Computational Study of CO2 Adsorption and Reduction on Doped Graphene Sheets

In recent decades, growing CO2 in the Earth\u27s atmosphere has become a major issue. Thus, it is crucial to reduce the level of concentration of CO2 in the atmosphere. We have investigated the adsorption and reduction of CO2 on metal-doped graphene sheets, through computational methods. The electrochemical reduction of CO2 to CO, CH3OH and CH4 were calculated. Co-doped graphene sheet shows very promising catalytic behavior for CO2 reduction with the highest elemental reaction energy less than 0.7 eV. In addition, tThe reaction pathways reveal the possible rate limiting step could be the removal of the second H2O, CH3OH or CH4 from the doped graphene sheet, depending upon the type of dopant in graphene

Modeling halo and central galaxy orientations on the SO(3) manifold with score-based generative models

Upcoming cosmological weak lensing surveys are expected to constrain cosmological parameters with unprecedented precision. In preparation for these surveys, large simulations with realistic galaxy populations are required to test and validate analysis pipelines. However, these simulations are computationally very costly -- and at the volumes and resolutions demanded by upcoming cosmological surveys, they are computationally infeasible. Here, we propose a Deep Generative Modeling approach to address the specific problem of emulating realistic 3D galaxy orientations in synthetic catalogs. For this purpose, we develop a novel Score-Based Diffusion Model specifically for the SO(3) manifold. The model accurately learns and reproduces correlated orientations of galaxies and dark matter halos that are statistically consistent with those of a reference high-resolution hydrodynamical simulation.Comment: Accepted as extended abstract at Machine Learning and the Physical Sciences workshop, NeurIPS 202

Reinterpreting Fundamental Plane Correlations with Machine Learning

This work explores the relationships between galaxy sizes and related observable galaxy properties in a large volume cosmological hydrodynamical simulation. The objectives of this work are to both develop a better understanding of the correlations between galaxy properties and the influence of environment on galaxy physics in order to build an improved model for the galaxy sizes, building off of the {\it fundamental plane}. With an accurate intrinsic galaxy size predictor, the residuals in the observed galaxy sizes can potentially be used for multiple cosmological applications, including making measurements of galaxy velocities in spectroscopic samples, estimating the rate of cosmic expansion, and constraining the uncertainties in the photometric redshifts of galaxies. Using projection pursuit regression, the model accurately predicts intrinsic galaxy sizes and have residuals which have limited correlation with galaxy properties. The model decreases the spatial correlation of galaxy size residuals by a factor of $\sim$ 5 at small scales compared to the baseline correlation when the mean size is used as a predictor.Comment: 16 pages, 12 figures, MNRA

Relativistic Lee Model and its Resolvent Analysis

We reexamine the relativistic 2+1 dimensional Lee model in light-front coordinates on flat space and on a space-time with a spatial section given by a compact manifold in the usual canonical formalism. The simpler 2+1 dimension is chosen because renormalization is needed only for the mass difference but not required for the coupling constant and the wavefunction. The model is constructed non-perturbatively based on the resolvent formulation [1]. The bound state spectrum is studied through its ``principal operator" and bounds for the ground state energy are obtained. We show that the formal expression found indeed defines the resolvent of a self-adjoint operator--the Hamiltonian of the interacting system. Moreover, we prove an essential result that the principal operator corresponds to a self-adjoint holomorphic family of type-A in the sense of Kato.Comment: 35 page

Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models

The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computationally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.Comment: Accepted as extended abstract at ICML 2022 Workshop on Machine Learning for Astrophysics. Condensed version of arXiv:2204.0707

Computational Study of CO2 Adsorption and Reduction on Doped Graphene Sheets

We have investigated the adsorption and reduction of CO2 on metal-doped graphene sheets, through computational methods. The electrochemical reduction of CO2 to CO, CH3OH and CH4 were calculated. Co-doped graphene sheet shows very promising catalytic behavior for CO2 reduction with the highest elemental reaction energy less than 0.7 eV. In addition, The reaction pathways reveal the possible rate limiting step could be the removal of the second H2O, CH3OH or CH4 from the doped graphene sheet, depending upon the type of dopant in graphene

