2026/07/16 by Victoria Valeeva, Cheuk Hin Ho, Mario Geiger +4
#physics.comp-ph
The DFT-D3 dispersion correction is routinely added to machine learning force fields (MLFFs) trained on dispersion-deficient functionals such as PBE. Its environment-dependent pair coefficients, however, break the atom-centered separability that fast summation methods require, forcing practitioners either to truncate D3 or to accept a substantial slowdown. We introduce FourierD3, a method that uses a functional low-rank decomposition to restore this separability and enable particle-mesh evaluation in O(Nlog N) time without a real-space cutoff on the dispersion sum.