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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. 

The electrochemical nitrogen reduction reaction (NRR) is a promising route to enable carbon-free ammonia production. However, this reaction is limited by the poor activity and selectivity of current catalysts. The rational design of superior NRR electrocatalysts requires a detailed mechanistic understanding of current material limitations to inform how these might be overcome. The current understanding of how scaling limits NRR on metal catalysts is predicated on a simplified reaction pathway that considers only proton-coupled electron transfer (PCET) steps. Here, we apply grand-canonical density functional theory to investigate a more comprehensive NRR mechanism that includes both electrochemical and chemical steps on 30 metal surfaces in solvent under an applied potential. We applied Φmax, a grandcanonical adaptation of the Gmax thermodynamic descriptor, to evaluate trends in catalyst activity. This approach produces a Φmax “volcano” diagram for NRR activity scaling on metals that qualitatively differs from the scaling relations identified when only PCET steps are considered. NH3* desorption was found to limit the NRR activity for materials at the top of the volcano and truncate the volcano’s peak at increasingly reducing potentials. These revised scaling relations may inform the rational design of superior NRR electrocatalysts. This approach is transferable to study other materials and reaction chemistries where both electrochemical and chemical steps are modeled under an applied potential.