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Gas-evolving photochemical reactions use light and mild conditions to access strained organic compounds irreversibly. Cyclopropenones are a class of light-responsive molecules used in bioorthogonal photoclick reactions; their excited-state decarbonylation reaction mechanisms are misunderstood due to their ultrafast (<100 femtosecond) lifetimes. We have combined multiconfigurational quantum mechanical (QM) calculations and non-adiabatic molecular dynamics (NAMD) simulations to uncover the excited-state mechanism of cyclopropenone and a photoprotected cyclooctyne-(COT)-precursor in gaseous and explicit aqueous environments. We explore the role of H-bonding with fully quantum mechanical explicitly solvated NAMD simulations for the decarbonylation reaction. The cyclopropenones pass through asynchronous conical intersections and have dynamically concerted photodecarbonylation mechanisms. The COT-precursor has a higher quantum yield of 55% than cyclopropenone (28%) because these trajectories prefer to break a σCC bond to avoid the strained trans-cyclooctene geometries. Our solvated simulations show an increased quantum yield (58%) for the systems studied here. 

Machine learning (ML) continues to revolutionize computational chemistry for accelerating predictions and simulations by training on experimental or accurate but expensive quantum mechanical (QM) calculations. Photodynamics simulations require hundreds of trajectories coupled with multiconfigurational QM calculations of excited-state potential energies surfaces that contribute to the prohibitive computational cost at long timescales and complex organic molecules. ML accelerates photodynamics simulations by combining nonadiabatic photodynamics simulations with an ML model trained with high-fidelity QM calculations of energies, forces, and non-adiabatic couplings. This approach has provided time-dependent molecular structural information for understanding photochemical reaction mechanisms of organic reactions in vacuum and complex environments (i.e., explicit solvation). This review focuses on the fundamentals of QM calculations and ML techniques. We, then, discuss the strategies to balance adequate training data and the computational cost of generating these training data. Finally, we demonstrate the power of applying these ML-photodynamics simulations to understand the origin of reactivities and selectivities of organic photochemical reactions, such as cis–trans isomerization, [2 + 2]-cycloaddition, 4π-electrostatic ring-closing, and hydrogen roaming mechanism. 

Tuning strained alkyne reactivity via organic synthesis has evolved into a burgeoning field of study largely focused on cyclooctyne, wherein physical organic chemistry helps guide rational molecular design to produce molecules with intriguing properties. Concurrent research in the field of carbon nanomaterials has produced new types of strained alkyne macrocycles, such as cycloparaphenyleneacetylenes, that possess uniquely curved aromatic π systems but hover on the edge of stability. In 2018, we introduced a strained alkyne scaffold that marries the synthetic accessibility and stability of cyclooctyne with the curved π system of carbon nanomaterials. These molecules are strained alkyne-containing cycloparaphenylenes (or [n+1]CPPs), which have been shown to possess size-dependent reactivity as well as the classic characteristics of the unfunctionalized parent CPP, such as a tunable HOMO–LUMO gap and bright fluorescence for large sizes. Herein, we elaborate further on this scaffold, introducing two modifications to the original design and fully characterizing the kinetics of the strain-promoted azide–alkyne cycloaddition (SPAAC) for each [n+1]CPP with a model azide. Additionally, we explain how electronic (the incorporation of fluorine atoms) and strain (a meta linkage which heightens local strain at the alkyne) modulations affect SPAAC reactivity via the distortion–interaction computational model. Altogether, these results indicate that through a modular synthesis and rational chemical design, we have developed a new family of tunable and inherently fluorescent strained alkyne carbon nanomaterials. 

Azoarene photoswitches are versatile molecules that interconvert from their E-isomer to their Z-isomer with light. Azobenzene is a prototypical photoswitch but its derivatives can be poorly suited for in vivo applications such as photopharmacology due to undesired photochemical reactions promoted by ultraviolet light and the relatively short half-life (t1/2) of the Z-isomer (2 days). Experimental and computational studies suggest that these properties (λmax of the E isomer and t1/2 of the Z-isomer) are inversely related. We identified isomeric azobisthiophenes and azobisfurans from a high-throughput screening study of 1540 azoarenes as photoswitch candidates with improved λmax and t1/2 values relative to azobenzene. We used density functional theory to predict the activation free energies and vertical excitation energies of the E- and Z-isomers of 2,2- and 3,3-substituted azobisthiophenes and azobisfurans. The half-lives depend on whether the heterocycles are π-conjugated or cross-conjugated with the diazo π-bond. The 2,2-substituted azoarenes both have t1/2 values on the scale of 1 hour, while the 3,3-analogues have computed half-lives of 40 and 230 years (thiophene and furan, respectively). The 2,2-substituted heteroazoarenes have significantly higher λmax absorptions than their 3,3-substituted analogues: 76 nm for azofuran and 77 nm for azothiophene.