Computational Chemistry Commons^™

Computational Insights Into Nucleosome Dynamics In Epigenetics Using Molecular Dynamics Simulations, Rutika Patel

Dissertations, Theses, and Capstone Projects

Nucleosome core particles (NCP) are the building blocks that form a highly organized and compact chromatin structure. Nucleosomes package DNA in the nucleus of eukaryotic cells. The NCP consists of about 147 base pairs of DNA wrapped around the histone octamer, with 1.65 superhelical turns in a left-handed manner. The histone octamer is composed of two copies of H3, H4, H2A, and H2B. Together with histone H1 and linker DNA, they further assemble into a higher-order chromatin structure. The nucleosome complex is stabilized by electrostatic interactions between positively charged histone residues and the negatively charged DNA backbone. To effectively access …

Basis Design For Electronic Structure And Beyond, Weishi Wang

Dartmouth College Ph.D Dissertations

At the intersection of quantum physics, quantum chemistry, and materials science, electronic structure is the study of electrons in solid-state and molecular systems. Electronic-structure computation relies on discretizing the many-electron Hamiltonian with a finite single-particle basis set. However, basis-set construction is conventionally treated as an ad hoc preprocessing step. This thesis develops an expressive and flexible framework for active, system-oriented basis-set design and numerical modeling strategies that treat basis functions as tunable representations to encode electronic ground-state information.

We first introduce a multi-layered, differentiable basis-construction framework that embeds a set of primitive parameters into mixed-contracted Gaussian-type orbitals. We then develop …

Simulating Chemiluminescence Spectra Of Oh Radical Using Quantum Dynamical Simulations, Triet M. Cao

Research from the Berry Summer Thesis Institute, 2025

Chemiluminescence is a phenomenon of light emission that occurs when molecules transition from an excited state to the ground state. This phenomenon is widely utilized in applications ranging from quantifying product yields in chemical reactions to biological imaging and atmospheric chemistry studies. It has also been used in many crucial applications in the scarce conditions of heat and electricity, such as light sources for astronomy discovery or deep-sea diving. A notable example of such reactions is combustion, where highly reactive hydroxyl radicals (OH) are products. These radicals exhibit a chemiluminescence spectrum because of the interaction between their ground state and …

Designing A Data Collection And Visualization Toolkit For Scalable Tensor Algebra In Quantum Chemistry Applications, Epiya J. Ebiapia

LSU Master's Theses

Large-scale quantum chemistry computations, such as those executed with the Tensor Algebra for Many-body Methods (TAMM) framework, require careful configuration of runtime parameters to achieve high performance and cost efficiency in high-performance computing (HPC) and cloud environments. Without effective performance analysis tools, researchers risk inefficient use of computational resources, leading to longer runtimes and higher costs.

To address this challenge, this thesis presents the design and implementation of a performance profiling and visualization toolkit for TAMM, developed as part of the DOE TEC4 project in collaboration with Pacific Northwest National Laboratory, Microsoft, and Louisiana State University. The toolkit collects detailed …

Characterization Of Sars-Cov-2 Replication And Transcription Complexes Via Structural And Evolutionary Approaches, Amelie Ghirardo, Ben Shabatian, Avishai Aghelian, Kyle Tau, Eleonora Gianti

Undergraduate Research

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused around 700M cases and over 7M COVID-19-related deaths recorded worldwide (World Health Organization, March 2025). Aiming to effectively combat this and other disease-causing Coronaviruses (CoV), unprecedented research efforts led to the development of new vaccines and antiviral therapies. Due to emergence of variants of concern (VOCs) with increased transmissibility, immune evasion from vaccination, and potential to resist the available treatments, SARS-CoV-2 continues to represent a major threat to global health. Hence, there is a pressing need to discover new antivirals with broad-spectrum efficacy against multiple SARS-CoV-2 variants and related CoVs. This project …

A Computational Method For Detecting Compound Promiscuity In Early-Stage Pharmaceutical Discovery, John Allen Ringer

Computer Science ETDs

Modern drug discovery and chemical biology research relies heavily on analyzing bioassay data. One of the many challenges in bioassay data analysis is identifying false trails, i.e., chemical compounds which initially appear to have desirable activity but are found to be problematic upon further investigation. Badapple (the BioAssay-Data Associative Promiscuity Pattern Learning Engine) was created over ten years ago to help researchers identify promiscuous compounds and thus avoid a common source of these false trails. Through an effort involving software engineering, cheminformatics, and biomedical data science we have developed Badapple 2.0, which incorporates updated assay records and expanded data semantics. …

Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee

Chemical Engineering Undergraduate Honors Theses

This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …

In Memoriam - Nora Sabelli: Master Orchestrator Of Grant Programs And Mentor For Advancing The Interdisciplinary Learning Sciences Field, Eric Hamilton, Jeremy Roschelle, Roy Pea, Barbara Means, Louis Gomez, Kim Gomez, Nancy Butler Songer

Education Division Scholarship

On Friday, September 6, 2024, the learning sciences field lost a giant in Dr. Nora Sabelli, 87 years old, a personal mentor to many researchers and an inspiration to so many learning scientists and STEM leaders. Nora’s first professional career was as a computational chemist, and later she became a passionate leader in research for improving STEM education. Nora’s time as a senior program officer at the National Science Foundation’s (NSF) Education and Human Resources (EHR) directorate was legendary; she was a force of nature who reshaped funding priorities for stronger science and a stronger connection of science to education …

Individual And Collective Properties Of Tunable Photochemical Belousov-Zhabotinsky Micro-Reactors, Kudakwashe Benedict Shumba

Graduate Theses, Dissertations, and Problem Reports (ETD)

Cell-like model chemical systems are powerful tools that can be used to explore the role of intercellular coupling on population level behaviors in communities of biological cells. Firstly, we present a new method for fabricating such micro-reactors using the photosensitive Belousov–Zhabotinsky (BZ) reaction system employed in silica microparticles. These BZ micro-reactors have a tunable response to photochemical coupling, varying from a fully excitatory response to a fully inhibitory response. Their response can be tuned through variations in either the reactive mixture or, on an individual micro-reactor level, by changes in the synthesis temperature used during the fabrication of the silica …

Use Of Computational Methods To Determine The Relative Thermodynamic Stability Of Myricetin, Quercetin And 4-Methylesculetin For Use As Spin Traps, Cole Walker

Electronic Theses and Dissertations

The research conducted focused on the oxidative intermediates of antioxidants known as Myricetin, Quercetin, and 4-Methylesutin by calculating the reaction mechanism. During this research, computational quantum chemistry on selected parts for the 3 antioxidants previously mentioned were performed to find the most stable intermediate to act as a spin trap. The hypothesis is that the intermediates found can act as spin traps which will allow longer preservation of a free radical to be identified using Electron Paramagnetic Resonance (EPR) spectroscopy. This may be because the buildup of excess free radicals in a system leads them to cause damage down to …

Inchi Isotopologue And Isotopomer Specifications, Hunter N. B. Moseley, Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Emma L. Schymanski

Markey Cancer Center Faculty Publications

This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically‑resolved chemi‑ cal entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically‑ resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry.

Scientific contribution

This new extension to the InChI standard …

Theoretical Spectroscopic Predictions Of Electronically Excited States, Noah R. Garrett

Honors Theses

The quest for faster computation of anharmonic vibrational frequencies of both ground and excited electronic states has led to combining coupled cluster theory harmonic force constants with density functional theory (DFT) cubic and quartic force constants for defining a quartic force field (QFF) utilized in conjunction with vibrational perturbation theory at second order (VPT2). This work shows that explicitly correlated coupled cluster theory at the singles, doubles, and perturbative triples level [CCSD(T)-F12] provides accurate anharmonic vibrational frequencies and rotational constants when conjoined with any of B3LYP, CAM-B3LYP, BHandHLYP, PBE0, and ωB97XD for roughly one-quarter of the computational time of the …

Learning, Optimizing, And Simulating Fermions With Quantum Computers, Andrew Zhao

Physics & Astronomy ETDs

Fermions are fundamental particles which obey seemingly bizarre quantum-mechanical principles, yet constitute all the ordinary matter that we inhabit. As such, their study is heavily motivated from both fundamental and practical incentives. In this dissertation, we will explore how the tools of quantum information and computation can assist us on both of these fronts. We primarily do so through the task of partial state learning: tomographic protocols for acquiring a reduced, but sufficient, classical description of a quantum system. Developing fast methods for partial tomography addresses a critical bottleneck in quantum simulation algorithms, which is a particularly pressing issue for …

