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In this study, we combine experiments, calculated properties, and machine learning (ML) to design new triphenylamine-based (TPA) molecules that have a high photoinduced radical (PIR) generation in crystals. A dataset of 34 crystal structures was extracted from the Cambridge Crystallographic Data Centre. Eighteen structures with experimentally reported PIR values from 0 to 0.85% were used to build an ML model trained using Random Forest that achieves an average leave-one-out test set error of 0.173% PIR. The ML model was used to screen the remaining 16 compounds, of which 4 were selected and sub-sequently compared with the experimentally measured PIR%. The predicted PIR% demonstrated good agreement with the measured values of TPA bis-urea macrocycles host-guest complexes and non-macrocyclic compounds of TPAs. Examining a broad set of molecular architectures/scaffolds allows for investigating the structural and electronic properties that lead to high PIR generation. We found very different trends for macrocycles, linear TPAs, and mono TPAs, where mono TPAs consist-ently have the lowest PIR generation. Macrocycles tend to have the highest PIR generation, especially for systems with ben-zene and fluorobenzene guests. Although linear analogs overall perform worse than macrocycles, they display clear trends with increasing excited-state dipole moment, oscillator strength and electron-hole covariance, while decreasing ionization potential and interatomic distance are generally correlated with higher PIRs. What is consistently observed is that higher PIRs are seen for brominated analogs. Our study, therefore, provides guidelines for future design strategies of TPAs for PIR generation. 

Exciplexes are excited-state complexes formed as a result of partial charge transfer from the donor to the acceptor species when one moiety of the donor–acceptor pair is electronically excited. The arene–amine exciplex formed between oligo-(p-phenylene) (OPP) and triethylamine (TEA) is of interest in the catalytic photoreduction of CO2 because it can compete with complete electron transfer to the OPP catalyst. Therefore, formation of the exciplex can hinder the generation of a radical anion OPP·− necessary for subsequent CO2 reduction. We report an implementation of a workflow automating quantum-chemistry calculations that generate and characterize an ensemble of structures to represent this exciplex state. We use FireWorks, Pymatgen, and Custodian Python packages for high-throughput ensemble generation. The workflow includes time-dependent density functional theory optimization, verification of excited-state minima, and exciplex characterization with natural transition orbitals, exciton analysis, excited-state Mulliken charges, and energy decomposition analysis. Fluorescence spectra computed for these ensembles using Boltzmann-weighted contributions of each structure agree better with experiment than our previous calculations based on a single representative exciplex structure [Kron et al., J. Phys. Chem. A 126, 2319–2329 (2022)]. The ensemble description of the exciplex state also reproduces an experimentally observed red shift of the emission spectrum of [OPP-4–TEA]* relative to [OPP-3–TEA]*. The workflow developed here streamlines otherwise labor-intensive calculations that would require significant user involvement and intervention.