Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory
In: ISSN: 1948-7185, 2023
academicJournal
Zugriff:
International audience ; We extend our recently proposed Deep Learning-aided many-body dispersion (DNN-MBD) model to quadrupole polarizability (Q) terms using a generalized Random Phase Approximation (RPA) formalism enabling to include van der Waals contributions beyond dipole. The resulting DNN-MBDQ model only relies on ab initio-derived quantities as the introduced quadrupole polarizabilities are recursively retrieved from dipole ones, in turn modelled via the Tkatchenko-Scheffler method. A transferable and efficient deep-neuronal network (DNN) provides atom in molecule volumes, while a single range-separation parameter is used to couple the model to Density Functional Theory (DFT). Since it can be computed at negligible cost, the DNN-MBDQ approach can be coupled with DFT functionals such as PBE/PBE0 or B86bPBE(dispersionless). DNN-MBQ-PBE/PBE0 reaches chemical accuracy exhibiting superior accuracy compared to other dispersion-corrected models, especially at near-equilibrium ranges where errors are lowered by nearly 25% compared to our dipole-only approach while gains reach nearly 50% compared to other corrected schemes.
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Generalized Many-Body Dispersion Correction through Random-phase Approximation for Chemically Accurate Density Functional Theory
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Autor/in / Beteiligte Person: | Poier, Pier Paolo ; Adjoua, Olivier ; Lagardère, Louis ; Piquemal, Jean-Philip ; Laboratoire de chimie théorique (LCT) ; Institut de Chimie - CNRS Chimie (INC-CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ; Institut Parisien de Chimie Physique et Théorique (IP2CT) ; Biomedical Engineering Austin ; University of Texas at Austin Austin ; Sorbonne Université (SU) ; European Project: 810367,EMC2(2019) |
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Zeitschrift: | ISSN: 1948-7185, 2023 |
Veröffentlichung: | HAL CCSD ; American Chemical Society, 2023 |
Medientyp: | academicJournal |
DOI: | 10.1021/acs.jpclett.2c03722 |
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