Many-body interactions and deep neural network potentials for water

21 March 2024, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


We present a detailed assessment of deep neural network potentials developed within the DeePMD framework and trained on the MB-pol data-driven many-body potential energy function. Specific focus is directed at the ability of DeePMD-based potentials to correctly reproduce the accuracy of MB-pol across various water systems. Analyses of bulk and interfacial properties as well as many-body interactions characteristic of water elucidate inherent limitations in the transferability and predictive accuracy of DeePMD-based potentials. These limitations can be traced back to an incomplete implementation of the “nearsightedness of electronic matter” principle, which may be common throughout machine learning potentials that do not include a proper representation of self-consistently determined long-range electric fields. These findings provide further support for the "short-blanket dilemma" faced by DeePMD-based potentials, highlighting the challenges in achieving a balance between computational efficiency and a rigorous, physics-based representation of the properties of water. Finally, we believe that our study contributes to the ongoing discourse on the de- velopment and application of machine learning models in simulating water systems, offering insights that could guide future improvements in the field.


machine learning
neural networks
many-body interactions
phase behavior
molecular dynamics

Supplementary materials

Supplementary Material
Overview of the data-driven many-body formalism behind MB-pol and summary of the main results obtained so far using MB-pol.


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