Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects.

ABSTRACT: We introduce FENNIX (Force-Field-Enhanced Neural Network InteraXions), a hybrid approach between machine-learning and force-fields. We leverage state-of-the-art equivariant neural networks to predict local energy contributions and multiple atom-in-molecule properties that are then used as geometry-dependent parameters for physically-motivated energy terms which account for long-range electrostatics and dispersion. Using high-accuracy ab initio data (small organic molecules/dimers), we trained a first version of the model. Exhibiting accurate gas-phase energy predictions, FENNIX is transferable to the condensed phase. It is able to produce stable Molecular Dynamics simulations, including nuclear quantum effects, for water predicting accurate liquid properties. The extrapolating power

SUBMITTER: Ple T 

PROVIDER: S-EPMC10646944 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature