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Achieving tunable electrical conductivity in organic materials is a key challenge for the development of next-generation semiconductors. This study demonstrates a novel approach using triphenylamine (TPA) bis-urea macrocycles as supramolecular hosts for guest-induced modulation of charge-transfer (CT) properties. By encapsulating guests with varying reduction potentials, including 2,5-dichloro-1,4-benzoquinone (ClBQ), 2,1,3-benzothiadiazole (BTD), and malononitrile (MN), we observed significant changes in the electrical conductivity. Crystals of the 1(ClBQ)0.31 complex exhibited an electrical conductivity of ∼2.08 × 10–5 S cm–1, a 10,000-fold enhancement compared to the pristine host. This is attributed to efficient CT observed in spectroscopic analyses and is consistent with the computed small HOMO–LUMO gap (2.92 eV) in a model of the host–guest system. 1(MN)0.39 and 1(BTD)0.5 demonstrated moderate conductivities explained by the interplay of electronic coupling, reorganization energy, and energy gap. Lower ratios of guest inclusion decreased the electrical conductivity by 10-fold in 1(ClBQ)0.18, while 1(MN)0.25 and 1(BTD)0.41 were nonconductive (10–9 S cm–1). This work highlights the potential of metal-free, porous organic systems as tunable semiconductors, offering a pathway to innovative applications in organic electronics. 

The structures and photoinduced radical (PIR) percentages of two crystalline solvates of unsubstituted triphenylamine bis-urea macrocycles are compared. Upon activation, both afford similar structures with higher PIR %. 

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.