In the simulation of molecular systems, the underlying force field (FF) model plays an extremely important role, determining the reliability of the simulation. However, the quality of the state-of-the-art molecular force fields is still unsatisfactory in many cases, and the FF parameterization process largely relies on human experience, which is not scalable. To address this issue, we introduce DMFF, an open-source molecular FF development platform based on automatic differentiation technique. DMFF serves as a powerful tool for both top-down and bottom-up FF development. Using DMFF, both energies/forces and thermodynamic quantities such as ensemble averages and free energies can be evaluated in a differentiable way, realizing an automatic, yet highly flexible force field optimization workflow. DMFF also eases the evaluation of forces and virial tensors for complicated advance force fields, helping the fast validation of new models in molecular dynamics simulation. DMFF has been released as an opensource package under the LGPL-3.0 license and is available at https://github.com/deepmodeling/DMFF.
DMFF: An Open-Source Automatic
Differentiable Platform for Molecular Force Field
Development and Molecular Dynamics
24 November 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.