2021/03/16 by Bingqing Cheng, Mandy Bethkenhagen, Chris J. Pickard +2 · 80 citations
Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical physics #Condensed matter physics #Diffusion #Energy landscape #Machine Learning in Materials Science #Materials science #Nanopore #Nanotechnology #Phase (matter) #Phase diagram #Phase transition #Physics #Premelting #Range (aeronautics) #Spectroscopy and Quantum Chemical Studies #Stacking #Thermodynamics #astro-ph.EP #cond-mat.mtrl-sci #cond-mat.stat-mech #physics.comp-ph
paper · pdf · doi:10.1038/s41567-021-01334-9
published in Nature Physics 17(11), 1228-1232 (Nature Portfolio)
arxiv created 2021/03/16 · openalex publication_date 2021/09/23 · openalex created_date 2021/09/27 · arxiv updated 2021/11/24 · openalex updated_date 2026/08/05
Most water in the universe may be superionic, and its thermodynamic and transport properties are crucial for planetary science but difficult to probe experimentally or theoretically. We use machine learning and free energy methods to overcome the limitations of quantum mechanical simulations, and characterize hydrogen diffusion, superionic transitions, and phase behaviors of water at extreme conditions. We predict that a close-packed superionic phase with mixed stacking is stable over a wide temperature and pressure range, while a body-centered cubic phase is only thermodynamically stable in a small window but is kinetically favored. Our phase boundaries, which are consistent with the existing-albeit scarce-experimental observations, help resolve the fractions of insulating ice, different superionic phases, and liquid water inside of ice giants.