Quantum-mechanical exploration of the phase diagram of water

TL;DR

This study uses advanced quantum-mechanical simulations combined with machine learning to explore water's phase diagram, achieving qualitative agreement with experiments and assessing the stability of various ice phases.

Contribution

It introduces a first-principles approach to predict water's phase diagram, integrating hybrid density functional theory with machine learning and free-energy methods.

Findings

Qualitative agreement with experimental phase diagram at pressures below 8000 bar.

No hypothetical ice phases found to be thermodynamically stable in the studied region.

Demonstrates the feasibility of first-principles prediction of polymorphic phase diagrams.

Abstract

The phase diagram of water harbours many mysteries: some of the phase boundaries are fuzzy, and the set of known stable phases may not be complete. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures $\lesssim$8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.