Electrochemical Reduction of CO2 on Graphene Supported Transition Metals – Towards Single Atom Catalysts

In this study, we have investigated the use of single metal atoms supported on defective graphene as catalysts for the electrochemical reduction of CO2 using the first-principles approach and the computational hydrogen electrode model. Reaction pathways to produce a variety of C1 products CO, HCOOH, HCHO, CH3OH and CH4 have been studied in detail for five representative transition metals Ag, Cu, Pd, Pt, and Co. Different pathways were revealed in contrast to those found for metallic crystalline surfaces and nanoparticles. These single atom catalysts have demonstrated a general improvement in rate limiting potentials to generate C1 hydrocarbons. They also show distinct differences in terms of their efficiency and selectivity in CO2 reduction, which can be correlated with their elemental properties as a function of their group number in the periodic table. Six best candidates for CH4 production are identified by conducting computational screening of 28 d-block transition metals. Ag has the lowest overpotential (0.73 V), and is followed by Zn, Ni, Pd, Pt and Ru with overpotentials all below 1 V. Cu in the supported single atom form shows a strong preference towards producing CH3OH with an overpotential of 0.68 V well below the value of 1.04 V for producing CH4

Electrochemical Reduction of CO2 on Graphene Supported Transition Metals – Towards Single Atom Catalysts

In this study, we have investigated the use of single metal atoms supported on defective graphene as catalysts for the electrochemical reduction of CO2 using the first-principles approach and the computational hydrogen electrode model. Reaction pathways to produce a variety of C1 products CO, HCOOH, HCHO, CH3OH and CH4 have been studied in detail for five representative transition metals Ag, Cu, Pd, Pt, and Co. Different pathways were revealed in contrast to those found for metallic crystalline surfaces and nanoparticles. These single atom catalysts have demonstrated a general improvement in rate limiting potentials to generate C1 hydrocarbons. They also show distinct differences in terms of their efficiency and selectivity in CO2 reduction, which can be correlated with their elemental properties as a function of their group number in the periodic table. Six best candidates for CH4 production are identified by conducting computational screening of 28 d-block transition metals. Ag has the lowest overpotential (0.73 V), and is followed by Zn, Ni, Pd, Pt and Ru with overpotentials all below 1 V. Cu in the supported single atom form shows a strong preference towards producing CH3OH with an overpotential of 0.68 V well below the value of 1.04 V for producing CH4

Electronic And Optical Properties Of TiO2 Nanoclusters and Nanosheets Supported on Silicene

TiO2 is known to be widely used in photocatalytic and photovoltaic applications. TiO2 alone, however, still suffers from the low quantum efficiency due to the electron-hole recombination. Silicene, a 2D monolayer composed of Si atoms, is known to have higher chemical reactivity than graphene. It may be a viable substrate for TiO2. The high conductivity of silicene may help charge separation after the photoexcitation. The composite system of TiO2 and silicene may provide superior properties than the individual entities. In this study I am investigating different TiO2 nanoclusters and nanolayers and their interaction with silicene. The optical and electronic properties of the combined system of TiO2 and silicene were calculated using the Density Functional Theory (DFT). Preliminary results reveal that combined systems do show different characteristics

First-Principles Study of Single Atom Catalyzed Photoelectrochemical Reduction of CO2

Production of synthetic chemical fuels from solar energy is critical for us to meet the globally growing need of energy as the fossil fuels are depleting fast. The greenhouse gas CO2, as the major product of consumption of both fossil fuels and solar fuels, can be used as the feedstock for solar fuels, thereby providing a sustainable way of closing the carbon cycle. The conversion rate of CO2 to fuels is, however, still too low to be practical besides the poor selectivity of products. In this study, we have investigated the use of single metal atoms supported on graphene sheets as catalysts for the photoelectrochemical reduction of CO2 using the first-principles approach. Reaction pathways to produce a variety of products such as CO, HCOOH, HCHO, CH3OH and CH4 will be presented to demonstrate the differences in metals with a focus on their efficiency and selectivity. Potential candidates of better catalytic performance for production of fuels are identified through computational screening