1,721,091 research outputs found
Initial stages of salt crystal dissolution determined with ab initio molecular dynamics
No full textThe initial stages of NaCl dissolution in liquid water have been examined with state-of-the-art ab initio molecular dynamics and free energy sampling techniques. Our simulations reveal a complex multi-step process triggered by the departure of Cl ions from the lattice, with a well-defined intermediate state wherein departing ions are partially solvated but remain in contact with the crystal. The polarizability of Cl- is identified as the source of the anion's preferential initial dissolution, an effect which leads a forcefield based description of NaCl dissolution to fail to identify a preference for Cl over Na dissolution
How accurate are simulations and experiments for the lattice energies of molecular crystals?
Full text linkMolecular crystals play a central role in a wide range of scientific fields,
including pharmaceuticals and organic semiconductor devices. However, they are
challenging systems to model accurately with computational approaches because
of a delicate interplay of intermolecular interactions such as hydrogen bonding
and van der Waals dispersion forces. Here, by exploiting recent algorithmic
developments, we report the first set of diffusion Monte Carlo lattice energies
for all 23 molecular crystals in the popular and widely used X23 dataset.
Comparisons with previous state-of-the-art lattice energy predictions (on a
subset of the dataset) and a careful analysis of experimental sublimation
enthalpies reveals that high-accuracy computational methods are now at least as
reliable as (computationally derived) experiments for the lattice energies of
molecular crystals. Overall, this work demonstrates the feasibility of
high-level explicitly correlated electronic structure methods for broad
benchmarking studies in complex condensed phase systems, and signposts a route
towards closer agreement between experiment and simulation
Perspective: How good is DFT for water?
Full text linkKohn-Sham density functional theory (DFT) has become established as an indispensable tool for investigating aqueous systems of all kinds, including those important in chemistry, surface science, biology and the earth sciences. Nevertheless, many widely used approximations for the exchange-correlation (XC) functional describe the properties of pure water systems with an accuracy that is not fully satisfactory. The explicit inclusion of dispersion interactions generally improves the description, but there remain large disagreements between the predictions of different dispersion-inclusive methods. We present here a review of DFT work on water clusters, ice structures and liquid water, with the aim of elucidating how the strengths and weaknesses of different XC approximations manifest themselves across this variety of water systems. Our review highlights the crucial role of dispersion in describing the delicate balance between compact and extended structures of many different water systems, including the liquid. By referring to a wide range of published work, we argue that the correct description of exchange-overlap interactions is also extremely important, so that the choice of semi-local or hybrid functional employed in dispersion-inclusive methods is crucial. The origins and consequences of beyond-2-body errors of approximate XC functionals are noted, and we also discuss the substantial differences between different representations of dispersion. We propose a simple numerical scoring system that rates the performance of different XC functionals in describing water systems, and we suggest possible future developments
Interaction strength of carbon dioxide on graphene from periodic quantum diffusion Monte Carlo
Full text linkOn the increase of the melting temperature of water confined in one-dimensional nano-cavities
Full text linkWater confined in nanoscale cavities plays a crucial role in everyday
phenomena in geology and biology, as well as technological applications at the
water-energy nexus. However, even understanding the basic properties of
nano-confined water is extremely challenging for theory, simulations, and
experiments. In particular, determining the melting temperature of
quasi-one-dimensional ice polymorphs confined in carbon nanotubes has proven to
be an exceptionally difficult task, with previous experimental and classical
simulations approaches report values ranging from up to
at ambient pressure. In this work, we use a machine
learning potential that delivers first principles accuracy to study the phase
diagram of water for confinement diameters . We
find that several distinct ice polymorphs melt in a surprisingly narrow range
between and , with a melting mechanism
that depends on the nanotube diameter. These results shed new light on the
melting of ice in one-dimension and have implications for the operating
conditions of carbon-based filtration and desalination devices
How strongly do hydrogen and water molecules stick to carbon nanomaterials?
Full text linkThe interaction strength of molecular hydrogen and water to carbon nanomaterials is relevant to, among many applications, hydrogen storage, water treatment, and water flow. However, accurate interaction energies for hydrogen and water with carbon nanotubes (CNTs) remain scarce despite the importance of having reliable benchmark data to inform experiments and to validate computational models. Here, benchmark fixed-node diffusion Monte Carlo (DMC) interaction energies are provided for hydrogen and water monomers inside and outside a typical zigzag CNT. The DMC interaction energies provide valuable insight into molecular interactions with CNTs in general and are also expected to be particularly relevant to gas uptake studies on CNTs. In addition, a selection of density functional theory (DFT) exchange-correlation (xc) functionals and force field potentials that ought to be suitable for these systems is compared. An unexpected variation is found in the performance of DFT van der Waals (vdW) models in particular. An analysis of the peculiar discrepancy between different vdW models indicates that medium-range correlation (at circa 3 to 5 Å) plays a key role inside CNTs and is poorly predicted by some vdW models. Using accurate reference information, this work reveals which xc functionals and force fields perform well for molecules interacting with CNTs. The findings will be valuable to future work on these and related systems that involve molecules interacting with low-dimensional systems
Interaction between water and carbon nanostructures: How good are current density functional approximations?
Full text linkDue to their current and future technological applications, including realisation of water filters and desalination membranes, water adsorption on graphitic sp-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 non-covalent 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 \textbf{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 semi-local exchange-correlation functionals, non-local (Fock) exchange contributions, and long-range van der Waals (vdW) treatments, are tested by computing the deviations from the reference interaction energies. The calculated mean absolute deviations on the WaC18 set depends crucially on the DFT approach, ranging from 135 meV for 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
Development of a machine learning potential for graphene
Full text linkWe present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org]
DMC-ICE13: ambient and high pressure polymorphs of ice from Diffusion Monte Carlo and Density Functional Theory
Full text linkIce is one of the most important and interesting molecular crystals exhibiting a rich and evolving phase diagram. Recent discoveries mean that there are now twenty distinct polymorphs; a structural diversity that arises from a delicate interplay of hydrogen bonding and van der Waals dispersion forces. This wealth of structures provides a stern test of electronic structure theories, with Density Functional Theory (DFT) often not able to accurately characterise the relative energies of the various ice polymorphs. Thanks to recent advances that enable the accurate and efficient treatment of molecular crystals with Diffusion Monte Carlo (DMC), we present here the DMC-ICE13 dataset; a dataset of lattice energies of 13 ice polymorphs. This dataset encompasses the full structural complexity found in the ambient and high-pressure molecular ice polymorphs and when experimental reference energies are available our DMC results deliver sub-chemical accuracy. Using this dataset we then perform an extensive benchmark of a broad range of DFT functionals. Of the functionals considered, we find revPBE-D3 and RSCAN to reproduce reference absolute lattice energies with the smallest error, whilst optB86b-vdW and SCAN+rVV10 have the best performance on the relative lattice energies. Our results suggest that a single functional achieving reliable performance for all phases is still missing, and that care is needed in the selection of the most appropriate functional for the desired application.The insights obtained here may also be relevant to liquid water and other hydrogen bonded and dispersion bonded molecular crystals
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