Computational Chemistry Commons^™

Data-Driven Analysis And Atomistic Simulations Of Next-Generation Materials For Energy Conversion And Storage, Yuliang Shi

Dissertations

Metal-organic frameworks (MOFs), with their modular architectures and tunable properties, represent an especially rich domain for accelerated material design and discovery for a range of diverse applications. Within this class of multifunctional materials, two-dimensional (2D) electrically conductive MOFs (EC MOFs) are of particular interest, as their 7r-stacked layered structures combine permanent porosity with electronic conductivity, enabling potential breakthroughs in energy storage, energy conversion, and quantum sensing. But the discovery and design of new EC MOFs based on expensive experimental screening is increasingly impractical due to the infinite chemical space. Furthermore, the practical implementation of EC MOFs for specific tasks depends …

Accurate Characterization Of Binding Kinetics And Allosteric Mechanisms For The Hsp90 Chaperone Inhibitors Using Ai-Augmented Integrative Biophysical Studies, Chao Xu, Xianglei Zhang, Lianghao Zhao, Gennady M. Verkhivker, Fang Bai

Mathematics, Physics, and Computer Science Faculty Articles and Research

The binding kinetics of drugs to their targets are gradually being recognized as a crucial indicator of the efficacy of drugs in vivo, leading to the development of various computational methods for predicting the binding kinetics in recent years. However, compared with the prediction of binding affinity, the underlying structure and dynamic determinants of binding kinetics are more complicated. Efficient and accurate methods for predicting binding kinetics are still lacking. In this study, quantitative structure–kinetics relationship (QSKR) models were developed using 132 inhibitors targeting the ATP binding domain of heat shock protein 90α (HSP90α) to predict the dissociation rate …

Assessing Interatomic Potentials For Molecular Dynamics Simulation Of Soybean Oil Pyrolysis, Tanner Garrett Rust

Graduate Theses/Dissertations

The world today relies on hydrocarbon combustion for many reasons, including its high energy density that provides ease of transportation. However, hydrocarbons sourced from fossil fuels are not expected to last forever. Biodiesel, a renewable alternative, has many attractive benefits but comes with other downsides. Biodiesel can gel in cold environments and may leave residue in an engine. Pyrolysis of biodiesel has shown promise in addressing these common detriments. Inducing pyrolysis on biodiesel feedstock (commonly soybean oil in the USA) would be an attractive option presuming it continues to produce fossil fuel analogs similar to biodiesel pyrolysis. Herein, Langevin molecular …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Computational Investigations Of Polymer Electrolytes In Aqueous Solutions, Hishara Keshani Gallage Dona

LSU Doctoral Dissertations

Polymer electrolyte membranes, an intriguing category of soft materials, are characterized by polymer backbones tethered with negatively or positively charged functional groups, finding applications from biological macromolecules to electrochemical separations in fuel cells. This dissertation focuses on computational investigations of polymer electrolytes in aqueous solutions using polypeptoids and ion exchange membranes. Polypeptoids, highly tailorable peptidomimetic polymers lacking backbone hydrogen bonding or chirality, were used as model systems. Micelles formed from these polypeptoid polymers exhibited electrostatic repulsion, which competes with charge-sodium interactions, adding complexity to predicting micelle shapes. This research refined the design strategy, enhancing micelle shape and structure through precise …

The Influence Of Allostery Governing The Changes In Protein Dynamics Upon Substitution, Joseph Hess

All Dissertations

The focus of this research is to investigate the effects of allostery on the function/activity of an enzyme, human immunodeficiency virus type 1 (HIV-1) protease, using well-defined statistical analyses of the dynamic changes of the protein and variants with unique single point substitutions ¹. The experimental data¹ evaluated here only characterized HIV-1 protease with one of its potential target substrates. Probing the dynamic interactions of the residues of an enzyme and its variants can offer insight of the developmental importance for allosteric signaling and their connection to a protein’s function. The realignment of the secondary structure elements can …

Machine Learning For Electronic And Atomistic Simulations, Jun Yang

Dartmouth College Ph.D Dissertations

The demand for accurate and efficient atomistic simulations and electronic structure calculations in materials science and quantum chemistry has motivated the development of novel computational methodologies. The rapid evolution of machine learning has brought new techniques for advancing the accuracy, efficiency, and predictive power of atomistic simulations and electronic structure calculations.

In this thesis, we explore the symmetry requirements and physics intuitions needed for developing machine-learning interatomic potentials, which are the most critical component in atomistic simulations. Specifically, we introduce a novel physics-inspired graph neural network interatomic potential that enables accurate and efficient atomistic simulations of complex materials. The machine …

Recent Advances In Electrochemical Kinetics Simulations And Their Applications In Pt-Based Fuel Cells, Ji-Li Li, Ye-Fei Li, Zhi-Pan Liu

Journal of Electrochemistry

Theoretical simulations of electrocatalysis are vital for understanding the mechanism of the electrochemical process at the atomic level. It can help to reveal the in-situ structures of electrode surfaces and establish the microscopic mechanism of electrocatalysis, thereby solving the problems such as electrode oxidation and corrosion. However, there are still many problems in the theoretical electrochemical simulations, including the solvation effects, the electric double layer, and the structural transformation of electrodes. Here we review recent advances of theoretical methods in electrochemical modeling, in particular, the double reference approach, the periodic continuum solvation model based on the modified Poisson-Boltzmann …