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 molecular many-body expansion, we adopt a separation scheme purely based on distances, which is more convenient for generic molecular systems. The new potential provides a physically interpretable energy decomposition, and in the meantime, is much more accurate than the conventional physically-motived potentials. Most importantly, through this study, we show that 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.
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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