AI Article Synopsis
- The study compares the performance of the SCAN meta-GGA and PBE-GGA functionals in simulating water through ab initio molecular dynamics.
- While SCAN meta-GGA doesn't outperform the TIP4P/2005 model in accuracy for the equation of state, it significantly surpasses PBE-GGA and other neural network potentials.
- The SCAN meta-GGA predicts a first-order liquid-liquid transition in water at negative pressure, aligning well with experimental findings and indicating proximity to critical stability limits in high-density water.
Article Abstract
Based on the newly developed SCAN meta-GGA and the widely used PBE-GGA functionals, ab initio molecular dynamics are performed on water. It is proved that, although the SCAN meta-GGA is not as good as the TIP4P/2005 model potential in describing the equation of state of water, it is much better than the PBE-GGA, the ST2 model potential, and ab initio trained neural network potentials. Moreover, the SCAN meta-GGA predicts a first-order liquid-liquid transition from high- to low-density water at negative pressure, in which the structures are qualitatively consistent with experimental observations, and the spinodal point of high-density water is very close to Speedy's stability limit line.
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| http://dx.doi.org/10.1039/c9cp06362e | DOI Listing |
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