Efficient computational prediction of ground and excited state molecular properties using implicit and explicit solvent methods Restricted; Files Only

Abstract

In this dissertation, four theoretical works are presented in which density functional theory (DFT) and the polarizable continuum model (PCM) are combined in novel ways to predict important solution-phase properties of molecules. First, there is an in-depth introduction to the quantum chemistry methods used in the papers covered (Chapter 1). Then, discussion of two newly developed methods: the implementation of a high pressure PCM method XP-PCM (Chapter 2) and an automated toolkit, AutoSolvate, to generate molecular dynamics simulations and extract microsolvated molecular clusters for quantum mechanical calculation (Chapter 3). Next, AutoSolvate is used to calculate redox potentials for many organic and organometallic redox pairs and train machine learning models to improve those predictions (Chapter 4). Finally, optimal tuning of range-separated hybrid functionals (OT-RSH) is applied to a small set of organic photoredox catalysts (Chapter 5).

Table of Contents

Chapter 1: Introduction

1.1  Quantum Chemistry Methods                                                                                                    2

1.2 Solvent Methods                                                                                                                       13

1.3 Implementing PCM with DFT                                                                                                 21

1.4 Photoredox Catalysis                                                                                                                26

1.5 References                                                                                                                                28

Chapter 2: Quantum chemistry for molecules at extreme pressure on graphical

processing units: Implementation of extreme-pressure polarizable continuum model

2.0 Abstract                                                                                                                                    37

2.1 Introduction                                                                                                                              38

2.2 Theory                                                                                                                                      39

2.3 Implementation on GPUs                                                                                                         46

2.4 Computational Details                                                                                                              51

2.5 Results and Discussion                                                                                                             53

2.6 Conclusions                                                                                                                              63

2.7 Acknowledgments                                                                                                                    64

2.8 References                                                                                                                                64

Chapter 3: AutoSolvate: A toolkit for automating quantum chemistry design

and discovery of solvated molecules

3.0 Abstract                                                                                                                                    75

3.1 Introduction                                                                                                                              76

3.2 Code Overview                                                                                                                         78

3.3 Code Architecture                                                                                                                     79

3.4 Computational Details                                                                                                              88

3.5 Results and Discussion                                                                                                             89

3.6 Conclusions                                                                                                                              95

3.7 Acknowledgements                                                                                                                  96

3.8 References                                                                                                                                96

Chapter 4: Bridging the Experiment-Calculation Divide: Machine Learning

Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models

4.0 Abstract                                                                                                                                  110

4.1 Introduction                                                                                                                            111

4.2 Theory                                                                                                                                    113

4.3 Computational Details                                                                                                            115

4.4 Results                                                                                                                                    120

4.5 Conclusion                                                                                                                              134

4.6 Acknowledgements                                                                                                                136

4.7 References                                                                                                                              136

Chapter 5: Optimal tuning of range separated hybrid functionals (OT-RSH) in

implicit solvent to improve DFT prediction accuracy in solution phase photoredox catalysis

5.1 Introduction                                                                                                                            146

5.2 Theory                                                                                                                                    149

5.3 Computational details                                                                                                             157

5.4 Results and Discussion                                                                                                           163

5.4.1. Optimal γ-Tuning in a PCM                                                                                   164

5.4.2. Redox property prediction                                                                                      171

5.5 Conclusions                                                                                                                            182

5.6 References

About this Dissertation

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School	Laney Graduate School
Department	Chemistry
Degree	Ph.D.
Submission	Dissertation
Language	English
Research Field	Chemistry, Physical Chemistry, General
关键词	density functional theory theoretical chemistry computational chemistry solvent model
Committee Chair / Thesis Advisor	Fang Liu, Emory University
Committee Members	Joel Bowman, Emory University Francesco Evangelista, Emory University

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