Slide decks, recorded presentations, posters, and related materials produced by faculty, staff, and students affiliated with Mississippi State University's College of Arts and Sciences.
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Absence of Superconductivity in the lightly doped Hubbard model
Jodie Roberts and Rudolf Torsten Clay
The mechanism of superconductivity (SC) in high critical temperature cuprate superconductors remains an unsolved problem. The simplest electronic model of cuprate superconductors is the one band Hubbard model. It models copper atom positions with lattice sites, omitting oxygen atoms for simplicity, and separates the Hamiltonian into hopping (t,t) and interaction (U) components. The simplest Hubbard model only considers nearest neighbor hopping, t. To account for overlap between oxygen orbitals also requires next nearest neighbor hopping, t'. Exact solutions of the model are computationally prohibitive to find for large systems. Quantum Monte Carlo (QMC) methods such as Constrained Path Monte Carlo (CPMC) can be used in cases where exact methods cannot. Constraining the imaginary time path removes the Fermion sign problem caused by sign degeneracy of Slater determinants. However, an additional approximate technique known as back propagation must also be used to measure any quantity besides the energy within CPMC. A newly proposed released constraint measurement method instead releases the path constraint of CPMC for short intervals. This is more accurate but reintroduces the sign problem. We present the first calculations of superconducting pair-pair correlations in the Hubbard model using the released constraint technique. Our results show that in general back propagation tends to underestimate long- range superconducting pairing in the Hubbard model. Recent work using CPMC has suggested that SC does exist in the lightly doped two-dimensional Hubbard model. Our results show that superconducting pair-pair correlations continuously weaken with increasing U, suggesting that SC is not present.
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Anomaly Detection in Material Images Using Scan Statistics
Kaitlyn Anderson, Asanka Duwage, and Tung-Lung Wu
Nanoproducts are a growing sector due to their unique properties and wide range of applications across industries. However, nanomaterial production is a complex process that requires a high degree of precision, making it challenging to ensure consistent quality at a large scale. Minor defects can significantly alter their functional properties and overall performance, making accurate detection of defects crucial for maintaining and controlling nanomaterial properties. To address these challenges, this project applies scan statistics to detect localized defects in scanning election microscope images of nanofibrous materials. We implemented both square and circular scanning windows of varying sizes to identify clusters in the images. By generating null distributions through permutation testing, we assessed the statistical significance of detected regions, leading to reliable identification of anomalies. For each image, we identified the most effective scan window by selecting the size that minimized the p-value, allowing us to adapt the detection process to the unique spatial features of each image and effectively address different types of anomalies. Overall, this approach provides a robust and adaptable method for automated anomaly detection, with the potential to enhance quality control in nanomaterial manufacturing.
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Computational Investigation of Substituent Effects on Zirconium Pincer Complexes via Density Functional Theory Methods
Anna Constable, Garrett M. Wells, Samuel D. Juarez Escamilla, Thedford K. Hollis, and Charles Edwin Webster
Organic light-emitting diodes (OLEDs) have emerged as a promising technology for displays due to their high efficiency and superior color performance. The primary objective of this study was to investigate the tunability of zirconium carbene "pincer" complexes for potential use in OLED applications. Specifically, we aim to understand how changes in coordinated ligands and their substituents influence the electronic structure and spectral properties. A series of computational tests were conducted using density functional theory (DFT) to optimize the ground-state geometries and time-dependent density functional theory (TD-DFT) to optimize the excited-state geometries. TD-DFT calculations are also used to predict absorption and emission spectra. The effects of ligand variation and the simulated spectra will be discussed.
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Efficient Image Denoising Models with Anderson Acceleration using Finite Difference Methods
Amanda E. Diegel, Spence Hanegan, Hyeona Lim, and Hoang Tran
Current image denoising algorithms based on variational methods can suffer from slow convergence or no convergence due to high nonlinearity of the images. To speed up the convergence of denoising, we apply Anderson acceleration to the fixed-point image denoising problem. Anderson acceleration is an algorithmic method for reducing the number of fixed-point iterations necessary for convergence. It involves using weighted updates to each iteration based on the weighted residuals from past iterations, or history. By using finite difference methods, we can approximate the gradient and higher-order partial derivatives at points on the image. We then use these approximations to create matrix equations to solve for a denoised image. By iterating and applying Anderson Acceleration, we achieve a faster convergence of image denoising. This method is tested and compared to the fixed-point method and other conventional denoising methods using peak signal to noise ratio (PSNR).
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Finite Element Methods with Anderson Acceleration and its Application to Image Denoising
Amanda E. Diegel, Spence Hanegan, Hyeona Lim, and Hoang Tran
Image denoising is an important computational tool with applications in the medical, material science, and defense fields where CT-scans have a lot of noise that degrades quality and clearness. While there are several methods of solving image denoising problems, the one we focused on is total variation where we solve a difficult nonlinear partial differential equation that minimizes noise. There are also many numerical methods to find an approximate solution to this nonlinear partial differential equation, but the one we focus on is the finite element method. In addition, we used a fixed-point iteration method to handle the nonlinearity and obtain convergence. But the main problem arises in the number of iterations needed to achieve convergence due to the complexity of the nonlinear equations. So, we propose implementing Anderson Acceleration to speed up the fixed-point iteration method. In addition, we propose adding length and angle filtering to Anderson Acceleration to reduce redundant data and get convergence quicker. We used MATLAB along with FELICTY: Finite Element Implementation and Computational Interface Tool for You toolbox to execute the computations.
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Virtual screening of peptides that can prevent insulin aggregation
Thanh Tien Dao, Bidisha Sengupta, and Steven Gwaltney
Diabetes is a growing health concern, with almost 3% of the population of the United States using insulin injections to control blood sugar levels. Insulin is prone to aggregation during storage and injection. The toxic products of aggregation can cause an increase in the required dosage to achieve the desired therapeutic effect. The driving intermediate of aggregation is believed to be a partially folded insulin, derived from the insulin monomer. We hypothesize that stabilizing the insulin monomer with a peptide may prevent this unfolding process and subsequent aggregation. However, the space of all possible peptides is impossibly large to study systematically. Therefore, we have generated a set of 2,000 randomly chosen 20-mer peptides. We have generated 3D structures of the proposed peptide sequence using AlphaFold 2 and have utilized the peptide-protein docking software Autodock CrankPep (ADCP) to determine how each peptide binds to the insulin monomer. Our results show that different peptides have drastically different binding geometries and binding energies. The next step will be to use the results of the docking studies to train a neural network that can predict the binding of any peptide sequence to the insulin monomer. Additionally, molecular dynamics simulations will be run on promising sequences, to identify short peptides that can prevent insulin aggregation.
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A docking Analysis of reactivators for sarin-inhibited acetylcholinestrase can aid in screening candidate compounds
Ryan Pirger and Steven Gwaltney
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
Organophosphate (OP) poisoning disrupts nerve signaling by inhibiting acetylcholinesterase (AChE), an essential enzyme. Current oxime-based treatments for OP poisoning lack efficacy in severe cases due to blood-brain barrier (BBB) impermeability. A prior machine learning study identified promising AChE reactivators with BBB permeability and synthetic feasibility. Our study employed in silico docking simulations using AutoDock to evaluate the interactions between these potential reactivators and a sarin-inhibited human AChE model. The 35 compounds proposed in the earlier study, along with five known good AChE reactivators, were docked against a model of human AChE that was inhibited by the OP nerve agent sarin. While all the positive control molecules yielded good docking results, none of the newly proposed compounds did. The results of this docking analysis will inform the development of novel OP antidotes capable of reaching the brain and effectively reactivating AChE in sever poisoning scenarios. We suggest adding a docking screening to any future protocol designed to generate potential reactivators.
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Alkylidyne Molybdenum formation from Lithiated Transmetalation: A Computational Insight
Niles Wahlin, Nghia Le, and Charles Edwin Webster
Alkylidyne complexes have garnered significant interest for advances in catalysis in various industrial processes, such as Fischer-Tropsch synthesis and alkyne metathesis. Recently, the alkylidyne complex [N3N]MoCH was synthesized by reacting [N3N]MoCI with lithiated methanoanthracene. Despite the structural similarity between norbornadiene and methanoanthracene, as well as the overall exergonic nature of the reactions, the reaction ony proceeds experimental with lithiated methanoanthracene. To shed light on this transformation, which involves multiple spin states of various short-lived intermediates, we report a computational study elucidating the structure-reactivity relationship of lithiated cyclic reagents. The reaction proceeds through a transmetallation step followed by an endergonic carbon transfer. The carbon transfer is expected to occur via a stepwise mechanism, breaking two C-C bonds separately. The short-lived intermediate after transmetallation can potentially be observed through absorption spectroscopy; thus, simulated spectra and Natural Transition Orbital (NTO) analysis are performed for reference. The proposed mechanism from the computational results suggests a switch in spin states from triplet to singlet during the reaction. We extend our study by examining the reaction between a molybdenum complex wth a pyridine-based PNP pincer ligand (PNP = 2,5-bis(di-tert-butylphosphinomethyl)pyrrolide) and lithiated methanoanthracene.
