A Conical Intersection Influences the Ground State Rearrangement of Fulvene to Benzene

Electronic Spectrum of α-Hydrofulvenyl Radical (C6H7), and a Simple and Accurate Recipe for Predicting Adiabatic Ionization Energies of Resonance-Stabilized Hydrocarbon Radicals

Using a combination of resonant two-photon two-color ionization (R2C2PI) and laser-induced fluorescence/dispersed fluorescence spectroscopy, we have examined the A ~  2A″ ← X ~  2A″ transition of the resonance-stabilized α-hydrofulvenyl radical, produced from methylcyclopentadiene dimer in a jet-cooled discharge. Like the related 1,4-pentadienyl and cyclohexadienyl radicals, the α-hydrofulvenyl Ã-state lifetime is orders of magnitude shorter than the predicted f-value implies, indicative of rapid nonradiative decay. The transition is fully allowed by symmetry but considerably weakened by transition moment interference. Intensity borrowing among a' modes brings about static (i.e., Condon) and vibronic (i.e., Herzberg-Teller) moments of similar size, the result being a spectrum substantially less origin-dominated than is usually observed for extensively delocalized radicals. Twenty A ~ -state modes and twelve X ~ -state modes are identified with high confidence and assignments for several others are suggested. In addition, from a series of two-color appearance potential scans with the A ~ -state zero-point level serving as an intermediate, we obtain a field-free adiabatic ionization energy (AIE) of 7.012(1) eV. For a set of 21 resonance-stabilized radicals bearing 5 to 11 carbon atoms, it emerges that the field-free AIE obtained by R2C2PI methods under jet-cooled conditions lies very close to the average of B3LYP/6-311G++(d,p) (with harmonic zero-point energy) and CBS-QB3 0 K calculations, with a mean absolute deviation of only 0.010(7) eV (approximately 1 kJ/mol). On average, this represents a nearly 10-fold improvement in accuracy over CBS-QB3 predictions for the same set of radicals.

GPU-accelerated on-the-fly nonadiabatic semiclassical dynamics

GPU-accelerated on-the-fly nonadiabatic dynamics is enabled by interfacing the linearized semiclassical dynamics approach with the TeraChem electronic structure program. We describe the computational workflow of the "PySCES" code interface, a Python code for semiclassical dynamics with on-the-fly electronic structure, including parallelization over multiple GPU nodes. We showcase the abilities of this code and present timings for two benchmark systems: fulvene solvated in acetonitrile and a charge transfer system in which a photoexcited zinc-phthalocyanine donor transfers charge to a fullerene acceptor through multiple electronic states on an ultrafast timescale. Our implementation paves the way for an efficient semiclassical approach to model the nonadiabatic excited state dynamics of complex molecules, materials, and condensed phase systems.

Exploring Exact-Factorization-Based Trajectories for Low-Energy Dynamics near a Conical Intersection

We study low-energy dynamics generated by a two-dimensional two-state Jahn-Teller Hamiltonian in the vicinity of a conical intersection using quantum wave packet and trajectory dynamics. Recently, these dynamics were studied by comparing the adiabatic representation and the exact factorization, with the purpose to highlight the different nature of topological-phase and geometric-phase effects arising in the two theoretical representations of the same problem. Here, we employ the exact factorization to understand how to accurately model low-energy dynamics in the vicinity of a conical intersection using an approximate description of the nuclear motion that uses trajectories. We find that since nonadiabatic effects are weak but non-negligible, the trajectory-based description that invokes the classical approximation struggles to capture the correct behavior.

Barrier Height Prediction by Machine Learning Correction of Semiempirical Calculations

Different machine learning (ML) models are proposed in the present work to predict density functional theory-quality barrier heights (BHs) from semiempirical quantum mechanical (SQM) calculations. The ML models include a multitask deep neural network, gradient-boosted trees by means of the XGBoost interface, and Gaussian process regression. The obtained mean absolute errors are similar to those of previous models considering the same number of data points. The ML corrections proposed in this paper could be useful for rapid screening of the large reaction networks that appear in combustion chemistry or in astrochemistry. Finally, our results show that 70% of the features with the highest impact on model output are bespoke predictors. This custom-made set of predictors could be employed by future Δ-ML models to improve the quantitative prediction of other reaction properties.

Comprehensive Kinetics on the C7H7 Potential Energy Surface under Combustion Conditions

The automated kinetics workflow code, KinBot, was used to explore and characterize the regions of the C₇H₇ potential energy surface that are relevant to combustion environments and especially soot inception. We first explored the lowest-energy region, which includes the benzyl, fulvenallene + H, and cyclopentadienyl + acetylene entry points. We then expanded the model to include two higher-energy entry points, vinylpropargyl + acetylene and vinylacetylene + propargyl. The automated search was able to uncover the pathways from the literature. In addition, three important new routes were discovered: a lower-energy pathway connecting benzyl with vinylcyclopentadienyl, a decomposition mechanism from benzyl that results in side-chain hydrogen atom loss to produce fulvenallene + H, and shorter and lower energy routes to the dimethylene-cyclopentenyl intermediates. We systematically reduced the extended model to a chemically relevant domain composed of 63 wells, 10 bimolecular products, 87 barriers, and 1 barrierless channel and constructed a master equation using the CCSD(T)-F12a/cc-pVTZ//ωB97X-D/6-311++G(d,p) level of theory to provide rate coefficients for chemical modeling. Our calculated rate coefficients show excellent agreement with measured ones. We also simulated concentration profiles and calculated branching fractions from the important entry points to provide an interpretation of this important chemical landscape.

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

Automation of rate-coefficient calculations for gas-phase organic species became possible in recent years and has transformed how we explore these complicated systems computationally. Kinetics workflow tools bring rigor and speed and eliminate a large fraction of manual labor and related error sources. In this paper we give an overview of this quickly evolving field and illustrate, through five detailed examples, the capabilities of our own automated tool, KinBot. We bring examples from combustion and atmospheric chemistry of C-, H-, O-, and N-atom-containing species that are relevant to molecular weight growth and autoxidation processes. The examples shed light on the capabilities of automation and also highlight particular challenges associated with the various chemical systems that need to be addressed in future work.

Mechanism, Thermochemistry, and Kinetics of the Reversible Reactions: C2H3 + H2 ⇌ C2H4 + H ⇌ C2H5

High-level coupled cluster theory, in conjunction with Active Thermochemical Tables (ATcT) and E,J-resolved master equation calculations were used in a study of the title reactions, which play an important role...