Machine learning and many-body molecular interactions

Machine learning (ML) potentials, particularly those based on deep neural networks (DNNs), have emerged as powerful tools for simulating molecular systems with a broad spectrum of applications, spanning liquids to materials. In our work, we aim at combining the computational prowess of the DeePMD framework with the proven accuracy of MB-pol, a data-driven many-body potential, to develop a DNN potential for large-scale simulations of water across various phases. Our findings underscore that while the DNN potential reliably reproduces the MB-pol results for liquid water, it falters in accurately describing the vapor-liquid equilibrium properties—a shortcoming rooted in the DNN potential's inability to correctly "learn" many-body interactions. Our attemp at encoding explicit many-body effects information leads to a new DNN potential, which albeit accurately rendering the MB-pol vapor-liquid equilibrium properties, stumbled in capturing liquid properties. Nevertheless, the computational efficiency of DeePMD holds promise for training DNN potentials on data-driven many-body potentials, thereby unlocking the potential for large-scale, chemically accurate simulations of water and aqueous solutions. This promise comes with a caveat—the targeted state points must be well-represented in the training phase by the reference data-driven many-body potential to ensure an accurate portrayal of the associated properties.