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Continuous Data Assimilation for Two-Phase Flow
Mary H. Graveman and Amanda E. Diegel
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
We propose a numerical approximation method modeling two-phase flow (via the Cahn-Hilliard equation) that incorporates data to achieve long-time accuracy. The underlying numerical method utilizes the Galerkin finite element method for spatial discretization and a method known as continuous data assimilation to incorporate the known data. We demonstrate the method is long-time stable and long-time accurate provided enough data measurements are incorporated into the simulation, overcoming possibly inaccurate initial conditions. Numerical experiments illustrate the effectiveness of the method on a benchmark test problem. All computations are completed in MATLAB.
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Developing Machine Learned Interatomic Potentials for Hydrogen Dissociation over Molybdenum Phosphide
Jeremy Lugo, John Michael Lane, Woodrow N. Wilson, and Neeraj Rai
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
Molybdenum phosphides are promising catalysts for biomass conversion, particularly in the hydrogenation of organic molecules. Hydrogen dissociation on the surface of such catalysts is a critical step in the process. Traditional computational methods such as density functional theory (DFT) can provide valuable insights into hydrogen-catalyst interactions but are limited by their high computational cost and inefficiency for large systems and long time scales. Machine learning has emerged as a solution to these limitations. Machine learned interatomic potentials (MLIPs), trained on ab initio molecular dynamics (AIMD) simulations, enable the development of molecular dynamics (MD) for much larger systems over much longer time scales, with significantly reduced computational cost. This allows more complex interactions and rare reactive events to be observed that would otherwise be inaccessible with traditional methods. In this study, MACE, a message passing neural network, is employed to develop MLIPs for the dissociation of hydrogen on molybdenum phosphide surfaces. Utilizing MACE results in highly accurate MD simulations with time scales on the order of 1,000 times longer than those achievable with AIMD. These MD simulations provide key insights about the system, including preferred adsorption sites and atomic interactions and serve as a bridge to studying biomass conversion. This study demonstrates the efficacy of machine learning for catalytic research, illustrating the robust and efficient predictive capabilities of MLIPs and underscoring the potential of machine learning techniques for the efficient discovery, optimization, and understanding of new catalysts.
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New Weighting Parameters for Non-Local Means based Denoising Algorithm
Ely Carroll and Hyeona Lim
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
Current image denoising techniques can be broadly categorized into two types: variational methods which use minimizing functionals, and filtering methods via pixel averaging. Specifically, the non-local means (NLM) filtering based image denoising algorithm involves replacing each pixel value by the weighted averages of all pixels in the entire image. The basic non-local means algorithm is predicated on manually inputting a parameter to determine the weight of each pixel. We introduce and analyze a new method of implementing the weight factors, based on small variances in the pixel values to identify noise. The new method will identify newly classified noisy pixels, then while comparing blocks of pixels for similarity it is possible to disregard noisy pixels and gain a clearer comparison of structure for weighting. The new method is numerically tested and compared to conventional NLM based methods via the peak signal to noise ratio (PSNR), and visual comparison. The results show that the new method is preferable to the current NLM methods.
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Repulsive Coulomb interactions enhance Superconductivity Selectively at Density 0.5 per site
Jeremy Padvorac and Rudolf Torsten Clay
Poster created as part of the Center for Computational Sciences' Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science and presented at the 2024 Undergraduate Research Showcase.
