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1. Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations

   https://doi.org/10.1039/D0SC05610C  

   Li, Jingbai; Reiser, Patrick; Boswell, Benjamin R.; Eberhard, André; Burns, Noah Z.; Friederich, Pascal; Lopez, Steven A. (April 2021, Chemical Science)

   null (Ed.)

   Photochemical reactions are widely used by academic and industrial researchers to construct complex molecular architectures via mechanisms that often require harsh reaction conditions. Photodynamics simulations provide time-resolved snapshots of molecular excited-state structures required to understand and predict reactivities and chemoselectivities. Molecular excited-states are often nearly degenerate and require computationally intensive multiconfigurational quantum mechanical methods, especially at conical intersections. Non-adiabatic molecular dynamics require thousands of these computations per trajectory, which limits simulations to ∼1 picosecond for most organic photochemical reactions. Westermayr et al. recently introduced a neural-network-based method to accelerate the predictions of electronic properties and pushed the simulation limit to 1 ns for the model system, methylenimmonium cation (CH 2 NH 2 + ). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI 2 MD) software for the cis – trans isomerization of trans -hexafluoro-2-butene and the 4π-electrocyclic ring-closing of a norbornyl hexacyclodiene. We performed a 10 ns simulation for trans -hexafluoro-2-butene in just 2 days. The same simulation would take approximately 58 years with traditional multiconfigurational photodynamics simulations. We generated training data by combining Wigner sampling, geometrical interpolations, and short-time quantum chemical trajectories to adaptively sample sparse data regions along reaction coordinates. The final data set of the cis – trans isomerization and the 4π-electrocyclic ring-closing model has 6207 and 6267 data points, respectively. The training errors in energy using feedforward neural networks achieved chemical accuracy (0.023–0.032 eV). The neural network photodynamics simulations of trans -hexafluoro-2-butene agree with the quantum chemical calculations showing the formation of the cis -product and reactive carbene intermediate. The neural network trajectories of the norbornyl cyclohexadiene corroborate the low-yielding syn -product, which was absent in the quantum chemical trajectories, and revealed subsequent thermal reactions in 1 ns. 

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2. Simultaneous CO ₂ capture and functionalization: solvated electron precursors as novel catalysts

   https://doi.org/10.1039/D1CC04748E  

   Jackson, Benjamin A.; Miliordos, Evangelos (January 2022, Chemical Communications)

   Metal complexes with diffuse solvated electrons (solvated electron precursors) are proposed as alternative catalysts for the simultaneous CO 2 capture and utilization. Quantum chemical calculations were used to study the reaction of CO 2 with H 2 and C 2 H 4 to produce formic acid, methyldiol and δ-lactone. Mechanisms of a complete reaction pathway are found and activation barriers are reasonably low. The metal ligand complex readily reduces CO 2 and significantly stabilizes CO 2 ˙ − . Ligand identity minimally influences the reaction. Additional reactions and future strategies are proposed with the goal of inducing experimental interest. 

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3. Photochemical Decarbonylation of Oxetanone and Azetidinone: Spectroscopy, Computational Models, and Synthetic Applications**

   https://doi.org/10.1002/anie.202215856  

   Singh, Manvendra; Dhote, Pawan; Johnson, Daniel R.; Figueroa‐Lazú, Samuel; Elles, Christopher G.; Boskovic, Zarko (January 2023, Angewandte Chemie International Edition)

   Abstract Photoexcitation of cyclic ketones leads to the expulsion of carbon monoxide and a mixture of products derived from diradical intermediates. Here we show that synthetic utility of this process is improved if strained heterocyclic ketones are used. Photochemistry of 3‐oxetanone and N ‐Boc‐3‐azetidinone has not been previously described. Decarbonylation of these 4‐membered rings proceeds through a step‐wise Norrish type I cleavage of the C−C bond from the singlet excited state. Ylides derived from both compounds are high‐energy species that are kinetically stable long enough to undergo [3+2] cycloaddition with a variety of alkenes and produce substituted tetrahydrofurans and pyrrolidines. The reaction has a sufficiently wide scope to produce scaffolds that were either previously inaccessible or difficult to synthesize, thereby providing experimental access to new chemical space. 

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4. Machine learning accelerated photodynamics simulations

   https://doi.org/10.1063/5.0159247  

   Li, Jingbai; Lopez, Steven A (September 2023, Chemical Physics Reviews)

   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. 

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5. Photochemically triggered cheletropic formation of cyclopropenone (c-C ₃ H ₂ O) from carbon monoxide and electronically excited acetylene

   https://doi.org/10.1039/D2CP01978G  

   Wang, Jia; Kleimeier, N. Fabian; Johnson, Rebecca N.; Gozem, Samer; Abplanalp, Matthew J.; Turner, Andrew M.; Marks, Joshua H.; Kaiser, Ralf I. (July 2022, Physical Chemistry Chemical Physics)

   For more than half a century, pericyclic reactions have played an important role in advancing our fundamental understanding of cycloadditions, sigmatropic shifts, group transfer reactions, and electrocyclization reactions. However, the fundamental mechanisms of photochemically activated cheletropic reactions have remained contentious. Here we report on the simplest cheletropic reaction: the [2+1] addition of ground state 18 O-carbon monoxide (C 18 O, X 1 Σ + ) to D2-acetylene (C 2 D 2 ) photochemically excited to the first excited triplet (T1), second excited triplet (T2), and first excited singlet state (S1) at 5 K, leading to the formation of D2- 18 O-cyclopropenone (c-C 3 D 2 18 O). Supported by quantum-chemical calculations, our investigation provides persuasive testimony on stepwise cheletropic reaction pathways to cyclopropenone via excited state dynamics involving the T2 (non-adiabatic) and S1 state (adiabatic) of acetylene at 5 K, while the T1 state energetically favors an intermediate structure that directly dissociates after relaxing to the ground state. The agreement between experiments in low temperature ices and the excited state calculations signifies how photolysis experiments coupled with theoretical calculations can untangle polyatomic reactions with relevance to fundamental physical organic chemistry at the molecular level, thus affording a versatile strategy to unravel exotic non-equilibrium chemistries in cyclic, aromatic organics. Distinct from traditional radical–radical pathways leading to organic molecules on ice-coated interstellar nanoparticles (interstellar grains) in cold molecular clouds and star-forming regions, the photolytic formation of cyclopropenone as presented changes the perception of how we explain the formation of complex organics in the interstellar medium eventually leading to the molecular precursors of biorelevant molecules. 

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