A computational chemist’s guide to the development of designer material systems through the utilization of past, present, and developmental computational techniques

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Ratcliff, Tyree, D.
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Computational chemistry broadly investigates chemical problems using computer aided simulations/models. The exploration of material properties through computational means requires traversing length scales, from atomic to macroscopic, and large amounts computing time and energy. The implementation of computational techniques; such as machine learning, molecular dynamics simulations, and density functional theory calculations, helps to bridge this gap. Two major computational technique were broached; Quantum Theory of Atoms in Molecules (QTAIM or AIM) and Quantitative Structure-Activity (or Property) Relationship modeling (QSAR or QSPR), in the development of metal-based TAE (Transferable Atom Equivalent) atom types, the utilization of a descriptor-based representation DNA-Pixels, and the evaluation of dielectric properties of nanocomposite systems. TAE descriptors encode the distributions of electron density of the atomic subsections producing a number of properties; Kinetic energy densities, local average ionization potentials, electrostatic potentials, and other atomic charge density derived properties. The development of metal-based TAE atom types allows for the utilization of these electron density derived properties for structural activity and molecular property modeling. The TAE framework provides an adaptive infrastructure for the creation of molecular electron density from TAE atom type fragments with low computational cost. The objective of this work was the development and incorporation of a Platinum TAE atom type library into the existing TAE library of atom types. Another application of the TAE methodology has be used in the generation of a descriptor-based representation of DNA formed from the investigations of the short-range effects of flanking base pairs. DNA-Pixel (DIXEL) represent electron density features such as Electrostatic Potential (EP) and Politzer’s local average Ionization Potential (PIP) on the accessible surfaces of the major or minor groove. DIXELs provide the user with the ability to create a raster of alignment-free spatially resolved interaction points on the major and minor grooves of an DNA sequence, allowing for bioinformatic application. DIXELs were developed using DNA triplets, three basepair combinations of Cytosine, Guanine, Thymine, and Adenine. Methylated Cytosine and Adenine were also included in these combinations. A DNA triplet contains the central basepair of interest flanked by its two adjacent basepairs, producing a total of 216 DNA triplet combinations. Evaluating the electrostatic potential surface of two Adenine triplets, AAA (non methylated) and AA*A (*methylated Adenine), the methylated AA*A triplet contains a region of decreased electrostatic potential on the surface. This decrease is directly opposite of the point of methylation, within the minor groove of the DNA system. This phenomenon demonstrates the effects points of methylation has on DNA electron density. An advantage of DIXEL is its ability to be utilized for a comparative approach evaluating the complementary of electron density surface features of DNA sequences and small molecules. This ligand based similarity approach creates an \emph{ad hoc} method for DNA ligand docking which could be apply to other techniques such as machine learning. These DIXEL descriptors were used to probe sequence-specific interactions of preferential binding of amino acid functionalized amikabeads to methylated over unmethylated regions of DNA. With results indicating that the methylation of Adenine is the primary driver of selected methylated DNA ligand lead of sequence-specific DNA interactions. Next, the evaluation of dielectric properties of high energy-density nanocomposite systems was done using a combination of machine learning, density functional theory calculations, and a large collaborative effort. Predicting the dielectric properties of high energy-dense nanocomposite materials requires a useful model of the electron trapping and mobility. This work investigated the electron trapping phenomenon at interfacial regions of nano-composites, nanoparticle surface, and polymeric bulk through the evaluation of the electronic structure at the interface using several quantum mechanical methods; density functional theory calculations for single molecules (surface functionalization groups) zero-point energy calculations, local density of state calculations for $\alpha$-Quartz Silica electron trapping, and local-density approximation studies for kinetic study of electron/hole mobility in polyethylene systems. Experimental work performed in Dr. Linda Schadler's group concluded the grafting of functional groups to nanoparticles increased the breakdown strength of resulting polymer nanocomposites. To investigate the underlying phenomenon quantum computation of electron affinity (EA) and ionization energy (IE) of isolated single molecules (surface functional groups) was done to explore the possible correlation between these two properties and dielectric breakdown of the resulting functionalized nanoparticle in a composite system. Even though the physical nature of electron traps and the mechanism of nanocomposite breakdown are not clearly understood; intuitively the relationship between the charged states of polymer chains and functionalized nanoparticles contributes to the restriction of free electrons and stabilization of the system. Our focus was on investigating the effect of the incorporation of different functional groups on the surface of a nanoparticle with the belief that systems with smaller band gaps (EA+IE value) would produce higher stability points for electron trapping, depressing likelihood for dielectric breakdown to occur within the system. Assuming the energy difference between the lower energy state (valence band) produced by the electron deficient state and the higher energy state (conduction band) produced by the enriched state. An $\alpha$-quartz silica model system was used to represent functional nanofiller particles and local density of state calculations were performed. These local density of state calculations were used to examine the changes to the band structures that occur when traversing from a fully coordinated silica system to an under-coordinated / functionalized system. At these interfacial regions, the attached functional chains create both deep and shallow electron traps that can affect electron mobility. To better simulate the experimental behavior of amorphous systems at interfacial regions, an amorphous model analog was developed and evaluated. A comparison of the defect states between amorphous and crystalline systems and their effects on the band structure, when functionalized, allow new insights into the role that surface modification/functionalization plays in dielectric breakdown. These model silica analogs enabled the investigation of electron trapping phenomenon at interfacial regions of nanocomposites, nanoparticle surfaces, and polymeric bulk through the evaluation of the electronic structure at the interface. This electron trapping and mobility study allows for the prediction of dielectric properties of new high energy-density nanocomposite materials. Local-density approximation studies for kinetic study of electron/hole mobility in polyethylene systems was done to investigate "voltage stabilizers", molecular additives that have been shown to provide deep traps for hopping carriers. These deep traps slows down the transport as carriers drop into the deep potential well formed by these species. The detrapping rate can be calculated based on the semi-classical thermally assisted tunneling model. To replicate Sato’s methodology for determination of hole mobility, a coreshell structure C8H18 polyethylene oligomers was constructed. Using the core-shell model, density functional theory computations were performed for polyethylene to construct Electron/Hole mobility model.
December 2023
School of Science
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Rensselaer Polytechnic Institute, Troy, NY
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