4.7 Article

Interaction between water and carbon nanostructures: How good are current density functional approximations?

期刊

JOURNAL OF CHEMICAL PHYSICS
卷 151, 期 16, 页码 -

出版社

AIP Publishing
DOI: 10.1063/1.5121370

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资金

  1. Alexander von Humboldt Foundation
  2. Air Force Office of Scientific Research, Air Force Material Command, U.S. Air Force [FA9550-19-1-7007]
  3. European Research Council (ERC) under the European Union's Seventh Framework Program (No. FP/2007-2013)/ERC Grant [616121]
  4. DOE Office of Science User Facility [DE-AC05-00OR22725]
  5. ARCHER UK National Supercomputing Service, United Kingdom Car-Parrinello (UKCP) consortium [EP/F036884/1]
  6. EPSRC [EP/P020194/1]
  7. EPSRC [EP/P022561/1, EP/P020194/1, EP/F036884/1] Funding Source: UKRI

向作者/读者索取更多资源

Due to their current and future technological applications, including realization of water filters and desalination membranes, water adsorption on graphitic sp(2)-bonded carbon is of overwhelming interest. However, these systems are notoriously challenging to model, even for electronic structure methods such as density functional theory (DFT), because of the crucial role played by London dispersion forces and noncovalent interactions, in general. Recent efforts have established reference quality interactions of several carbon nanostructures interacting with water. Here, we compile a new benchmark set (dubbed WaC18), which includes a single water molecule interacting with a broad range of carbon structures and various bulk (3D) and two-dimensional (2D) ice polymorphs. The performance of 28 approaches, including semilocal exchange-correlation functionals, nonlocal (Fock) exchange contributions, and long-range van der Waals (vdW) treatments, is tested by computing the deviations from the reference interaction energies. The calculated mean absolute deviations on the WaC18 set depend crucially on the DFT approach, ranging from 135 meV for local density approximation (LDA) to 12 meV for PBE0-D4. We find that modern vdW corrections to DFT significantly improve over their precursors. Within the 28 tested approaches, we identify the best performing within the functional classes of generalized gradient approximated (GGA), meta-GGA, vdW-DF, and hybrid DF, which are BLYP-D4, TPSS-D4, rev-vdW-DF2, and PBE0-D4, respectively. Published under license by AIP Publishing.

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