Title:
Reactive Molecular Dynamics in Ionic Media
Reactive Molecular Dynamics in Ionic Media
Author(s)
Stoppelman, John Paul
Advisor(s)
McDaniel, Jesse G.
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Abstract
Chemical reactions are among the most fundamental phenomena within the field of chemistry. In many contexts, reactions are conducted or occur in condensed phase environments. Environmental effects can cause a host of complicated changes to a given chemical process, such as altering thermodynamic equilibrium, reaction rates or the associated mechanism. Solvents can thus be used to tune a given reaction. In particular, ionic media can cause substantial changes to a reaction due to the long-range Coulombic interactions between the reacting complex and solvent molecules, which, energetically, can be quite large in magnitude. Further study of reactions within ionic solvents would allow for modulating these interactions for selected applications. Theoretical approaches, such as quantum chemistry, represent one tract of methods that can be applied for this purpose. However, while quantum chemical techniques can effectively investigate many gas phase reactions, condensed phase reactions are much more challenging to investigate. The many degrees of freedom associated with the bulk solvent makes first principles modeling infeasible due to unfavorable scaling with respect to system size.
Force fields derived from ab initio methods specifically designed for simulating reactions can significantly enhance insight into solvent modulation of chemical reactions. A sufficiently accurate force field can be used to perform molecular dynamics at quantum chemistry-level accuracy within an external environment at a fraction of the cost. However, such reactive force fields have been challenging to parameterize and use, as typical physics-based expressions used in force fields are better suited for asymptotic interactions than describing short-ranged effects associated with chemical bond-breaking/formation. Recent machine learning approaches have proved effective at learning a wide range of physical interactions, however, and can potentially be combined with standard force fields in order to build an extensive framework for modeling chemical reactions. This thesis details our development of a reactive force field framework that combines these two methodologies. We describe our procedure for building reactive force fields and apply it and similar methods to study reactions and other phenomenon within condensed phases. The systems we examine include N-heterocyclic carbene formation in [EMIM+][OAc-], proton transport in ionic liquid/water mixtures, density scaling within molecular liquids and negative thermal expansion in the materials ScF3 and CaZrF6. Through comparison with experimental and first principles predicted properties throughout this work, we demonstrate the utility of physics-based and machine learning models for understanding complex processes within challenging chemical environments.
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Date Issued
2023-08-03
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Resource Subtype
Dissertation