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 …

Application Of Crystal Engineering In Multicomponent Pharmaceutical Crystals: A Study Of Theory And Practice, Soroush Ahmadi Nasrabadi

Electronic Thesis and Dissertation Repository

Multicomponent crystallization, a prominent strategy in crystal engineering, offers the ability to modify the physicochemical properties of crystals by introducing a secondary component to their lattice structure. Such multicomponent crystals have found widespread application in the pharmaceutical industry. This thesis explores the experimental screening, characterization, application, and theoretical prediction of multicomponent crystals of Active Pharmaceutical Ingredients (APIs).

The first case study investigates a new solvate of Dasatinib which exhibits high instability at room temperature and transforms into a different polymorph upon desolvation. The crystal structure of this compound is obtained, revealing insights into its transient nature and the potential application …

Computational Studies Of Bond Dissociation Energies And Organic Reaction Mechanisms, Shehani Thishakkya Wetthasinghe

Theses and Dissertations

This dissertation presents the progress of two independent projects. Chapter 2 and Chapter 3 focus on the first project, which involves material exploration utilizing machine learning techniques. We explore the potential use of cobaltocenium (CoCp⁺₂) derivatives as metal cations in anion exchange membranes (AEMs) for alkaline fuel cells, highlighting their superior thermal and alkaline stability compared to ammonium derivatives. The stability of CoCp⁺₂ can be fine-tuned by varying the substituent groups attached to the cyclopentadienyl ring (Cp) in CoCp⁺₂ .These derivatives encompass a variety of electron-donating and electron-withdrawing groups as substituents on both …

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 …

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation Of Irregular Networks, Anand K. Nambisan, Akanksha Maurya, Norsang Lama, Thanh Phan, Gehana Patel, Keith Miller, Binita Lama, Jason Hagerty, Ronald Stanley, William V. Stoecker

Chemistry Faculty Research & Creative Works

Deep Learning Has Achieved Significant Success in Malignant Melanoma Diagnosis. These Diagnostic Models Are Undergoing a Transition into Clinical Use. However, with Melanoma Diagnostic Accuracy in the Range of Ninety Percent, a Significant Minority of Melanomas Are Missed by Deep Learning. Many of the Melanomas Missed Have Irregular Pigment Networks Visible using Dermoscopy. This Research Presents an Annotated Irregular Network Database and Develops a Classification Pipeline that Fuses Deep Learning Image-Level Results with Conventional Hand-Crafted Features from Irregular Pigment Networks. We Identified and Annotated 487 Unique Dermoscopic Melanoma Lesions from Images in the ISIC 2019 Dermoscopic Dataset to Create a …

Developing And Deploying Data-Driven Tools For Accelerated Design Of Organic Semiconductors, Vinayak Bhat

Theses and Dissertations--Chemistry

Organic semiconductors have gained widespread attention due to their potential applications in flexible, low-cost, lightweight electronics, energy storage and generation technologies, and sensing applications. However, developing new organic semiconductors with improved performance remains a significant challenge due to the vast chemical space of possible molecular and materials structures. Furthermore, the high cost and time-consuming nature of experimental synthesis and characterization hinder the rapid discovery of new materials. To overcome these challenges, this dissertation presents a data-driven approach to organic semiconductor discovery. The primary focus of this work is the development of data-driven tools, namely machine learning models, to predict critical …