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Thermodynamic picture of vitrification of water through complex specific heat and entropy: A journey through "no man's land".

Shinji SaitoBiman Bagchi
Published in: The Journal of chemical physics (2019)
We investigate thermodynamic properties of supercooled water across the "no man's land" onto the formation of amorphous ice. The calculations are aided by very long computer simulations, often more than 50 μs long, with the TIP4P/2005 model potential. Density fluctuations that arise from the proximity to a putative liquid-liquid (LL) transition at 228 K, cast a long shadow on the properties of water, both above and below the LL transition. We carry out the calculations of the quantum mechanical static and frequency-dependent specific heats by combining seminal studies of Lebowitz, Percus, and Verlet and Grest and Nagel with the harmonic approximation for the density of states. The obtained values are in quantitative agreement with all available experimental and numerical results of specific heats for both supercooled water and ice. We calculate the entropy at all the state points by integrating the specific heat. We find that the quantum corrected-contributions of intermolecular vibrational entropy dominate the excess entropy of amorphous phases over the crystal over a wide range of temperatures. Interestingly, the vibrational entropy lowers the Kauzmann temperature, TK, to 130 K, just below the experimental glass-to-liquid water transition temperature, Tg, of 136 K and the calculated Tg of 135 K in our previous study. A straightforward extrapolation of high temperature entropy from 250 K to below however would give a much higher value of TK ∼ 190 K. The calculation of Lindemann ratios shows the melting of amorphous ice ∼135 K. The amorphous state exhibits an extremely short correlation length for the distance dependence of orientational correlation.
Keyphrases
  • molecular dynamics
  • density functional theory
  • monte carlo
  • molecular dynamics simulations
  • room temperature
  • energy transfer
  • climate change
  • high resolution
  • high temperature
  • solid state
  • machine learning