Synthesis, characterization, and electronic structure calculations of doped period 4 metal tungstates and their activity towards photodegradation of methylene blue

Access to thesis permanently restricted to Ball State community only.Transition metal tungstates are promising low-cost semiconductor nanomaterials that are photocatalytically active towards the degradation of organic compounds, such as dyes or pollutants. CoWO4, NiWO4, CuWO4, and ZnWO4, as well as binary systems Co1−xNixWO4, Ni1−xCuxWO4, and Cu1−xZnxWO4, were synthesized via a coprecipitation method at 80 °C, followed by calcination in air at 580 °C and characterized by dynamic light scattering, X-ray diffraction, and infrared spectroscopy Photocatalytic tests were conducted by monitoring the semiconductor-mediated degradation of methylene blue under ambient conditions and irradiation with 372±4 nm light. The rates of the photodegradation of the dye were quantified via UV-visible spectroscopy. The introduction of low or high concentrations of dopants affects the electronic structure of the semiconductors and photodegradation rate. Calculations within Vienna Ab initio Simulation Package (VASP) allow for the electronic structure to be probed and are used to help provide insight into the photocatalytic activity trends. In particular, the nature of the valence and conductance band is investigated with regard to its elemental composition. Band gaps of materials are calculated as a function of dopant concentration.Thesis (M.S.

The atomic simulation environment — a python library for working with atoms

The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations

GPAW: open Python package for electronic-structure calculations

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW

Transferable Water Potentials Using Equivariant Neural Networks

<p>Training configurations used in the development of the potential described in the related publication.</p&gt

Graph theory approach to determine configurations of multidentate and high coverage adsorbates for heterogeneous catalysis

AbstractHeterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt3Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH3*) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt3Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems.</jats:p

Transferable Water Potentials Using Equivariant Neural Networks

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor–liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K), vapor–liquid equilibrium properties up to 550 K, the MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations

Meiji at 150 Podcast, Episode 021, Dr. Barbara Molony (Santa Clara University), Dr. Sabine Frühstück (University of California-Santa Barbara), Dr. Hillary Maxson (University of Oregon)

In this episode, Dr. Molony, Dr.Frühstück, and Dr. Maxson trace how gender norms and the position of women and children changed during the Meiji, Taishō, and Shōwa Periods. We deconstruct notions of masculinity, femininity, and childhood, map the unevenness of the “Good Wife Wise Mother” ideology, and debate postwar disruptions of prewar and wartime norms.Arts, Faculty ofHistory, Department ofNon UBCUnreviewedFacult