Computational 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 …

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 …

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 …

Transition Metal Computational Catalysis: Mechanistic Approaches And Development Of Novel Performance Metrics, Brett Anthony Smith

Doctoral Dissertations

Computational catalysis is an ever-growing field, thanks in part to the incredible progression of computational power and the efficiency offered by our current methodologies. Additionally, the accuracy of computation and the emergence of new methods that can decompose energetics and sterics into quantitative descriptors has allowed for researchers to begin to identify important structure-function relationships that predict the properties of unexplored subspaces within the overall chemical space. Catalytic descriptors have been used frequently in data driven high-throughput computational screenings. With the use of machine learning, a large portion of the chemical space an be predicted in matter of minutes or …

State-Based Biological Communication, Nathan Clement

All Theses

Allostery (1) is the process through which proteins self-regulate in response to various stimuli. Allosteric interactions occur between nonadjacent spatially distant residues (1), and they are exhibited through the correlated motions (2) and momenta of participating residues. The location of allosteric sites in proteins can be determined experimentally but computational methods to predict the location of allosteric sites are being developed as well (2-4, 10). Experimental and computational methodologies for locating allosteric sites can be used to design specific targeted drug delivery (5-6, 19), but these methods have not yet …

From Evaluating The Performance Of Approximations In Density Functional Theory To A Machine Learning Design, Pedram Tavazohi

Graduate Theses, Dissertations, and Problem Reports

Density-functional theory (DFT) has gained popularity because of its ability to predict the properties of a large group of materials a priori. Even though DFT is exact, there are inaccuracies introduced into the theory due to the approximations in the exchange-correlation (XC) functionals. Over the 50 years of its existence, scientists have tried to improve the design of the XC functionals. The errors introduced by these functionals are not consistent across all types of solid-state materials. In this project, a high throughput framework was utilized to compare the theoretical DFT predictions with the experimental results available in the Inorganic Crystal …