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We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), ahybrid approach between machine-learning and force-fields. We leveragestate-of-the-art equivariant neural networks to predict local energycontributions and multiple atom-in-molecule properties that are then used asgeometry-dependent parameters for physically-motivated energy terms whichaccount for long-range electrostatics and dispersion. Using high-accuracy abinitio data (small organic molecules/dimers), we trained a first version of themodel. Exhibiting accurate gas-phase energy predictions, FENNIX is transferableto the condensed phase. It is able to produce stable Molecular Dynamicssimulations, including nuclear quantum effects, for water predicting accurateliquid properties. The extrapolating power of the hybrid physically-drivenmachine learning FENNIX approach is exemplified by computing: i) the solvatedalanine dipeptide free energy landscape; ii) the reactive dissociation of smallmolecules.

Force-Field-Enhanced Neural Network Interactions: from Local Equivariant Embedding to Atom-in-Molecule properties and long-range effects