Data-driven Approaches for Predicting Excited-state Properties in Condensed Phase Molecular Systems Open Access
Ren, Fangning (Spring 2026)
Abstract
Predicting excited-state properties of molecular systems in condensed phase environments remains a challenge in theoretical chemistry, where the interplay between environmental effects and conformational diversity imposes severe computational bottlenecks. This dissertation develops and applies data-driven approaches that address three key problems in this field.
First, we resolve the long-lasting question of which optimal tuning scheme for range-separated hybrid (RSH) density functionals yield the most accurate solution-phase excitation energies. We evaluated multiple gamma-tuning protocols—gas-phase gamma tuning (GPγT), partial vertical gamma tuning (PVγT), strict vertical gamma tuning (SVγT), and optimally tuned screened RSH functional with polarizable continuum model (SRSH-PCM)—against experimental UV/Vis absorption data for 937 solvated molecules spanning 14 solvents. The analysis reveals that SVγT achieves a mean absolute error of 0.35 eV, outperforming other protocols. Furthermore, we found that the smaller γ values from SVγT captured the expected 1/εr asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations.
Second, we apply the extended Frenkel exciton model with charge-transfer (CT) states to investigate the fluorescence properties of asphaltene aggregates. Through large-scale calculations on over 500 asphaltene dimer configurations encompassing 133 monomers, we demonstrate that dimer fluorescence never exceeds both of its monomers, with further redshifts in heterodimers are primarily caused by LE-CT coupling. These findings provide the first systematic, data-driven understanding of PAH aggregate photo physics across a chemically diverse compositional space.
Third, we introduce a size-transferable machine learning (ML) method based on Frenkel exciton model that predicts excited-state properties for molecular assemblies of arbitrary size using models trained only on dimer Hamiltonians. We found that the exciton model Hamiltonian of large aggregates can be evaluated by conducting QC calculation on all dimer pairs in the assemblies, allowing an ML model trained on dimers to reconstruct Hamiltonians for aggregates of any size. We also proposed a new method to address the phase-correction problem by introducing coupling terms’ approximations. Our model accurately predicted the excitation energies of trimer and tetramer of perylene and tetracene and estimated S1 oscillator strengths of perylene aggregates. Leveraging our ML model, the optical gaps of nanosized perylene aggregates with up to 50 monomers are analyzed, qualitatively revealing the role of different types of couplings on their size dependency.
Together, these studies demonstrate that data-driven methodologies—spanning high-throughput benchmarking, statistical analysis of large conformational ensembles, and machine learning—can overcome fundamental barriers in condensed-phase excited-state modeling that are intractable with conventional quantum chemistry approaches alone.
Table of Contents
1 Chapter 1 : Introduction
1 1.1 Electronic Excited-states and Theoretical Modeling
2 1.2 Challenges Facing Quantum Chemistry Methods
4 1.3 Data-Driven Approaches and the Overview
6 References
10 Chapter 2 : Theoretical Background
10 2.1 Density Functional Theory and Time-Dependent Extensions
10 2.1.1 The Electronic Structure Problem and Hartree-Fock Theory
12 2.1.2 Density Functional Theory
15 2.1.3 Time-Dependent Density Functional Theory
20 2.1.4 Range-Separated Hybrid Functionals and Optimal Tuning
23 2.2 Solvation Models
23 2.2.1 The Polarizable Continuum Model
25 2.2.2 Explicit Solvation: Molecular Dynamics and QM/MM
26 2.3 The Frenkel Exciton Model
26 2.3.1 The Standard Frenkel Exciton Model
27 2.3.2 The Extended Frenkel Exciton Model with Charge-Transfer States
31 References
36 Chapter 3 : Data-Driven Evaluation of Optimal Tuning Schemes for Range-Separated Hybrid Functionals in Solution
36 3.1 Introduction
43 3.2 Method
43 3.2.1 Dataset curation
45 3.2.2 DFT calculations
48 3.2.3 γ-tuning procedure
49 3.2.4 Asymptote behavior evaluation
51 3.3. Results
51 3.3.1 The optimum γ-value distribution
58 3.3.2 Assessing the γ-tuning procedure
69 3.3.3 Impact of solutes and solvents
76 3.3.5 Impact of γ-tuning on delocalization error.
