Benchmarking DFT for Chain-Length and Water Effects in Thermal Conia–Ene Cyclization

Abstract Reliable density functional theory (DFT) methods for thermally driven cyclization reactions remain insufficiently established, particularly for systems in which proton transfer, conformational preorganization, and weak environmental effects jointly shape the energy landscape. Here, we present a systematic benchmark of DFT exchange–correlation functional/basis-set combinations against CCSD(T) reference energies for the thermal Conia–Ene cyclization, using chain length and explicit water assistance as chemically relevant probes of functional performance. Representative reaction profiles were constructed for two model substrates (hex-5-ynal and hept-6-ynal), considering pathways with and without explicit water, in both gas phase and implicit solvent. The reaction proceeds stepwise through enolization followed by cyclization, with enolization defining the intrinsic kinetic bottleneck in the water-free pathway (ca. 70 kcal mol–1). Explicit water selectively lowers this barrier by up to 30 kcal mol–1 through proton-transfer mediation, whereas the cyclization barrier is primarily controlled by chain length through transition-state preorganization; implicit solvation has only a minor energetic effect. Across 38 approximate exchange–correlation functionals and 14 basis sets, range-separated hybrids provide the most accurate and balanced description of both activation barriers and reaction energies. In particular, ωB97XD and CAM-B3LYP-D3BJ achieve near-chemical accuracy, with mean absolute errors close to 1 kcal mol–1 when combined with triple-ζ basis sets containing polarization and diffuse functions. Comparable accuracy is also achieved by the double-hybrid functionals mPW2PLYP and DSD-PBEP86, albeit at a substantially higher computational cost. Overall, this study establishes a practical CCSD(T)-referenced benchmark and identifies robust DFT protocols for modeling thermal Conia–Ene cyclizations, with expected transferability to more complex substrates and confined environments.