  Using a Deep Neuronal Network model (DNN) trained on the large ANI-1 data set
of small organic molecules, we propose a transferable density-free many-body
dispersion model (DNN-MBD). 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 MBD's applicability beyond electronic
structure theory within methodologies such as force fields and neural networks.
