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We use the MB-pol theoretical/computational framework to introduce a new family of data-driven many-body potential energy functions (PEFs) for water, named MB-pol(2023). By employing larger 2-body and 3-body training sets, including an explicit machine-learned representation of 4-body energies, and adopting more sophisticated machine-learned representations of 2-body and 3-body energies, we demonstrate that the MB-pol(2023) PEFs achieve sub-chemical accuracy in modeling the energetics of the hexamer isomers, outperforming both the original MB-pol and q-AQUA PEFs, which currently provide the most accurate description of water clusters in the gas phase. Importantly, the MB-pol(2023) PEFs provide remarkable agreement with the experimental results for various properties of liquid water, improving upon the original MB-pol PEF and effectively closing the gap with experimental measurements.
Zhu et al. (Tue,) studied this question.