No description available.

No description available.

No description available.

Metal organic frameworks (MOFs) are nanoporous materials composed of inorganic and organic structural building units (SBUs). Over the last several decades, interest in MOFs has grown considerably partially due to their promising capabilities for carbon capture and storage (CCS) technologies. This is mostly due to their tunable pore chemistry, high internal surface area and unique structural diversity. This thesis focuses on computational methods that were used to analyze and organize a database of hypothetical structures to facilitate MOF discovery. The work done is detailed in two main parts. In the first part of the thesis, a topologically diverse hypothetical MOF database, containing over 300,000 structures, is screened using simplified molecular-input line-entry system (SMILES) strings to identify SBUs in each structure. The structures in the database are then renamed according to the SBUs identified by the SMILES strings algorithm. The renaming of the structures allows users to have a good idea of the geometrical and topological distribution of the database. Furthermore, a quick and reliable test is developed to identify structures with incorrect bonding patterns/missing hydrogen. In the second part of the thesis, density functional theory (DFT) - derived charges are generated for each structure in the hypothetical MOF database. Using these charges, the CO₂/N₂ selectivity is calculated and compared with the selectivity values obtained from another charge generating method, split-charge equilibration (SQE), and it is determined that there is good agreement, r = 0.96, between the two methods. A machine learning model is then developed to identify relationships between geometrical features and CO₂/N₂ selectivity.

In recent decades, machine learning techniques have received an explosion of interest in the domain of high-throughput materials discovery, which is largely attributed to the fastgrowing development of quantum-chemical methods and learning algorithms. Nevertheless, machine learning for catalysis is still at its initial stage due to our insufficient knowledge of the structure-property relationships. In this regard, we demonstrate a holistic machine-learning framework as surrogate models for the expensive density functional theory to facilitate the discovery of high-performance catalysts. The framework, which integrates the descriptor-based kinetic analysis, material fingerprinting and machine learning algorithms, can rapidly explore a broad range of materials space with enormous compositional and configurational degrees of freedom prior to the expensive quantum-chemical calculations and/or experimental testing. Importantly, advanced machine learning approaches (e.g., global sensitivity analysis, principal component analysis, and exploratory analysis) can be utilized to shed light on the underlying physical factors governing the catalytic activity on a diverse type of catalytic materials with different applications. Chapter 1 introduces some basic concepts and knowledge relating to the computational catalyst design. Chapter 2 and Chapter 3 demonstrate the methodology to construct the machine-learning models for bimetallic catalysts. In Chapter 4, the multi-functionality of the machine-learning models is illustrated to understand the metalloporphyrin's underlying structure-property relationships. In Chapter 5, an uncertainty-guided machine learning strategy is introduced to tackle the challenge of data deficiency for perovskite electrode materials design in the electrochemical water splitting cell. / Doctor of Philosophy / Machine learning and deep learning techniques have revolutionized a range of industries in recent years and have huge potential to improve every aspect of our daily lives. Essentially, machine-learning provides algorithms the ability to automatically discover the hidden patterns of data without being explicitly programmed. Because of this, machine learning models have gained huge successes in applications such as website recommendation systems, online fraud detection, robotic technologies, image recognition, etc. Nevertheless, implementing machine-learning techniques in the field of catalyst design remains difficult due to 2 primary challenges. The first challenge is our insufficient knowledge about the structure-property relationships for diverse material systems. Typically, developing a physically intuitive material feature method requests in-depth expert knowledge about the underlying physics of the material system and it is always an active field. The second challenge is the lack of training data in academic research. In many cases, collecting a sufficient amount of training data is not always feasible due to the limitation of computational/experimental resources. Subsequently, the machine learning model optimized with small data tends to be over-fitted and could provide biased predictions with huge uncertainties. To address the above-mentioned challenges, this thesis focus on the development of robust feature methods and strategies for a variety of catalyst systems using the density functional theory (DFT) calculations. Through the case studies in the chapters, we show that the bulk electronic structure characteristics are successful features for capturing the adsorption properties of metal alloys and metal oxides. While molecular graphs are robust features for the molecular property, e.g., energy gap, of metal-organics compounds. Besides, we demonstrate that the adaptive machine learning workflow is an effective strategy to tackle the data deficiency issue in search of perovskite catalysts for the oxygen evolution reaction.

