A Metal-Free Cyclobutadiene Reagent for Intermolecular [4 + 2] Cycloadditions.

Cyclobutadiene is a highly reactive antiaromatic hydrocarbon that has fascinated chemists for over 60 years. However, its preparation and uses in chemical synthesis are sparing, in part due to its lengthy synthesis that generates hazardous byproducts including excess heavy metals. Herein, we report a scalable, metal-free cyclobutadiene reagent, diethyldiazabicyclohexene dicarboxylate, and explore its intermolecular [4 + 2] cycloaddition with various electron-deficient alkenes. We also demonstrate its utility in a three-step synthesis of dipiperamide G and a diverse array of product derivatizations including bromocyclobutadiene.

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

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 (CH2NH2 +). We have adapted this methodology to develop the Python-based, Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics (PyRAI2MD) 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.

Mechanochemical synthesis of an elusive fluorinated polyacetylene.

Polymer mechanochemistry has traditionally been employed to study the effects of mechanical force on chemical bonds within a polymer backbone or to generate force-responsive materials. It is under-exploited for the scalable synthesis of wholly new materials by chemically transforming the polymers, especially products inaccessible by other means. Here we utilize polymer mechanochemistry to synthesize a fluorinated polyacetylene, a long-sought-after air-stable polyacetylene that has eluded synthesis by conventional means. We construct the monomer in four chemical steps on gram scale, which involves a rapid incorporation of fluorine atoms in an exotic photochemical cascade whose mechanism and exquisite stereoselectivity were informed by computation. After polymerization, force activation by ultrasonication produces a gold-coloured, semiconducting fluoropolymer. This work demonstrates that polymer mechanochemistry is a valuable synthetic tool for accessing materials on a preparative scale.

Xenon Difluoride Mediated Fluorodecarboxylations for the Syntheses of Di- and Trifluoromethoxyarenes.

XeF2 is demonstrated to be a more proficient fluorine-transfer reagent than either NFSI or Selectfluor in fluorodecarboxylations of both mono- and difluoroaryloxy acetic acid derivatives. This method efficiently converts a wide range of neutral and electron-poor substrates to afford the desired di- and trifluoromethyl aryl ethers in good to excellent yields. The purifications are facile, and the reaction times are less than 5 min, which makes these fluorodecarboxylations promising for future PET-imaging applications.