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|a 10.1002/jcc.27488
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| 041 |
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|a eng
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| 100 |
1 |
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|a Wang, Yizhen
|e verfasserin
|4 aut
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| 245 |
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|a How accurate can Kohn-Sham density functional be for both main-group and transition metal reactions
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|c 2024
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 08.11.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2024 Wiley Periodicals LLC.
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|a Achieving chemical accuracy in describing reactions involving both main-group elements and transition metals poses a substantial challenge for density functional approximations (DFAs), primarily due to the significantly different behaviors for electrons moving in the s,p-orbitals or in the d,f-orbitals. MOR41, a representative dataset of transition metal chemistry, has highlighted the PWPB95-D3(BJ) functional, a B2PLYP-type doubly hybrid (bDH) approximation equipped with an empirical dispersion correction, as the leading functional thus far (Dohm et al., J Chem Theory Comput 2018;14: 2596-2608). However, this functional is not among the top bDH methods for main-group chemistry (Goerigk et al., Phys Chem Chem Phys. 2017;19: 32184). Conversely, bDH methods such as DSD-BLYP-D3, proficient in main-group chemistry, often falter for transition metal chemistry. Herein, taking advantage of the home-made Rust-based Electronic-Structure Toolkits, we examine a suite of XYG3-type doubly hybrid (xDH) methods. We confirm that the trade-off in descriptive accuracy between main-group and transition metal systems persists within the realm of perturbation theory (PT2)-based xDH methods. Notably, however, our study ushers in a pivotal advance with the recently proposed renormalized xDH method, R-xDH7-SCC15. This method not only distinguishes itself among the elite methods for main-group chemistry, but also achieves an unprecedented accuracy for the MOR41 dataset, outperforming all other reported DFAs. The efficacy of R-xDH7-SCC15 stems from the successful integration of a renormalized PT2 correlation model (rPT2) and a machine-learning strong-correlation correction (SCC15), marking a significant step forward in the realm of computational chemistry
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|a Journal Article
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|a DFT
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|a REST
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|a double hybrid
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|a machine‐learning
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| 650 |
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|a strong‐correlation
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| 700 |
1 |
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|a Zhang, Igor Ying
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Xin
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 45(2024), 32 vom: 15. Nov., Seite 2878-2884
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:45
|g year:2024
|g number:32
|g day:15
|g month:11
|g pages:2878-2884
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|u http://dx.doi.org/10.1002/jcc.27488
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