We start from the Hessian of molecular mechanical terms, decompose it and retain only its positive definite part to construct a sparse preconditioner matrix. The construction requires only the computation of the gradient of the corresponding molecular mechanical terms that are already available in popular force field software packages. For molecular crystals, the preconditioner can be combined straightforwardly with the exponential preconditioner recently introduced for periodic systems. The efficiency is demonstrated on several systems using empirical, semiempirical and ab initio potential energy surfaces

Exploring potential energy surfaces in ground- and excited states

Chemical reactivity of atoms, molecules and ions is governed by their underlying potential energy surface. Calculating the whole potential energy surface within reasonable bounds, is impossible for all but the smallest molecules. Usually, only parts of the full potential energy surface can be studied, namely stationary points and the minimum energy paths connecting them. By comparing energies of stationary points and their separating barriers, conclusions regarding possible reactions mechanism, or their infeasibility, can be drawn. Taking excited states into account leads to further complications, as now multiple potential energy surfaces have to be considered and root flips between different excited states may occur, requiring effective state-tracking. Part II of this thesis describes the required methods to locate stationary points and minimum energy paths on potential energy surfaces, by using surface-walking, chain-of-states optimization and intrinsic reaction coordinate integration. Several approaches to state-tracking are presented in chapter 4. Results of this thesis are presented in Part III, containing two contributions to the field of photochemistry: chapter 12 provides a possible excited-state reaction mechanism for a biaryl cross-coupling reaction and offers a plausible explanation for its high regioselectivity. The second contribution is the development pysisyphus (chapter 13), an external optimizer implemented in python, aware of excited states and thus the core of this thesis. By implementing the state-tracking algorithms outlined in chapter 4 it allows effective and efficient optimizations of stationary points in ground- and excited-states. The performance of pysisyphus is verified for several established benchmark sets. Results for several excited-state optimizations are presented in section 13.3, where pysisyphus shows good performance for the optimization of sizeable transition-metal complexes

Atmospheric Chemistry Modelling of Amine Emissions from Post Combustion CO2 Capture Technology

Emissions from post combustion CO2 capture plants using amine solvents are of concern due to their adverse impacts on the human health and environment. Potent carcinogens such as nitrosamines and nitramines resulting from the degradation of the amine emissions in the atmosphere have not been fully investigated. It is, therefore, imperative to determine the atmospheric fate of these amine emissions, such as their chemical transformation, deposition and transport pathways away from the emitting facility so as to perform essential risk assessments. More importantly, there is a lack of integration of amine atmospheric chemistry with dispersion studies. In this work, the atmospheric chemistry of the reference solvent for CO2 capture, monoethanolamine, and the most common degradation amines, methylamine and dimethylamine, formed as part of the post combustion capture process are considered along with dispersion calculations. Rate constants describing the atmospheric chemistry reactions of the amines of interest are obtained using theoretical quantum chemistry methods and kinetic modeling. The dispersion of these amines in the atmosphere is modeled using an air-dispersion model, ADMS 5. A worst case study on the UK's largest CO2 capture pilot plant, Ferrybridge, is carried out to estimate the maximum tolerable emissions of these amines into the atmosphere so that the calculated concentrations do not exceed guideline values and that the risk is acceptable

Understanding the Heteroatom Effect on the Ullmann Copper-Catalyzed Cross-Coupling of X-Arylation (X = NH, O, S) Mechanism

Density Functional Theory (DFT) calculations have been carried out in order to unravel the governing reaction mechanism in copper-catalyzed cross-coupling Ullmann type reactions between iodobenzene (1, PhI) and aniline (2-NH, PhNH2), phenol (2-O, PhOH) and thiophenol (2-S, PhSH) with phenanthroline (phen) as the ancillary ligand. Four different pathways for the mechanism were considered namely Oxidative Addition–Reductive Elimination (OA-RE), σ-bond Metathesis (MET), Single Electron Transfer (SET), and Halogen Atom Transfer (HAT). Our results suggest that the OA-RE route, involving CuIII intermediates, is the energetically most favorable pathway for all the systems considered. Interestingly, the rate-determining step is the oxidative addition of the phenyl iodide to the metal center regardless of the nature of the heteroatom. The computed energy barriers in OA increase in the order O < S < NH. Using the Activation Strain Model (ASM) of chemical reactivity, it was found that the strain energy associated with the bending of the copper(I) complex controls the observed reactivity

Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally