Chemistry Commons^™

Machine Learning Modeling Of Polymer Coating Formulations: Benchmark Of Feature Representation Schemes, Nelson I. Evbarunegbe

Masters Theses

Polymer coatings offer a wide range of benefits across various industries, playing a crucial role in product protection and extension of shelf life. However, formulating them can be a non-trivial task given the multitude of variables and factors involved in the production process, rendering it a complex, high-dimensional problem. To tackle this problem, machine learning (ML) has emerged as a promising tool, showing considerable potential in enhancing various polymer and chemistry-based applications, particularly those dealing with high dimensional complexities.

Our research aims to develop a physics-guided ML approach to facilitate the formulations of polymer coatings. As the first step, this …

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 …

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.

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 …

The Interaction Of Different Primary Producers And Physical And Chemical Dynamics Of An Urban Shallow Lake, Majid Sahin

Dissertations, Theses, and Capstone Projects

An artificial urban shallow lake, Prospect Park Lake (PPL), is situated on a terminal moraine in Brooklyn New York, and supplied with municipal water treated with ortho-phosphates. The constant input of the phosphate nutrient is the primary source of eutrophication in the lake. The numerous pools along the water course houses various aquatic phototrophs, which influence the water quality and the state of the system, driving conditions into favoring the survival of their species. In the first half of the dissertation, the focus of the project is on analyzing how the different primary producers in different regions of PPL affect …

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 …

Automated Parallel Optimization Of Simulation Parameters Using Modified Nelder-Mead Simplex Algorithm, Erina Mills

All Dissertations

Computational simulations used in many fields have parameters that define models that are used to evaluate simulated properties. When developing these models, the goal is to choose the parameters that best replicate a set of desired properties. Mathematical optimization methods can be used to optimize the simulation parameters by defining a function that uses simulation parameters as input and outputs a value describing how well a set of experimental properties are reproduced.

Because simulated properties are often calculated using stochastic sampling methods, this optimization involves an objective function that is noisy and expensive to evaluate. Also, optimization of the simulation …

Atomistic Simulation Of Na+ And Cl- Ions Binding Mechanisms To Tobermorite 14Å As A Model For Alkali Activated Cements, Ahmed Abdelkawy

Theses and Dissertations

The production of ordinary Portland cement (OPC) is responsible for ~8% of all man-made CO2 emissions. Unfortunately, due to the continuous increase in the number of construction projects, and since virtually all projects depend on hardened cement from the hydration of OPC as the main binding material, the production of OPC is not expected to decrease. Alkali-activated cement produced from the alkaline activation of byproducts of industries, such as iron and coal industries, or processed clays represents a potential substitute for OPC. However, the interaction of the reaction products of AAC with corrosive ions from the environment, such as Cl-, …

Rapid Method For Consistency And Concentration Reporting Of Cannabidiol Using 1H-Nmr And Computer-Assisted Chemical Software, Michael A. Fernando

University Honors Theses

An integrated computational method was demonstrated with hemp-derived Cannabidiol for an assessment of its purity and concentration. The sample was structurally verified, high purity, and 2.98 mmol/L in dissolved DMSO. The method presented is a general approach to assessing purity and concentration for any small organic molecule in CMC-Assist.

Molecular Dynamics Simulations Of Self-Assemblies In Nature And Nanotechnology, Phu Khanh Tang

Dissertations, Theses, and Capstone Projects

Nature usually divides complex systems into smaller building blocks specializing in a few tasks since one entity cannot achieve everything. Therefore, self-assembly is a robust tool exploited by Nature to build hierarchical systems that accomplish unique functions. The cell membrane distinguishes itself as an example of Nature’s self-assembly, defining and protecting the cell. By mimicking Nature’s designs using synthetically designed self-assemblies, researchers with advanced nanotechnological comprehension can manipulate these synthetic self-assemblies to improve many aspects of modern medicine and materials science. Understanding the competing underlying molecular interactions in self-assembly is always of interest to the academic scientific community and industry. …

Computational Approaches For Screening Drugs For Bioactivation, Reactive Metabolite Formation, And Toxicity, Noah Flynn

Arts & Sciences Electronic Theses and Dissertations

Cytochrome P450 enzymes aid in the elimination of a preponderance of small molecule drugs, but can generate reactive metabolites that may adversely conjugate to protein and DNA, in a process known as bioactivation, and prompt adverse reaction, drug candidate attrition, or market withdrawal. Experimental assays are low-throughput and expensive to perform, so they are often reserved until later stages of the drug development pipeline when the drug candidate pools are already significantly narrowed. Reactive metabolites also elude in vivo detection, as they are transitory and generally do not circulate. In contrast, computational methods are high-throughput and cheap to screen millions …

Automated Parsing Of Flexible Molecular Systems Using Principal Component Analysis And K-Means Clustering Techniques, Matthew J. Nwerem

Computational and Data Sciences (MS) Theses

Computational investigation of molecular structures and reactions of biological and pharmaceutical interests remains a grand scientific challenge due to the size and conformational flexibility of these systems. The work requires parsing and analyzing thousands of conformations in each molecular state for meaningful chemical information and subjecting the ensemble to costly quantum chemical calculations. The current status quo typically involves a manual process where the investigator must look at each conformation, separating each into structural families. This process is time-intensive and tedious, making this process infeasible in some cases, and limiting the ability of theoreticians to study these systems. However, the …

Identification Of Chemical Structures And Substructures Via Deep Q-Learning And Supervised Learning Of Ftir Spectra, Joshua D. Ellis

MSU Graduate Theses

Fourier-transform infrared (FTIR) spectra of organic compounds can be used to compare and identify compounds. A mid-FTIR spectrum gives absorbance values of a compound over the 400-4000 cm^-1 range. Spectral matching is the process of comparing the spectral signature of two or more compounds and returning a value for the similarity of the compounds based on how closely their spectra match. This process is commonly used to identify an unknown compound by searching for its spectrum’s closes match in a database of known spectra. A major limitation of this process is that it can only be used to identify …

Thermoelectric Transport In Disordered Organic And Inorganic Semiconductors, Meenakshi Upadhyaya

Doctoral Dissertations

The need for alternative energy sources has led to extensive research on optimizing the conversion efficiency of thermoelectric (TE) materials. TE efficiency is governed by figure-of-merit (ZT) and it has been an enormously challenging task to increase ZT > 1 despite decades of research due to the interdependence of material properties. Most doped inorganic semiconductors have a high electrical conductivity and moderate Seebeck coefficient, but ZT is still limited by their high lattice thermal conductivity. One approach to address this problem is to decrease thermal conductivity by means of alloying and nanostructuring, another is to consider materials with an inherently low …

Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie

Undergraduate Honors Theses

Density functional theory (DFT) has become a popular method for computational work involving larger molecular systems as it provides accuracy that rivals ab initio methods while lowering computational cost. Nevertheless, computational cost is still high for systems greater than ten atoms in size, preventing their application in modeling realistic atmospheric systems at the molecular level. Machine learning techniques, however, show promise as cost-effective tools in predicting chemical properties when properly trained. In the interest of furthering chemical machine learning in the field of atmospheric science, I have developed a training method for predicting cluster energetics of newly characterized nitrogen-based brown …

Data-Driven Approaches To Complex Materials: Applications To Amorphous Solids, Dil Kumar Limbu

Dissertations

While conventional approaches to materials modeling made significant contributions and advanced our understanding of materials properties in the past decades, these approaches often cannot be applied to disordered materials (e.g., glasses) for which accurate total-energy functionals or forces are either not available or it is infeasible to employ due to computational complexities associated with modeling disordered solids in the absence of translational symmetry. In this dissertation, a number of information-driven probabilistic methods were developed for the structural determination of a range of materials including disordered solids to transition metal clusters. The ground-state structures of transition-metal clusters of iron, nickel, and …

A Unique, Project-Based, Microcourse To Teach The Fundamental Concepts Of Quantum Mechanics, Elijah Begin

Honors Theses

Spyder, a Scientific Python Development Environment, provides an easy-to-use software that can be used to generate data from quantum mechanical systems. This project proposes and explores a microcourse which takes advantage of this utility to teach undergraduates the fundamentals of quantum mechanics.

The Application Of Machine Learning In Analyzing Organic Compounds From Nmr Spectral Data, Nicole Maia Powell

Senior Independent Study Theses

Nuclear magnetic resonance (NMR) is used in organic chemistry to identify unknown organic compounds. The data obtained from an NMR spectrometer are typically shown in the form of a spectrum, which is then analyzed by an analytical chemist. The action of analyzing a spectrum, especially one of a large and complex molecule, is a long and tedious process. In this project, Python is used to implement hierarchical clustering on NMR data obtained from an NMR spectrometer at the College of Wooster to explore its application in NMR analysis. MATLAB is used to build a decision tree from the same data, …

Information Architecture For A Chemical Modeling Knowledge Graph, Adam R. Luxon

Theses and Dissertations

Machine learning models for chemical property predictions are high dimension design challenges spanning multiple disciplines. Free and open-source software libraries have streamlined the model implementation process, but the design complexity remains. In order better navigate and understand the machine learning design space, model information needs to be organized and contextualized. In this work, instances of chemical property models and their associated parameters were stored in a Neo4j property graph database. Machine learning model instances were created with permutations of dataset, learning algorithm, molecular featurization, data scaling, data splitting, hyperparameters, and hyperparameter optimization techniques. The resulting graph contains over 83,000 nodes …

Predicting Material Properties: Applications Of Multi-Scale Multiphysics Numerical Modeling To Transport Problems In Biochemical Systems And Chemical Process Engineering, Tom Pace

Theses and Dissertations--Physics and Astronomy

Material properties are used in a wide variety of theoretical models of material behavior. Descriptive properties quantify the nature, structure, or composition of the material. Behavioral properties quantify the response of the material to an imposed condition. The central question of this work concerns the prediction of behavioral properties from previously determined descriptive properties through hierarchical multi-scale, multiphysics models implemented as numerical simulations. Applications covered focus on mass transport models, including sequential enzyme-catalyzed reactions in systems biology, and an industrial chemical process in a common reaction medium.

The Role Of Ammonia In Atmospheric New Particle Formation And Implications For Cloud Condensation Nuclei, Arshad Arjunan Nair

Legacy Theses & Dissertations (2009 - 2024)

Atmospheric ammonia has received recent attention due to (a) its increasing trend across various regions of the globe; (b) the associated direct and indirect (through PM2.5) effects on human health, the ecosystem, and climate; and (c) recent evidence of its role in significantly enhancing atmospheric new particle formation (NPF or nucleation) rates. The mechanisms behind nucleation in the atmosphere are not fully understood, although over the last decade there have been significant developments in our understanding. This dissertation aims at improving our understanding of atmospheric ammonia in the atmosphere, its spatiotemporal variability, its role in atmospheric new particle formation, and …

Modified Firearm Discharge Residue Analysis Utilizing Advanced Analytical Techniques, Complexing Agents, And Quantum Chemical Calculations, William J. Feeney

Graduate Theses, Dissertations, and Problem Reports

The use of gunshot residue (GSR) or firearm discharge residue (FDR) evidence faces some challenges because of instrumental and analytical limitations and the difficulties in evaluating and communicating evidentiary value. For instance, the categorization of GSR based only on elemental analysis of single, spherical particles is becoming insufficient because newer ammunition formulations produce residues with varying particle morphology and composition. Also, one common criticism about GSR practitioners is that their reports focus on the presence or absence of GSR in an item without providing an assessment of the weight of the evidence. Such reports leave the end-used with unanswered questions, …

Development Of Computational Tools To Target Microrna, Luo Song

Dissertations & Theses (Open Access)

MicroRNAs (a.k.a, miRNAs) play an important role in disease development. However, few of their structures have been determined and structure-based computational methods remain challenging in accurately predicting their interactions with small molecules. To address this issue, my thesis is to develop integrated approaches to screening for novel inhibitors by targeting specific structure motifs in miRNAs. The project starts with implementing a tool to find potential miRNA targets with desired motifs. I combined both sequence information of miRNAs and known RNA structure data from Protein Data Bank (PDB) to predict the miRNA structure and identify the motif to target, then I …