78 3.3.4 Reasons for the better performance of PVγT and SVγT
85 3.4. Conclusion
88 3.5 Appendix
88 3.5.1 Proof of the impact of PCM on the HOMO energy and IP for the N+1 anionic state
91 3.5.2 Assessing the impact of solvent polarity on optimal γ under SVγT.
92 3.6 References
102 Chapter 4 Data-Driven Insights into Asphaltene Aggregate Fluorescence Using the Extended Frenkel Exciton Model
102 4.1 Introduction
105 4.2 Theory
110 4.3 Methods
110 4.3.1 Preparation of asphaltene monomers
111 4.3.2 Generation of initial asphaltene dimer structures
114 4.3.3 DFT & TDDFT calculations
118 4.3.4 Exciton model and electron excitation analysis
119 4.4 Results
119 4.4.1 Monomer properties
121 4.4.2 The C22-PC113 Dimer
126 4.4.3 The C05-C22 Dimer
130 4.4.4 Fluorescence energy of the C22-X and PC113-X series
135 4.4.5 Oscillator strength of the C22-X series
138 4.4.6 Fluorescence and oscillator strength of 465 coal asphaltene dimers.
144 4.5 Conclusion
147 4.6 Appendix
152 4.7 References
161 Chapter 5 Size-Transferable Prediction of Excited-state Properties with Machine-Learned Exciton Models
161 5.1 Introduction
165 5.2 The Extended Frenkel Hamiltonian for Large Assemblies
166 5.2.1 Hamiltonian Structure of a N-mer (an assembly has N monomers)
168 5.2.2 Pairwise Decomposability of Matrix Elements
170 5.2.3 Approximating the Type 1 CT-CT coupling
173 5.2.4 Investigation of the proportionality factor for Type 1 CT-CT couplings
176 5.3 Physical Approximations for the Hamiltonian Matrix Elements
176 5.3.1 Motivation: a Δ-ML Strategy for the Exciton Hamiltonian
177 5.3.2 Reference Wavefunction Alignment
178 5.3.3 Approximations for Each Hamiltonian Term
180 5.3.4 Decomposition of Approximations into Atomic Contributions
181 5.4 Training Dataset Construction and Reference Calculations
181 5.4.1 Molecular Dynamics Sampling for Dimer Extraction
182 5.4.2 SEP5A: Constructing the Separated-Dimer Subset via Approximations
185 5.4.3 QC Calculations
186 5.4.4 Data Augmentation via Belonging-Label Swap
186 5.5 Solving the Phase Problem for Large Aggregates
189 5.6 Machine Learning Model Architecture and Prediction Workflow
189 5.6.1 Modified TorchANI Architecture Overview
193 5.6.2 The LE network.
193 5.6.3 The CT network.
194 5.6.4 The coupling networks.
194 5.6.5 Training Procedure and Hyperparameters
195 5.7 Results
195 5.7.1 Dimer Hamiltonian Accuracy
197 5.7.2 Excited-state Energy Prediction for Trimers and Tetramers
199 5.7.3 Oscillator Strength Prediction for Trimers and Tetramers
203 5.8 Application: Optical Gap Size Dependency of Perylene Nanoaggregates
204 5.8.1 Optical Gap vs. Aggregate Size
207 5.8.2 Decomposition of Coupling Contributions
210 5.9 Discussion, Limitations, and Future Directions
211 5.10 Conclusion
About this Dissertation
| School | |
|---|---|
| Department | |
| Degree | |
| Submission | |
| Language |
|
| Research Field | |
| Keyword | |
| Committee Chair / Thesis Advisor | |
| Committee Members |
Primary PDF
| Thumbnail | Title | Date Uploaded | Actions |
|---|---|---|---|
|
|
Data-driven Approaches for Predicting Excited-state Properties in Condensed Phase Molecular Systems () | 2026-04-26 16:01:23 -0400 |
|
Supplemental Files
| Thumbnail | Title | Date Uploaded | Actions |
|---|