Can range-separated functionals be optimally tuned to predict spectra and excited state dynamics in photoactive iron...

Area of Science:

• Computational Chemistry
• Quantum Chemistry
• Photochemistry and Photophysics

Background:

• Density functional theory (DFT) is vital for understanding transition metal complex photophysics and photochemistry.
• Optimally tuned range-separated functionals aim to improve upon standard approximate functionals.
• Accurate computational models are needed to interpret experimental data for iron complexes.

Purpose of the Study:

• To investigate the influence of parameter selection in optimally tuned range-separated DFT functionals on excited state dynamics.
• To compare different tuning strategies for functionals using an iron complex, [Fe(cpmp)2]2+.
• To assess the impact of parameter choice on relaxation pathways and timescales via dynamics simulations.

Main Methods:

• Utilized density functional theory (DFT) with optimally tuned range-separated functionals.
• Employed various tuning strategies based on self-consistent DFT protocols and comparison with experimental spectra and CASPT2 calculations.
• Performed nonadiabatic surface-hopping dynamics simulations using the two most promising parameter sets.

Main Results:

• Different optimal parameter sets yielded significantly divergent excited state relaxation pathways and timescales.
• One parameter set predicted long-lived metal-to-ligand charge transfer triplet states.
• Another parameter set, aligning better with CASPT2 and experimental data, indicated deactivation via metal-centered states.

Conclusions:

• The choice of optimal parameters for range-separated functionals critically affects the predicted excited state dynamics of iron complexes.
• Iron complex excited state landscapes are complex, and unambiguous functional parametrization often requires experimental input.
• Computational studies necessitate careful validation against experimental spectroscopic and dynamic data.