In recent years, deep learning technologies have received huge attention and interest in the field of high-performance material design. This is primarily because deep learning algorithms in nature have huge advantages over the conventional machine learning models in processing massive amounts of unstructured data with high performance. Besides, deep learning models are capable of recognizing the hidden patterns among unstructured data in an automatic fashion without relying on excessive human domain knowledge. Nevertheless, constructing a robust deep learning model for assessing materials' structure-property relationships remains a non-trivial task due to highly flexible model architecture and the challenge of selecting appropriate material representation methods. In this regard, we develop advanced deep-learning models and implement them for predicting the quantum-chemical calculated properties (i.e., formation energy) for an enormous number of crystal systems. Chapter 1 briefly introduces some fundamental theory of deep learning models (i.e., CNN, GNN) and advanced analysis methods (i.e., saliency map). In Chapter 2, the convolutional neural network (CNN) model is established to find the correlation between the physically intuitive partial electronic density of state (PDOS) and the formation energies of crystals. Importantly, advanced machine learning analysis methods (i.e., salience mapping analysis) are utilized to shed light on underlying physical factors governing the energy properties. In Chapter 3, we introduce the methodology of implementing the cutting-edge graph neural networks (GNN) models for learning an enormous number of crystal structures for the desired properties. / Master of Science / Machine learning technologies, particularly deep learning, have demonstrated remarkable progress in facilitating the high-throughput materials discovery process. In essence, machine learning algorithms have the ability to uncover the hidden patterns of data and make appropriate decisions without being explicitly programmed. Nevertheless, implementing machine learning models in the field of material design remains a challenging task. One of the biggest limitations is our insufficient knowledge about the structure-property relationships for material systems. As the performance of machine learning models is to a large degree determined by the underlying material representation method, which typically requires the experts to have in-depth knowledge of the material systems. Thus, designing effective feature representation methods is always the most crucial aspect for machine learning model development and the process takes a significant amount of manual effort. Even though tremendous efforts have been made in recent years, the research process for robust feature representation methods is still slow. In this regard, we attempt to automate the feature engineering process with the assistance of advanced deep learning algorithms. Unlike the conventional machine learning models, our deep learning models (i.e., convolutional neural networks, graph neural networks) are capable of processing massive amounts of structured data such as spectrum and crystal graphs. Specifically, the deep learning models are explicitly designed to learn the hidden latent variables that are contained in crystal structures in an automatic fashion and provide accurate prediction results. We believe the deep learning models have huge potential to simplify the machine learning modeling process and facilitate the discovery of promising functional materials.

A real-space time-domain calculation of the frequency-dependent dielectric constant of nonmetallic crystals is outlined and the integrals required for this calculation are computed. The outline is based on time dependent current density functional theory and is partially implemented in the ab initio density functional theory FIREBALL program. The addition of a vector potential to the Hamiltonian of the system is discussed as well as the need to include the current density in addition to the particle density. The derivation of gradient integrals within a localized atomic-like orbital basis is presented for use in constructing the current density. Due to the generality of the derivation we also give the derivation of the kinetic energy, dipole, and overlap interactions.

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In recent years, both hafnia and zirconia have been looked at closely in the quest for a high permittivity gate dielectric to replace silicon dioxide in advanced metal oxide semiconductor field effect transistors (MOSFET). Hafnium dioxide or HfO2 is chosen for its high dielectric constant (five times that of SiO2) and compatibility with stringent requirements of the Si process. As deposited, thin hafnia films are typically amorphous but turn polycrystalline after a post-deposition anneal. To control the phase composition in hafnia films understanding of structural phase transitions is a first step. In this dissertation using first principles methods we consider three phase transitions of hafnia and zirconia: monoclinic to tetragonal, tetragonal to cubic and amorphous to crystalline. Because the high surface to volume ratio in hafnia films and powders plays an important role in phase transitions, we also study the surface properties of hafnia. We discuss the mechanisms of various phase transitions and theoretically estimate the transition temperatures. We find two types of amorphous hafnia and show that they have different structural and electronic properties. The small energy barrier between the amorphous and crystalline structures is found to cause the low crystallization temperature. Moreover, we calculate work functions and surface energies for hafnia surfaces and show the surface suppression of the phase transitions. / text

Paddlewheel complexes are molecules in which two interacting metal atoms are bridged by four chelating ligands. This class of complexes has a large range of electronic variability while keeping a rigid geometric structure. This variability has led to their use as catalysts, strong reductants, anti-tumor agents, and electron transfer agents. This dissertation examines the effects of changing both the dimetal core and the surrounding ligands on the electronic structure properties of the paddlewheel complexes. Examination of Bi₂(O₂CCF₃)₄, a p-orbital dimetal paddlewheel complex, provided a way to probe the orbitals that are important in metal-ligand σ bonding. The b(1g) and b(2u) ligand orbitals of Bi₂(O₂CCF₃)₄ have no dimetal orbital counterpart, unlike the case of the more familiar d-orbital dimetal paddlewheel complexes such as Mo₂(O₂CCF₃)₄. This had the effect of destabilizing these ligand orbitals compared to d-orbital paddlewheel complexes. The ligand a1g orbital in Bi₂(O₂CCF₃)₄ was also destabilized due to nodal differences in the dimetal σ orbital. The unusual coincidence of Mo-Mo σ and π ionization bands is due to a greater amount of ligand character in the Mo-Mo σ orbital compared to its ditungsten analogue, which has separate ionization bands for the σ and π bonds. A series of p-substituted dimolybdenum tetrabenzoate complexes was synthesized and studied by photoelectron spectroscopy in order to further examine the delocalization of electron density from the metals to the ligands in these complexes. A 0.89 eV shift in the δ ionization band was observed from Mo₂(O₂CPh-p-OMe) ₄ and Mo₂(O₂CPh-p-CF₃)₄. Overlap effects are the major factor causing the shift in the δ bond ionization, as the calculated charges on the molybdenum and oxygen atoms did not vary significantly on change of substituent. Molybdenum and tungsten guanidinate paddlewheel complexes have promise as good reducing agents due to their extremely low ionization energies. The solubility of the complexes poses a problem for their widespread adoption for use as reducing agents. Alkyl substituents were added to the complexes to increase their solubility. W₂(TEhpp)₄ was observed to have the lowest ionization energy at 3.71 eV (vertical ionization) and 3.40 eV (onset ionization) of any molecule yet prepared.