Assessment of Density Functional Theory in Predicting Interaction Energies between Water and Polycyclic Aromatic Hydrocarbons: from Water on Benzene to Water on Graphene

Abstract

The interactions of water with polycyclic aromatic hydrocarbons, from benzene to graphene, are investigated using various exchange-correlation functionals selected across the hierarchy of density functional theory (DFT) approximations. The accuracy of the different functionals is assessed through comparisons with random phase approximation (RPA) and coupled-cluster with single, double, and perturbative triple excitations [CCSD(T)] calculations. Diffusion Monte Carlo (DMC) data reported in the literature are also used for comparison. Relatively large variations are found in interaction energies predicted by different DFT models, with GGA functionals underestimating the interaction strength for configurations with the water oxygen pointing toward the aromatic molecules. The meta-GGA B97M-rV and range-separated hybrid, meta-GGA ωB97M-V functionals provide nearly quantitative agreement with CCSD(T) values for the water-benzene, water-coronene, and water-circumcoronene dimers, while RPA and DMC predict interaction energies that differ by up to ∼1 kcal/mol and ∼0.4 kcal/mol from the corresponding CCSD(T) values, respectively. Similar trends among GGA, meta-GGA, and hybrid functionals are observed for larger polycyclic aromatic hydrocarbons. By performing absolutely localized molecular orbital energy decomposition analyses (ALMO-EDA), it is found that, independently of the number of carbon atoms and exchange-correlation functional, the dominant contributions to the interaction energies between water and polycyclic aromatic hydrocarbon molecules are the electrostatic and dispersion terms while polarization and charge transfer effects are negligibly small. Calculations carried out with GGA and meta-GGA functionals indicate that, as the number of carbon atoms increases, the interaction energies slowly converge to the corresponding values obtained for an infinite graphene sheet. 2

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

“…We test as many as 28 DFAs on the WaC18 set, including several recently developed vdW corrections to DFAs. Some DFT benchmarks already exist on these or related system types, [58][59][60][61][62][63][64][65][66][67][68][69][70][71][72][73][74][75][76] but they typically include a limited set of DFAs, the recent vdW developments are not included, and the used reference data is not equally well converged. Here, we will address all of these issues.…”

Transparent proton transport through a two-dimensional nanomesh material

“…Calculations with hybrid functional B3LYP-D3 verify that the choice of exchange-correlation functionals does not affect the qualitative conclusions of this work (Supplementary Note 4). We note that BLYP-D3 exhibits an overestimate of adsorption energy of a water molecule on graphene 49 and on graphdiyne here (Supplementary Fig. 12b); however, the characteristic interfacial water structure is well described in BLYP-D3 (Supplementary Figs.…”

“…Due to the delicate interaction of water, none of the existing functionals is able to universally and faultlessly describe water 41,42 and its self-ions 43–45 in various realistic conditions. In particular, the water–carbon interaction is demonstrated to be extremely difficult to simulate, even high-cost diffusion Monte Carlo, coupled cluster theory, and random phase approximation give rise to various adsorption energies of a single water molecule absorbed on graphene (Supplementary Tables 1 and2 and Supplementary Note 5) 46–49 . Although dispersion correction remedies the bad performance of DFT in describing water–carbon adsorption interactions 46,47 , relatively large variations are still found in interaction energies between a single water molecule and graphene predicted by different DFT models 49,50 .…”

“…In particular, the water–carbon interaction is demonstrated to be extremely difficult to simulate, even high-cost diffusion Monte Carlo, coupled cluster theory, and random phase approximation give rise to various adsorption energies of a single water molecule absorbed on graphene (Supplementary Tables 1 and2 and Supplementary Note 5) 46–49 . Although dispersion correction remedies the bad performance of DFT in describing water–carbon adsorption interactions 46,47 , relatively large variations are still found in interaction energies between a single water molecule and graphene predicted by different DFT models 49,50 . Despite these discrepancies, dispersion-corrected generalized gradient approximation (GGA) still gives rise to reasonable interfacial structures between liquid water and graphene 51,52 or carbon nanotubes 53 , and describes well the monolayer ice on graphite 54 .…”

e-Graphene: A Computational Platform for the Prediction of Graphene-Based Drug Delivery System by Quantum Genetic Algorithm and Cascade Protocol

“…Regarding the computational predictions of GDDS, molecular dynamics (MD) simulation and quantum mechanics (QM) programs are powerful to accurately investigate the non-bonded interaction between the graphene/GO and ligand for a given GDDS (Gráfová et al, 2010;Ramraj and Hillier, 2010;Calero et al, 2013;Cho et al, 2013;Guo et al, 2013;Vovusha et al, 2013Vovusha et al, , 2018Mudedla et al, 2014;Vincent and Hillier, 2014;Wang et al, 2015;Mahdavi et al, 2016Mahdavi et al, , 2020Krepel and Hod, 2017;Safdari et al, 2017;Ajala et al, 2019;Alkathiri et al, 2019;Azhagiya Singam et al, 2019;Mason et al, 2019). However, both MD and QM methods are usually time-consuming, need to know a reasonable initial binding mode between the graphene/GO and ligand before calculations and tend to entrap the graphene/GO and ligand in a local minimum.…”