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Insights Into Density Functional Performance From a Main‐Group and Transition‐Metal Molecular Benchmark.

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Title: Insights Into Density Functional Performance From a Main‐Group and Transition‐Metal Molecular Benchmark.
Authors: Liu, Yiwei1 (AUTHOR) yiweiliu@sit.edu.cn, Wei, Lijie1 (AUTHOR), Tao, Shuai1 (AUTHOR), Wu, Yichu1 (AUTHOR), Wu, Fanhong1 (AUTHOR), Wang, Ying2 (AUTHOR) wangyin@hunnu.edu.cn, He, Xiao3,4,5 (AUTHOR) xiaohe@phy.ecnu.edu.cn
Source: Journal of Computational Chemistry. 5/5/2026, Vol. 47 Issue 12, p1-11. 11p.
Subjects: Density functionals, Transition metal compounds, Activation energy, Scission (Chemistry), Density functional theory, Molecular energy levels (Quantum mechanics)
Abstract: Density functional theory (DFT) is widely used for modeling molecular energetics, yet the accuracy of density functionals strongly depends on the chemical environment, making reliable functional selection across diverse applications. To facilitate the rational selection of functionals, we systematically assess the performance of 26 density functionals across six representative classes of molecular energetics, including reaction barriers, polar σ‐bond dissociation, ionization energies, metal–ligand dissociation, catalytic barrier heights, and strongly correlated 3d transition‐metal complexes. By jointly analyzing datasets spanning both main‐group and transition‐metal chemistry, this work provides a cross‐domain assessment of functional performance across chemically distinct regimes. Our results indicate that functional transferability between these two domains tends to be constrained, with relatively few hybrid meta‐NGAs and hybrid meta‐GGAs (e.g., CF22D, PW6B95‐D3(BJ)) demonstrating comparatively balanced accuracy across diverse datasets, while multi‐reference cases remain challenging for all functionals considered. The dataset‐specific analysis provides practical insights for functional selection: HSE06‐D3(BJ), PBE‐D3(BJ), and M06‐2X‐D3(0) functionals perform well for main‐group reaction barriers, while CF22D, M06‐D3(0), M06 and MN15 functionals are more reliable for polar bond dissociation. For transition‐metal energetics, CF22D, PW6B95‐D3(BJ), and HSE06‐D3(BJ) functionals offer robust performance. Overall, this study delineates the strengths and limitations of modern density‐functional approximations and offers data‐driven guidance for functional selection in heterogeneous molecular problems. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Density functional theory (DFT) is widely used for modeling molecular energetics, yet the accuracy of density functionals strongly depends on the chemical environment, making reliable functional selection across diverse applications. To facilitate the rational selection of functionals, we systematically assess the performance of 26 density functionals across six representative classes of molecular energetics, including reaction barriers, polar σ‐bond dissociation, ionization energies, metal–ligand dissociation, catalytic barrier heights, and strongly correlated 3d transition‐metal complexes. By jointly analyzing datasets spanning both main‐group and transition‐metal chemistry, this work provides a cross‐domain assessment of functional performance across chemically distinct regimes. Our results indicate that functional transferability between these two domains tends to be constrained, with relatively few hybrid meta‐NGAs and hybrid meta‐GGAs (e.g., CF22D, PW6B95‐D3(BJ)) demonstrating comparatively balanced accuracy across diverse datasets, while multi‐reference cases remain challenging for all functionals considered. The dataset‐specific analysis provides practical insights for functional selection: HSE06‐D3(BJ), PBE‐D3(BJ), and M06‐2X‐D3(0) functionals perform well for main‐group reaction barriers, while CF22D, M06‐D3(0), M06 and MN15 functionals are more reliable for polar bond dissociation. For transition‐metal energetics, CF22D, PW6B95‐D3(BJ), and HSE06‐D3(BJ) functionals offer robust performance. Overall, this study delineates the strengths and limitations of modern density‐functional approximations and offers data‐driven guidance for functional selection in heterogeneous molecular problems. [ABSTRACT FROM AUTHOR]
ISSN:01928651
DOI:10.1002/jcc.70388