Development Of On-The-Fly Quasi-Steady State Approximation For Chemical Kinetics In Cfd, Abhinav Balamurugan

Doctoral Dissertations and Master's Theses

This study analyzes the feasibility of On-The-Fly Quasi-Steady-State Approximation (OTF-QSSA) application for solving chemical kinetics within Computational Fluid Dynamics (CFD) simulations, aiming to reduce the computational demand of detailed mechanisms. An algorithm that dynamically identifies and designates Quasi-Steady-State (QSS) species at specific grid locations and instances during the simulation was developed. With this information, our method pseudo-delays the advancement of concentrations for these QSS species—effectively setting their rate of concentration change to zero for a set number iteration before updating using the detailed mechanism and thereby omitting the computationally intensive processes typically required for their calculation during those skipped iteration. …

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …

De Novo Drug Design Using Transformer-Based Machine Translation And Reinforcement Learning Of An Adaptive Monte Carlo Tree Search, Dony Ang, Cyril Rakovski, Hagop S. Atamian

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

The discovery of novel therapeutic compounds through de novo drug design represents a critical challenge in the field of pharmaceutical research. Traditional drug discovery approaches are often resource intensive and time consuming, leading researchers to explore innovative methods that harness the power of deep learning and reinforcement learning techniques. Here, we introduce a novel drug design approach called drugAI that leverages the Encoder–Decoder Transformer architecture in tandem with Reinforcement Learning via a Monte Carlo Tree Search (RL-MCTS) to expedite the process of drug discovery while ensuring the production of valid small molecules with drug-like characteristics and strong binding affinities towards …

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

Enhanced Quantum Chemistry With Machine Learning, Brock Dyer

Physics and Astronomy Summer Fellows

This file is a catalogue of the relevant quantum mechanical and computer programming topics that I learned during the summer which will be helping me to generate an artificial intelligence that will be able to perform computational chemical calculations at a much faster rate and comparable or better accuracy than current methods.

Chatgpt As Metamorphosis Designer For The Future Of Artificial Intelligence (Ai): A Conceptual Investigation, Amarjit Kumar Singh (Library Assistant), Dr. Pankaj Mathur (Deputy Librarian)

Library Philosophy and Practice (e-journal)

Abstract

Purpose: The purpose of this research paper is to explore ChatGPT’s potential as an innovative designer tool for the future development of artificial intelligence. Specifically, this conceptual investigation aims to analyze ChatGPT’s capabilities as a tool for designing and developing near about human intelligent systems for futuristic used and developed in the field of Artificial Intelligence (AI). Also with the helps of this paper, researchers are analyzed the strengths and weaknesses of ChatGPT as a tool, and identify possible areas for improvement in its development and implementation. This investigation focused on the various features and functions of ChatGPT that …

Computer Simulation Of The Light Absorption Band Of The Jumping Spider Isorhodopsin, Noah Zoldak

Honors Projects

In order to simulate the photoisomerization of the 9-cis Jumping Spider Isorhodopsin (JSiR-1) it is necessary to first simulate its light-absorption band. Here we report on the absorption band simulated using protein models constructed using the advanced Automatic Rhodopsin Modeling (a-ARM) program. A population of S₀ models was created and the corresponding S₀ to S₁ transitions were determined for each member of the resulting population. The calculation resulted in a Gaussian plot showing that the wavelength of the absorption maximum of 560 nm (a violet color) that is consistent, but red-shifted, with respect the experimentally observed value.

