Ab initio theory and modeling of water

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 114; no. 41; pp. 10846 - 10851
• Main Authors: Chen, Mohan, Ko, Hsin-Yu, Remsing, Richard C., Andrade, Marcos F. Calegari, Santra, Biswajit, Sun, Zhaoru, Selloni, Annabella, Car, Roberto, Klein, Michael L., Perdew, John P., Wu, Xifan
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 10.10.2017; National Academy of Sciences, Washington, DC (United States)
• Subjects: ab initio theory; catalysis (heterogeneous); Covalent bonds; defects; density functional theory; energy storage (including batteries and capacitors); Fluid mechanics; hydrogen and fuel cells; Hydrogen bonding; Hydrogen bonds; INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; materials and chemistry by design; Mathematical models; MATHEMATICS AND COMPUTING; mechanical behavior; molecular dynamics; Physical Sciences; Predictions; solar (photovoltaic); synthesis (novel materials); Water; hydrogen bonding; ab initio theory; molecular dynamics; water; density functional theory
• ISSN: 0027-8424; 1091-6490; 1091-6490
• DOI: 10.1073/pnas.1712499114

Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice Ih at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.