Accurate Spin-State Energetics for Aryl Carbenes

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Theoretical Chemistry

Background:

• Aryl carbenes are key intermediates in organic synthesis.
• Accurate prediction of their electronic structure, particularly singlet-triplet energy gaps, is crucial for understanding reactivity.
• Existing computational methods vary in accuracy and efficiency for these systems.

Purpose of the Study:

• To establish a benchmark test set (AC12) for evaluating computational methods for aryl carbene singlet-triplet energy gaps.
• To assess the performance of various correlated wave function and density functional theory methods.
• To identify reliable and accurate computational strategies for predicting these energy gaps.

Main Methods:

• Development of the AC12 test set comprising 12 aryl carbenes with diverse electronic properties.
• Application of high-level correlated wave function methods, including coupled cluster with perturbative triples (CCSD(T)) and basis set extrapolation.
• Evaluation of domain-based local pair natural orbital coupled cluster (DLPNO-CCSD(T)) as a computationally efficient alternative.
• Assessment of second-order Møller-Plesset perturbation theory (MP2) with orbital optimization (OO-MP2) and selected density functional methods, including double-hybrid functionals.

Main Results:

• High accuracy for singlet-triplet energy gaps necessitates the inclusion of perturbative triples and basis set extrapolation.
• DLPNO-CCSD(T) provides a reliable and accurate (mean absolute error of 0.2 kcal/mol) alternative to canonical coupled cluster methods for larger systems.
• OO-MP2 methods are only applicable with orbital optimization.
• Among density functional methods, only double-hybrid functionals demonstrate sufficient accuracy for small singlet-triplet gaps.

Conclusions:

• Correlated wave function methods, particularly coupled cluster approaches with appropriate basis sets and corrections, are essential for accurate singlet-triplet energy gap predictions.
• DLPNO-CCSD(T) emerges as a practical and accurate method for larger aryl carbene systems.
• The study provides a robust benchmark for future computational studies of aryl carbenes and discusses solvation effects.