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Area of Science:

  • Computational Chemistry
  • Materials Science
  • Theoretical Physics

Background:

  • Many-Body Dispersion (MBD) models are crucial for accurate simulations of intermolecular forces.
  • Treating periodic boundary conditions in MBD is computationally challenging, often limiting accuracy and efficiency.
  • Existing methods like the replica method suffer from slow convergence for long-range interactions.

Purpose of the Study:

  • To develop an improved formulation of the Stochastic Lanczos algorithm for MBD calculations.
  • To enable efficient and accurate treatment of periodic boundary conditions within MBD.
  • To overcome the limitations of existing methods in handling long-range interactions.

Main Methods:

  • Derivation of an alternative Stochastic Lanczos algorithm formulation.
  • Introduction of generalized dipoles and fields for periodic boundary conditions.
  • Integration with the O(Nlog(N)) Smooth Particle Mesh Ewald (SPME) method using fast Fourier transforms.

Main Results:

  • The new SPME-Lanczos algorithm significantly outperforms the standard replica method.
  • Achieved drastically improved convergence rates for long-range periodic boundary conditions.
  • Demonstrated efficient and reliable inclusion of long-range interactions in MBD.

Conclusions:

  • The proposed algorithm offers a state-of-the-art approach for periodic MBD calculations.
  • It inherits the parallelism of the original Stochastic Lanczos scheme for scalability.
  • Enables fully converged and efficient periodic boundary conditions treatment in MBD.