Determining Material Structures and Surface Chemistry by Genetic Algorithms and Quantum Chemical Simulations

With the advent of modern computing, the use of simulation in chemistry has become just as important as experiment. Simulations were originally only applicable to small molecules, but modern techniques, such as density functional theory (DFT) allow extension to materials science. While there are many valuable techniques for synthesis and characterization in chemistry laboratories, there are far more materials possible than can be synthesized, each with an entire host of surfaces. This wealth of chemical space to explore begs the use of computational chemistry to mimic synthesis and experimental characterization. In this work, genetic algorithms (GA), for the former, and DFT calculations, for the latter, are developed and used for the in silico exploration of materials chemistry. Genetic algorithms were first theorized in 1975 by John Holland and over the years subsequently expanded and developed for a variety of purposes. The first application to chemistry came in the early 1990’s and surface chemistry, specifically, appeared soon after. To complement the ability of a GA to explore chemical space is a second algorithmic technique: machine learning (ML) wherein a program is able to categorize or predict properties of an input after reviewing many, many examples of similar inputs. ML has more nebulous origins than GA, but applications to chemistry also appeared in the 1990’s. A history perspective and assessment of these techniques towards surface chemistry follows in this work. A GA designed to find the crystal structure of layered chemical materials given the material’s X-ray diffraction pattern is then developed. The approach reduces crystals into layers of atoms that are transformed and stacked until they repeat. In this manner, an entire crystal need only be represented by its base layer (or two, in some cases) and a set of instructions on how the layers are to be arranged and stacked. Molecules that may be present may not quite behave in this fashion, and so a second set of descriptors exist to determine the molecule’s position and orientation. Finally, the lattice of the unit cell is specified, and the structure is built to match. The GA determines the structure’s X-ray diffraction pattern, compares it against a provided experimental pattern, and assigns it a fitness value, where a higher value indicates a better match and a more fit individual. The most fit individuals mate, exchanging genetic material (which may mutate) to produce offspring which are further subjected to the same procedure. This GA can find the structure of bulk, layered, organic, and inorganic materials. Once a material’s bulk structure has been determined, surfaces of the material can be derived and analyzed by DFT. In this thesis, DFT is used to validate results from the GA regarding lithium-aluminum layered double hydroxide. Surface chemistry is more directly explored in the prediction of adsorbates on surfaces of lithiated nickel-manganese-cobalt oxide, a common cathode material in lithium-ion batteries. Surfaces are evaluated at the DFT+U level of theory, which reduces electron over-delocalization, and the energies of the surfaces both bare and with adsorbates are compared. By applying first-principles thermodynamics to predict system energies under varying temperatures and pressures, the behavior of these surfaces in experimental conditions is predicted to be mostly pristine and bare of adsorbates. For breadth, this thesis also presents an investigation of the electronic and optical properties of organic semiconductors via DFT and time-dependent DFT calculations

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century