Role of the Perfluoro Effect in the Selective Photochemical Isomerization of Hexafluorobenzene

Cox, Jordan M.; Kellogg, Mike; Bain, Matthew; Bradforth, Stephen E.; Lopez, Steven A.

doi:10.26434/chemrxiv.13727932.v1

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“…Hexafluorination of benzene significantly enhances the chemoselectivity of the photochemical 4π disrotatory electrocyclization to strained Dewar-HFB (Figure 9a). 96 However, the longlived nonradiative decay of HFB and low quantuam yield of Dewar-HFB prevent productive QC-NAMD simulations from understanding the mechanisms. 96 The ML photodynamics simulations enable us to model the excited-state dynamics of HFB in 4 ns at the XMS-CASPT2(6,7)/aug-cc-pVDZ level.…”

Section: Photocyclization Of Hexafluorobenzenesmentioning

confidence: 99%

“…96 However, the longlived nonradiative decay of HFB and low quantuam yield of Dewar-HFB prevent productive QC-NAMD simulations from understanding the mechanisms. 96 The ML photodynamics simulations enable us to model the excited-state dynamics of HFB in 4 ns at the XMS-CASPT2(6,7)/aug-cc-pVDZ level. 3 The ML photodynamics simulations show that most HFBs reform the reactant.…”

Section: Photocyclization Of Hexafluorobenzenesmentioning

confidence: 99%

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A Look Inside the Black Box of Machine Learning Photodynamics Simulations

Lopez

2022

Acc. Chem. Res.

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Metrics & MoreArticle Recommendations * sı Supporting Information CONSPECTUS: Photochemical reactions are of great importance in chemistry, biology, and materials science because they take advantage of a renewable energy source, mild reaction conditions, and high atom economy. Light absorption can excite molecules to a higher energy electronic state of the same spin multiplicity. The following nonadiabatic processes induce molecular transformations that afford exotic molecular architectures and high-energy-isomers that are inaccessible by thermal means. Computational simulations now complement time-resolved instrumentation to reveal ultrafast excited-state mechanistic information for photochemical reactions that is essential in disentangling elusive spectroscopic features, excitedstate lifetimes, and excited-state mechanistic critical points. Nonadiabatic molecular dynamics (NAMD), powered by surface hopping techniques, is among the most widely applied techniques to model the photochemical reactions of medium-sized molecules. However, the computational efficiency is limited because of the requisite thousands of multiconfigurational quantum-chemical calculations multiplied by hundreds of trajectories. Machine learning (ML) has emerged as a revolutionary force in computational chemistry to predict the outcome of the resource-intensive multiconfigurational calculations on the fly. An ML potential trained with a substantial set of quantum-chemical calculations can predict the energies and forces with errors under chemical accuracy at a negligible cost. The integration of ML potentials in NAMD dramatically extends the maximum simulation time scale by ∼10 000fold to the nanosecond regime.In this Account, we present a comprehensive demonstration of ML photodynamics simulations and summarize our most recent applications in resolving complex photochemical reactions. First, we address three fundamental components of ML techniques for photodynamics simulations: the quantum-chemical data set, the ML potential, and NAMD. Second, we describe best practices in building training data and our procedure toward training the ML photodynamics model with our recent literature contributions. We introduce a convenient training data generation scheme combining Wigner sampling and geometrical interpolation. It trains reliable and effective ML potentials suitable for subsequent active learning to detect undersampled data. We demonstrate how active learning automatically discovers new mechanistic pathways and reproduces experimental results. We point out that atomic permutation is an essential data augmentation approach to improve the learnability of distance-based molecular descriptors for highly symmetric molecules. Third, we demonstrate the utility of ML-photodynamics by showing the results of ML photodynamics simulations of (1) photo-torquoselective 4π disrotatory electrocyclic ring closing of norbornyl cyclohexadiene, which reveals a thermal conversion from experimentally unobserved intermediates to the reactant in 1 ns; (2) [2 + 2] ph...

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Section: Photocyclization Of Hexafluorobenzenesmentioning

confidence: 99%