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Abstract Crystalline pentacene is a model solid-state light-harvesting material because its quantum efficiencies exceed 100% via ultrafast singlet fission. The singlet fission mechanism in pentacene crystals is disputed due to insufficient electronic information in time-resolved experiments and intractable quantum mechanical calculations for simulating realistic crystal dynamics. Here we combine a multiscale multiconfigurational approach and machine learning photodynamics to understand competing singlet fission mechanisms in crystalline pentacene. Our simulations reveal coexisting charge-transfer-mediated and coherent mechanisms via the competing channels in the herringbone and parallel dimers. The predicted singlet fission time constants (61 and 33 fs) are in excellent agreement with experiments (78 and 35 fs). The trajectories highlight the essential role of intermolecular stretching between monomers in generating the multi-exciton state and explain the anisotropic phenomenon. The machine-learning-photodynamics resolved the elusive interplay between electronic structure and vibrational relations, enabling fully atomistic excited-state dynamics with multiconfigurational quantum mechanical quality for crystalline pentacene. 

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.