Spin-state energies of heme-related models from spin-flip TDDFT calculations
Literature Information
Hui Zhao, Changfeng Fang, Chengbu Liu
Spin-state energies of heme-related models are of vital importance in biochemistry. To compute the energies of different spin states, the traditional ΔSCF method based on the density functional theory (DFT) is usually employed. In this work, the spin-flip TDDFT (SF-TDDFT) approach is investigated to compute the spin-state energies, with six different exchange–correlation (XC) functionals. With the present protocol, the spin contamination is fully avoided by choosing appropriate reference states. Additionally, multiple excited states can be obtained with SF-TDDFT. Compared with the CCSD(T) results, it is shown that the SF-TDDFT calculations with the BHandHLYP functional provide better accuracy than ΔSCF for D–Q (doublet–quartet) and Q–S (quartet–sextet) gaps and agree well with the experimental results. A possible solution for the precise calculation of spin-state energies is proposed to improve the performance of SF-TDDFT, on account of that the excitation energies show highly linear dependence on the amount of Hartree–Fock (HF) exchange in the XC functionals.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.












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