Eelgrass (Zostera Marina) Population Decline In Morro Bay, Ca: A Meta-Analysis Of Herbicide Application In San Luis Obispo County And Morro Bay Watershed, Tyler King Sinnott

Master's Theses

The endemic eelgrass (Zostera marina) community of Morro Bay Estuary, located on the central coast of California, has experienced an estimated decline of 95% in occupied area (reduction of 344 acres to 20 acres) from 2008 to 2017 for reasons that are not yet definitively clear. One possible driver of degradation that has yet to be investigated is the role of herbicides from agricultural fields in the watershed that feeds into the estuary. Thus, the primary research goal of this project was to better understand temporal and spatial trends of herbicide use within the context of San Luis …

Vertical Ionization Energies From The Average Local Electron Energy Function, Amer Marwan El-Samman

Electronic Thesis and Dissertation Repository

It is a non-intuitive but well-established fact that the first and higher vertical ionization energies (VIE) of any N-electron system are encoded in the system's ground-state electronic wave function. This makes it possible to compute VIEs of any atom or molecule from its ground-state wave function directly, without performing calculations on the (N-1)-electron states. In practice, VIEs can be extracted from the wave function by using the (extended) Koopmans' theorem or by taking the asymptotic limit of certain wave-function-based quantities such as the ratio of kinetic energy density to the electron density. However, when the wave function is expanded in …

Implementation Of Multivariate Artificial Neural Networks Coupled With Genetic Algorithms For The Multi-Objective Property Prediction And Optimization Of Emulsion Polymers, David Chisholm

Master's Theses

Machine learning has been gaining popularity over the past few decades as computers have become more advanced. On a fundamental level, machine learning consists of the use of computerized statistical methods to analyze data and discover trends that may not have been obvious or otherwise observable previously. These trends can then be used to make predictions on new data and explore entirely new design spaces. Methods vary from simple linear regression to highly complex neural networks, but the end goal is similar. The application of these methods to material property prediction and new material discovery has been of high interest …

A Robust And Automated Deconvolution Algorithm Of Peaks In Spectroscopic Data, William Johan Burke Iv

Theses and Dissertations

The huge amount of spectroscopic data in use in metabolomic experiments requires an algorithm that can process the data in an autonomous fashion while providing quality of analysis comparable to manual methods. Scientists need an algorithm that effectively deconvolutes spectroscopic peaks automatically and is resilient to the presence of noise in the data. The algorithm must also provide a simple measure of quality of the deconvolution. The deconvolution algorithm presented in this thesis consists of preprocessing steps, noise removal, peak detection, and function fitting. Both a Fourier Transform and Continuous Wavelet Transform (CWT) method of noise removal were investigated. The …

Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor

Electronic Theses and Dissertations

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …

Quantum Chemical Analysis Of Stable Noble Gas Cations For Astrochemical Detection, Carlie M. Novak

Honors College Theses

The search for possible, natural, noble gas molecules has led to quantum chemical, spectroscopic analysis of NeCCH+, ArNH+ ArCCH+, and ArCN+. Each of these systems have been previously shown to be a stable minimum on its respective potential energy surface. However, no spectroscopic data are available for laboratory detection or interstellar observation of these species, and the interstellar medium may be the most likely place, in nature, where these noble gas cations are found. The bent shape of NeCCH+ is confirmed here with a fairly large dipole moment and a bright C -- H stretching frequency at 3101.9 cm^{-1 …}

Synthesis And Evaluation Of Acetylcholine Molecularly Imprinted Polymers, Nathaniel Donald Thiemann

Masters Theses

Polymers imprinted with acetylcholine during synthesis were prepared in order to evaluate their potential for implementation as a novel recognition element in acetylcholine biosensors. Biosensors, such as the glucose monitor, are used to rapidly detect and quantify a target analyte. Acetylcholine biosensors have already been produced using enzymatic recognition elements, but they are currently expensive and plagued by short viability. Molecularly imprinted polymers are not only cheap and durable, but have also been successfully used as a recognition element in biosensors for other analytes. Therefore, computational tools were used to rationally design acetylcholine molecularly imprinted polymers. Three chemicals, itaconic acid, …