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This study introduces the Deep Learning-aided many-body dispersion quadrupole (DNN-MBDQ) model, enhancing van der Waals force calculations beyond dipole interactions. The new model achieves chemical accuracy with reduced errors in Density Functional Theory (DFT) applications.

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Area of Science:

  • Computational chemistry
  • Quantum mechanics
  • Materials science

Background:

  • Accurate modeling of van der Waals (vdW) forces is crucial for predicting molecular and material properties.
  • Existing methods often struggle to capture higher-order dispersion interactions beyond the dipole term.
  • The Deep Learning-aided many-body dispersion (DNN-MBD) model previously addressed dipole contributions effectively.

Purpose of the Study:

  • To extend the DNN-MBD model to incorporate quadrupole polarizability (Q) terms for more comprehensive vdW interactions.
  • To develop a computationally efficient method for including higher-order dispersion corrections in Density Functional Theory (DFT).
  • To assess the accuracy and performance of the new DNN-MBDQ model compared to dipole-only approaches.

Main Methods:

  • Generalized Random Phase Approximation (RPA) formalism to include quadrupole polarizability.
  • Recursive retrieval of quadrupole polarizabilities from dipole polarizabilities modeled via the Tkatchenko-Scheffler method.
  • A deep neural network (DNN) for efficient calculation of atom-in-molecule volumes.
  • Coupling the DNN-MBDQ model with various DFT functionals (PBE, PBE0, B86bPBE) using a single range-separation parameter.

Main Results:

  • The developed DNN-MBDQ model successfully incorporates quadrupole polarizability contributions to vdW interactions.
  • Quadrupole-corrected DFT functionals achieve chemical accuracy.
  • The DNN-MBDQ approach demonstrates lower errors compared to models relying solely on dipole polarizabilities.
  • The method is computationally inexpensive, allowing for integration with standard DFT workflows.

Conclusions:

  • The DNN-MBDQ model provides a significant advancement in accurately describing van der Waals forces by including quadrupole terms.
  • This approach offers a computationally efficient and accurate way to improve DFT predictions for a wide range of chemical systems.
  • The model's reliance on ab initio-derived quantities and transferable DNNs ensures broad applicability and ease of use.