4 research outputs found
Machine Learning of Atomic Forces from Quantum Mechanics: a Model Based on Pairwise Interatomic Forces
We present a new chemically intuitive
approach, pairF-Net, to directly predict the atomic forces
in a molecule to quantum chemistry accuracy using machine learning
techniques. A residual artificial neural network has been designed and trained with
features and targets based on pairwise interatomic forces,
to determine the Cartesian atomic forces suitable for use in
molecular mechanics and dynamics calculations. The scheme
implicitly maintains rotational and translational invariance
and predicts Cartesian forces as a linear combination of a set of force
components in an interatomic basis. We
show that the method can predict the reconstructed Cartesian atomic forces for a
set of small organic molecules to less than 2 kcal mol-1 Ã…-1
from the reference force values obtained via density functional theory. The
pairF-Net scheme utilises a simple and chemically intuitive route to furnish atomic
forces at a quantum mechanical level but at a fraction of the cost, providing a
step towards the efficient calculation of accurate thermodynamic
properties. </p
A neural network potential based on pairwise resolved atomic forces and energies
Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq(https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.<br/