期刊
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
卷 116, 期 4, 页码 1110-1115出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1815117116
关键词
ab initio thermodynamics; machine-learning potential; water; density functional theory; nuclear quantum effects
资金
- Swiss National Science Foundation [200021-159896]
- International Center for Advanced Studies of Energy Conversion
- Swiss National Supercomputing Center [s787]
- European Research Council under European Union [677013-HBMAP]
- DFG [BE3264/11-2]
- Austrian Science Fund Spezialforschungsbereich Vienna Computational Materials Laboratory Project [F41]
Thermodynamic properties of liquid water as well as hexagonal (Ih) and cubic (Ic) ice are predicted based on density functional theory at the hybrid-functional level, rigorously taking into account quantum nuclear motion, anharmonic fluctuations, and proton disorder. This is made possible by combining advanced free-energy methods and state-of-the-art machine-learning techniques. The ab initio description leads to structural properties in excellent agreement with experiments and reliable estimates of the melting points of light and heavy water. We observe that nuclear-quantum effects contribute a crucial 0.2 meV/H2O to the stability of ice Ih, making it more stable than ice Ic. Our computational approach is general and transferable, providing a comprehensive framework for quantitative predictions of ab initio thermodynamic properties using machine-learning potentials as an intermediate step.
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