Accurate Deep Learning-Aided Density-Free Strategy for Many-Body Dispersion-Corrected Density Functional Theory

Using
a deep neuronal network (DNN) model trained on the large
ANI-1 data set of small organic molecules, we propose a transferable
density-free many-body dispersion (DNN-MBD) model. The DNN strategy
bypasses the explicit Hirshfeld partitioning of the Kohn–Sham
electron density required by MBD models to obtain the atom-in-molecules
volumes used by the Tkatchenko–Scheffler polarizability rescaling.
The resulting DNN-MBD model is trained with minimal basis iterative
Stockholder atomic volumes and, coupled to density functional theory
(DFT), exhibits comparable (if not greater) accuracy to other approaches
based on different partitioning schemes. Implemented in the Tinker-HP
package, the DNN-MBD model decreases the overall computational cost
compared to MBD models where the explicit density partitioning is
performed. Its coupling with the recently introduced Stochastic formulation
of the MBD equations (J. Chem. Theory Comput. 2022, 18 (3), 1633–1645) enables large
routine dispersion-corrected DFT calculations at preserved accuracy.
Furthermore, the DNN electron density-free features extend the MBD
model’s applicability beyond electronic structure theory within
methodologies such as force fields and neural networks.