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.
An accurate, transferrable, and computationally efficient potential energy surface (PES) is of paramount importance for all molecular mechanics simulations. In this work, using water as example, we demonstrate how one can construct a reliable force field by combining the advantages of both physically-motivated and data-driven machine learning (ML) methods. Different to the existing water models based on many-body expansion, we adopt a separation scheme purely based on distances, and systematically investigate how the long-range asymptotic terms increase the transferability and the data efficiency of the ML potential. We also show how the ML model can be an ideal tool to fit the short-range interactions which used to post great challenges to the conventional physically-motivated force fields. The water force field we obtain is highly accurate and transferrable in different environments, and the distance-based separation scheme is easy to be extended to general molecular systems. Through this study, we show how the information we learn from small clusters can be extrapolated into larger systems, thus providing a general recipe for the intermolecular force field development at CCSD(T) level of theory in future.
An accurate, transferrable, and computationally efficient potential energy surface (PES) is of paramount importance for all molecular mechanics simulations. In this work, using water as example, we demonstrate how one can construct a reliable force field by combining the advantages of both physically-motivated and data-driven machine learning (ML) methods. Different to the existing water models based on many-body expansion, we adopt a separation scheme purely based on distances, and systematically investigate how the long-range asymptotic terms increase the transferability and the data efficiency of the ML potential. We also show how the ML model can be an ideal tool to fit the short-range interactions which used to post great challenges to the conventional physically-motivated force fields. The water force field we obtain is highly accurate and transferrable in different environments, and the distance-based separation scheme is easy to be extended to general molecular systems. Through this study, we show how the information we learn from small clusters can be extrapolated into larger systems, thus providing a general recipe for the intermolecular force field development at CCSD(T) level of theory in future.
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