The globally accepted Bardeen-Cooper-Schrieffer (BCS) theory explains the pairing mechanism in low-temperature elemental superconductors but fails to explain pairing in high critical temperature superconductors. Unconventional superconductors with higher critical temperatures require moreexotic pairing mechanisms considering electron-electron interactions, such as spin-fluctuated-mediated pairing. Many-body calculations within the Hubbard model, which includes short-range electron-electron repulsion, have shown that superconducting pair correlations are enhanced when the density of carriers is close to one-half per orbital, a characteristic density in many unconventional superconductors. However, questions related to the accuracy of previous calculations remain. In this work, we employed a more accurate numerical method, Self-Consistent Constrained Path Quantum Monte Carlo (SC-CPMC), to check the accuracy of CPMC calculations without the self-consistent optimization of the trial wavefunction. By eliminating the artificial lattice symmetry breaking previously used, we restored the intrinsic symmetry of the lattice. This study also investigated effects of a larger Hubbard U. Our results confirm that the Hubbard U significantly enhancessuperconducting pair-pair correlations for carrier densities close to 0.5 per site.
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The Impact of Single vs. Poly Victimization by Maltreatment Type on Pre-Treatment PTSD Scores
Ashley G. O'Donnell, Sabrina DiCarlo, and Arazais D. Oliveros
Studies show that an estimated 21-50% of children who have experienced maltreatment will develop post-traumatic stress disorder (PTSD) within their lifetime (Schuck & Widom 2019). Research suggests that the type and number of exposures to trauma may influence symptom severity. Further, trauma symptom scores of children who experienced child sexual abuse (CSA) were higher among the children who endorsed poly-victimization, meaning when CSA was combined with another form of maltreatment (Racine et al 2022). This study seeks to examine the impact of poly-victimization and combined types of maltreatment on children’s PTSD scores in an archival dataset from a child advocacy center (CAC) serving children exposed to various forms of trauma. Participants from the overall child sample (N = 721) who have pre-treatment PTSD scores (n = 290) will be analyzed. The analyzed sample includes 83 minors exposed to poly-victimization, 175 exposed to single victimization, and 32 where the trauma type was missing from the dataset. Specifically, the following hypotheses will be tested: (1) victims of poly-victimization will have higher PTSD scores than victims of single victimization; (2) among participants with poly-victimization, those with a combination of sexual abuse and physical abuse will have the highest PTSD scores compared to other combinations. Results will be discussed in the context of current referral pathways for child advocacy centers.
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History of Childhood Physical Abuse and Current Risky Drinking: The Role of Coping Motives
H. Addison Lowery, Deepali M. Dhruve, and Arazais D. Oliveros
A childhood history of physical abuse is associated with increased risky drinking in adolescence, and a two-fold risk of alcohol dependence. Research on substance use disorders (SUD) suggests that people who drink with the motivation to cope with their emotions face a greater risk of SUD. Given that childhood physical abuse is associated with emotional difficulties, the current study examined the interplay between childhood physical discipline (inclusive of abuse) and coping motives in predicting risky drinking. Specifically, we expected ratings of childhood physical discipline and current risky drinking to be associated positively and for that relation to be mediated (explained) to some extent by their motivation to drink for emotional coping.
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An in situ Electrochemical Study of Electrodeposited Nickel and Nickel-Yttrium Oxide Composite Using Scanning Electrochemical Microscopy
David O. Wipf, L. Diaz-Ballote, and L. Veleva
Electrodeposited nickel and nickel-yttrium oxide composite samples were studied in situ using scanning electrochemical microscopy (SECM). The monitored probe currents in phosphate-citrate buffer (pH 4.2) in the presence or absence of Ru(NH3)63+ as an oxidizing mediator near the Ni surface show that the SECM is a useful tool for study of the electrochemical activity of heterogeneous metal surface at micrometer scales. The SECM ultramicroelectrode probe tip provides information about the shape, activity and location of particles, such as Y2O3 introduced (co-deposited) in the Ni-matrix of the composite. Experiments show that the Ni-matrix in the composite coating is more active than the pure Ni-coating. This fact is expected, because of texture changes in the Ni structure upon introduction (by co-deposition) of Y2O3 particles. In the absence of mediator in the solution, the electrochemical activity of heterogeneous metal surface at a micro-level is investigated by using O2 concentration changes. The rate of reaction for O2 reduction was found to locally vary at electrodes floating at the open-circuit potential (o.c.p) when compared to an electrode potentiostatically polarized at a more positive potential than the o.c.p. This behavior suggests that local anode and cathode regions are being observed at the o.c.p. sample.