Hydrogen Bonding In Small Model Peptides; The Dft And Mp2 Study, Gracie Smith, Martina Kaledin

Symposium of Student Scholars

Formamide is a small model compound for the study of the peptide bond. The peptide bond links amino acids together, specifies rigidity to the protein backbone, and includes the essential docking sites for hydrogen-bond-mediated protein folding and protein aggregation, namely, the C=O acceptor and the N-H donor parts. Therefore, the infrared C=O (amide-I) and N-H (amide-A) vibrations provide sensitive and widely used probes into the structure of peptides. This computational chemistry work, we study hydrogen bonds in formamide dimer isomers. We evaluate the accuracy of the density functional theory (DFT) and many-body perturbation theory to the 2^nd order (MP2) …

Turning Density Functional Theory Calculations Into Molecular Mechanics Simulations : Establishing The Fluctuating Density Model For Rna Nucleobases, Christopher A. Myers

Legacy Theses & Dissertations (2009 - 2024)

Molecular mechanics (MD) simulations and density functional theory (DFT) have been the backbone of computational chemistry for decades. Due to its accuracy and computational feasibility, DFT has become the go-to method for theoretically predicting interaction energies, polarizability, and other electronic properties of small molecules at the quantum mechanical level. Although less fundamental than DFT, molecular mechanics (MM) algorithms have been just as influential in the fields of biology and chemistry, owing their success to the ability to compute measurable, macroscopic quantities for systems with tens of thousands to hundreds of thousands of atoms at a time. Nevertheless, MD simulations would …

Computational Investigations Into Astrochemical Inorganic Oxides, Ammonia Borane, And Genetic Algorithms, E. Michael Valencia

Honors Theses

The formulations of quantum mechanics in the early 1900s were exciting theoretical discoveries, but were not practical to apply until the advent of computers and the subsequent computational methods in 1951. With the introduction of tractable simplifications, procedures such as Hartree-Fock allowed for determination of properties of non-trivial systems. Presently, huge leads of computational power have allowed for extremely precise, quantitative work that can be applied to the human body, synthesis, or even astrochemical processes. This thesis presents works concerning 1) the history of quantum mechanics; 2) a brief primer on computational chemistry and its methods; 3) inorganic oxides in …

Computer Simulation Of Raman Spectra And Mode Assignment: Application To Methane, Oluwaseun Omodemi, Ciara Tyler, Martina Kaledin

Symposium of Student Scholars

This work uses driven molecular dynamics (DMD) method, in conjunction with an analytic PES calculated using MP2/aug-cc-pVDZ energies to identify and assign Raman vibrational modes of methane. Recently, a new linearized approach was proposed for the Polarizability Tensor Surfaces (PTS) that yields a unique solution to the least-squares fitting problem and provides a competitive level of accuracy compared to the non-linear PTS model. We used the previously reported B3LYP/6-31+G(d) molecular geometries for CH₄ and generated a new PTS at the MP2/aug-cc-pVDZ level of theory. The performance of the linearly parametrized functional form for the CH₄ PTS is examined. …

Development Of Accurate And Efficient Computational Methodologies For Predicting Protein-Ligand And Protein-Protein Binding Free Energies, Alexander Hamilton Williams

Theses and Dissertations--Pharmacy

Computational modeling is an invaluable tool in the drug discovery process either for small ligand or protein therapeutics. The widespread availability of protein X-Ray Crystal and Cryo-Electron Microscopy (Cryo-EM) structures has allowed for more accurate molecular dynamics (MD) simulations that are not reliant on methods such as homology modeling, which may produce structures that require significant computational time to demonstrate their stability. In this thesis we describe several novel methodologies for the computationally efficient modeling of protein/ligand and protein/protein complexes that may be employed within both large-scale virtual screenings and lead compound optimization. These methodologies may also be utilized in …

Computational Catalyst Discovery: Active Classification Through Myopic Multiscale Sampling, Kevin Tran, Willie Neiswanger, Kirby Broderick, Eric Xing, Jeff Schneider, Zachary W. Ulissi

Machine Learning Faculty Publications

The recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is …

Integrated In Silico Techniques For Mechanism Studies Of Antiviral And Antidiabetic Compounds, Mohd Isa Diyana

Student Works (2020-2029)

Discovery and development of drug is an iterative process that involves identification of target and leads discovery, lead optimization and pre-clinical of biological profile and is continued until the drugs are eligible to be tested in the clinical phase. Computer-aided drug design (CADD) is an efficient way to overcome the demands of cost, time consumption and drug validation. CADD utilizes in silico technique approaches such as molecular docking to understand their binding pattern and affinity with the target protein and molecular dynamic (MD) simulations to provide an atomic view of the dynamic behavior and stability of bioactive compound inside the …

Diffusion And Random Walks, Lewis Ludwig, Annabel Edwards

Faculty Publications

No abstract provided.