General optimization procedure towards the design of a new family of minimal parameter spin-component-scaled double-hybrid density functional theory
文献情報
A general optimization procedure towards the development and implementation of a new family of minimal parameter spin-component-scaled double-hybrid (mSD) density functional theory (DFT) is presented. The nature of the proposed exchange–correlation functional establishes a methodology with minimal empiricism. This new family of double-hybrid (DH) density functionals is demonstrated using the PBEPBE functional, illustrating the optimization procedure to the mSD-PBEPBE method, and the performance characteristics shown for a set of non-covalent complexes covering a broad regime of weak interactions. With only two parameters, mSD-PBEPBE and its cost-effective counterpart, RI-mSD-PBEPBE, show a mean absolute error of ca. 0.4 kcal mol−1 averaged over 66 weak interacting systems. Following a successive 2D-grid refinement for a CBS extrapolation of the coefficients, the optimization procedure can be recommended for the design and implementation of a variety of additional DH methods using any of the plethora of currently available 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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