Browsing by Subject "Transition state theory"
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Item Materials design via tunable properties(2012-05) Pozun, Zachary David; Henkelman, Graeme; Rossky, Peter J.; Chelikowsky, James R.; Makarov, Dmitrii E.; Mullins, Charles B.In the design of novel materials, tunable properties are parameters such as composition or structure that may be adjusted in order to enhance a desired chemical or material property. Trends in tunable properties can be accurately predicted using computational and combinatorial chemistry tools in order to optimize a desired property. I present a study of tunable properties in materials and employ a variety of algorithms that ranges from simple screening to machine learning. In the case of tuning a nanocomposite membrane for olefin/paraffin separations, I demonstrate a rational design approach based on statistical modeling followed by ab initio modeling of the interaction of olefins with various nanoparticles. My simplified model of gases diffusing on a heterogeneous lattice identifies the conditions necessary for optimal selectivity of olefins over paraffins. The ab initio modeling is then applied to identify realistic nanomaterials that will produce such conditions. The second case, [alpha]-Fe₂O₃, commonly known as hematite, is potential solar cell material. I demonstrate the use of a screened search through chemical compound space in order to identify doped hematite-based materials with an ideal band gap for maximum solar absorption. The electronic structure of hematite is poorly treated by standard density functional theory and requires the application of Hartree-Fock exchange in order to reproduce the experimental band gap. Using this approach, several potential solar cell materials are identified based on the behavior of the dopants within the overall hematite structure. The final aspect of this work is a new method for identifying low-energy chemical processes in condensed phase materials. The gap between timescales that are attainable with standard molecular dynamics and the processes that evolve on a human timescale presents a challenge for modeling the behavior of materials. This problem is particularly severe in the case of condensed phase systems where the reaction mechanisms may be highly complicated or completely unknown. I demonstrate the use of support vector machines, a machine-learning technique, to create transition state theory dividing surfaces without a priori information about the reaction coordinate. This method can be applied to modeling the stability of novel materials.Item Methods, software, and benchmarks for modeling long timescale dynamics in solid-state atomic systems(2014-08) Chill, Samuel T.; Henkelman, GraemeThe timescale of chemical reactions in solid-state systems greatly exceeds what may be modeled by direct integration of Newton's equation of motion. This limitation spawned the development of many different methods such as (adaptive) kinetic Monte Carlo (A)KMC, (harmonic) transition state theory (H)TST, parallel replica dynamics (PRD), hyperdynamics (HD), and temperature accelerated dynamics. The focus of this thesis was to (1) implement many of these methods in a single open-source software package (2) develop standard benchmarks to compare their accuracy and computational cost and (3) develop new long timescale methods. The lack of a open-source package that implements long timescale methods makes it difficult to directly evaluate the quality of different approaches. It also impedes the development of new techniques. Due to these concerns we developed Eon, a program that implements several long timescale methods including PRD, HD, and AKMC as well as global optimization algorithms basin hopping, and minima hopping. Standard benchmarks to evaluate the performance of local geometry optimization; global optimization; and single-ended and double-ended saddle point searches were created. Using Eon and several other well known programs, the accuracy and performance of different algorithms was compared. Important to this work is a website where anyone may download the code to repeat any of the numerical experiments. A new method for long timescale simulations is also introduced: molecular dynamics saddle search adaptive kinetic Monte Carlo (AKMC-MDSS). AKMC-MDSS improves upon AKMC by using short high-temperature MD trajectories to locate the important low-temperature reaction mechanisms of interest. Most importantly, the use of MD enables the development of a proper stopping criterion for the AKMC simulation that ensures that the relevant reaction mechanisms at the low temperature have been found. Important to the simulation of any material is knowledge of the experimental structure. Extended x-ray absorption fine structure (EXAFS) is a technique often used to determine local atomic structure. We propose a technique to quantitatively measure the accuracy of the commonly used fitting models. This technique reveals that the fitting models interpreted nanoparticles as being significantly more ordered and of much shorter bond length than they really are.Item Simulation methodology for the kinetics of solid state atomic systems(2015-08) Duncan, Juliana Rebecca; Henkelman, Graeme; Makarov, Dmitrii E; Stanton, John F; Ganesan, Venkat; Elber, Ron; Hwang, Gyeong SA major challenge in computational chemistry and solid-state atomic systems is overcoming the limitations of molecular dynamics (MD) simulations by utilizing alternative methods to efficiently calculate the rate of chemical reactions and diffusion events. The focus of my dissertation is the following: 1) development of new methods to overcome the time scale limitations of MD; 2) greater understanding of the failures of current methods; and 3) application of the current methods to solid-state atomic systems. Harmonic transition state theory (HTST) is a powerful approximation within the transition state theory (TST) framework that reduces the problem of capturing reaction rates to indentifying the lowest energy first order saddle points. In this work, the biased gradient squared descent (BGSD) saddle point finding method is introduced. BGSD first converts all critical points into global minima by transforming the PES into the gradient squared landscape. A biasing term is added to stabilize critical points at a specified energy levels and destabilize other critical points. BGSD is shown to be competitive with the dimer method in terms of force evaluations required to find a set of low-energy saddle points around a reactant minimum. We use adaptive kinetic Monte Carlo simulations to investigate the transformation of a topologically closed packed (TCP) structure to a cubic phase in molybdenum. Molybdenum is one refractory element added to nickel-based superalloys to improve properties of the material for high-temperature applications. However, when the concentrations of refractory elements are too high TCP phases can form and degrade properties of the material. This study is a first step towards an atomistic description of the transformation of TCP phases to cubic phases. A successful method for accelerating MD simulations is Voter's hyperdynamics approach, which adds a non-negative bias potential to the system's potential energy surface (PES). A novel bias potential is introduced which utilizes a machine learning technique and constructs the bias potential based off of the distance to the ridge. The bias potential is shown to produce boost factors, or computational acceleration, that scale well with dimensionality. HTST does a remarkably good job of capturing reaction rates at low temperatures. However, as the temperature increases results generated by HTST can di ffer from direct MD rates by an order of magnitude. The successes and failures of HTST to capture reactions rates are investigated with the goal of inspiring increased accuracy at less cost than other existing methods.