Prior revPBE-D3 DFT simulations of liquid water showed >20% variation in diffusion and 10% in density; new ML-potential-enabled benchmarks resolve these to consistent values across six codes, attributing prior errors to basis-set and sampling issues.
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Machine-learning interatomic potentials trained on prior ab initio data for multiple DFT functionals are used to compare water dynamical properties, with RPBE-D3/zd identified as best matching experiment and further validated across conditions.
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How reproducible are first-principles simulations of liquid water?
Prior revPBE-D3 DFT simulations of liquid water showed >20% variation in diffusion and 10% in density; new ML-potential-enabled benchmarks resolve these to consistent values across six codes, attributing prior errors to basis-set and sampling issues.
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Dynamical properties of ab initio water from machine-learning potentials
Machine-learning interatomic potentials trained on prior ab initio data for multiple DFT functionals are used to compare water dynamical properties, with RPBE-D3/zd identified as best matching experiment and further validated across conditions.