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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. 

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. 

Abstract Benzene fluorination increases chemoselectivities for Dewar‐benzenes via 4π‐disrotatory electrocyclization. However, the origin of the chemo‐ and regioselectivities of fluorobenzenes remains unexplained because of the experimental limitations in resolving the excited‐state structures on ultrafast timescales. The computational cost of multiconfigurational nonadiabatic molecular dynamics simulations is also currently cost‐prohibitive. We now provide high‐fidelity structural information and reaction outcome predictions with machine‐learning‐accelerated photodynamics simulations of a series of fluorobenzenes, C₆F_6‐nH_n, n=0–3, to study their S₁→S₀decay in 4 ns. We trained neural networks with XMS‐CASPT2(6,7)/aug‐cc‐pVDZ calculations, which reproduced the S₁absorption features with mean absolute errors of 0.04 eV (<2 nm). The predicted nonradiative decay constants for C₆F₄H₂, C₆F₆, C₆F₃H₃, and C₆F₅H are 116, 60, 28, and 12 ps, respectively, in broad qualitative agreement with the experiments. Our calculations show that a pseudo Jahn–Teller distortion of fluorinated benzenes leads to an S₁local‐minimum region that extends the excited‐state lifetimes of fluorobenzenes. The pseudo Jahn–Teller distortions reduce when fluorination decreases. Our analysis of the S₁dynamics shows that the pseudo‐Jahn–Teller distortions promote an excited‐statecis‐transisomerization of a π_C‐Cbond. We characterized the surface hopping points from our NAMD simulations and identified instantaneous nuclear momentum as a factor that promotes the electrocyclizations. 

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. 

Cu( i ) and Au( i ) ions, capped with an N-heterocyclic carbene (NHC), react with (TMS) 3 P 7 (TMS = trimethyl-silyl) to afford an η 4 -coordinated anion [NHC Dipp Cu–P 7 (TMS)] − and a neutral trinuclear complex (NHC Dipp Au) 3 P 7 . Protecting the P 7 cage with the TMS groups is instrumental in controlling the course of these reactions.