Can Ice-Like Structures Form on Non-Ice-Like Substrates? The Example of the K-feldspar Microcline

Feldspar minerals are the most common rock formers in Earth’s crust. As such they play an important role in subjects ranging from geology to climate science. An atomistic understanding of the feldspar structure and its interaction with water is therefore desirable, not least because feldspar has been shown to dominate ice nucleation by mineral dusts in Earth’s atmosphere. The complexity of the ice/feldspar interface arising from the numerous chemical motifs expressed on the surface makes it a challenging system. Here we report a comprehensive study of this challenging system with ab initio density functional theory calculations. We show that the distribution of Al atoms, which is crucial for the dissolution kinetics of tectosilicate minerals, differs significantly between the bulk environment and on the surface. Furthermore, we demonstrate that water does not form ice-like overlayers in the contact layer on the most easily cleaved (001) surface of K-feldspar. We do, however, identify contact layer structures of water that induce ice-like ordering in the second overlayer. This suggests that even substrates without an apparent match with the ice structure may still act as excellent ice nucleating agents

A machine learning potential for hexagonal boron nitride applied to thermally and mechanically induced rippling

We introduce an interatomic potential for hexagonal boron nitride (hBN) based on the Gaussian approximation potential (GAP) machine learning methodology. The potential is based on a training set of configurations collected from density functional theory (DFT) simulations and is capable of treating bulk and multilayer hBN as well as nanotubes of arbitrary chirality. The developed force field faithfully reproduces the potential energy surface predicted by DFT while improving the efficiency by several orders of magnitude. We test our potential by comparing formation energies, geometrical properties, phonon dispersion spectra, and mechanical properties with respect to benchmark DFT calculations and experiments. In addition, we use our model and a recently developed graphene-GAP to analyze and compare thermally and mechanically induced rippling in large scale two-dimensional (2D) hBN and graphene. Both materials show almost identical scaling behavior with an exponent of η ≈ 0.85 for the height fluctuations agreeing well with the theory of flexible membranes. On the basis of its lower resistance to bending, however, hBN experiences slightly larger out-of-plane deviations both at zero and finite applied external strain. Upon compression, a phase transition from incoherent ripple motion to soliton-ripples is observed for both materials. Our potential is freely available online at [http://www.libatoms.org]

An accurate and transferable machine learning potential for carbon

We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csányi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon

Ice formation on kaolinite: Insights from molecular dynamics simulations

The formation of ice affects many aspects of our everyday life as well as important technologies such as cryotherapy and cryopreservation. Foreign substances almost always aid water freezing through heterogeneous ice nucleation, but the molecular details of this process remain largely unknown. In fact, insight into the microscopic mechanism of ice formation on different substrates is difficult to obtain even if state-of-the-art experimental techniques are used. At the same time, atomistic simulations of heterogeneous ice nucleation frequently face extraordinary challenges due to the complexity of the water-substrate interaction and the long time scales that characterize nucleation events. Here, we have investigated several aspects of molecular dynamics simulations of heterogeneous ice nucleation considering as a prototypical ice nucleating material the clay mineral kaolinite, which is of relevance in atmospheric science. We show via seeded molecular dynamics simulations that ice nucleation on the hydroxylated (001) face of kaolinite proceeds exclusively via the formation of the hexagonal ice polytype. The critical nucleus size is two times smaller than that obtained for homogeneous nucleation at the same supercooling. Previous findings suggested that the flexibility of the kaolinite surface can alter the time scale for ice nucleation within molecular dynamics simulations. However, we here demonstrate that equally flexible (or non flexible) kaolinite surfaces can lead to very different outcomes in terms of ice formation, according to whether or not the surface relaxation of the clay is taken into account. We show that very small structural changes upon relaxation dramatically alter the ability of kaolinite to provide a template for the formation of a hexagonal overlayer of water molecules at the water-kaolinite interface, and that this relaxation therefore determines the nucleation ability of this mineral

Defect-Dependent Corrugation in Graphene

Graphene’s intrinsically corrugated and wrinkled topology fundamentally influences its electronic, mechanical, and chemical properties. Experimental techniques allow the manipulation of pristine graphene and the controlled production of defects which allows one to control the atomic out-of-plane fluctuations and thus tune graphene’s properties. Here, we perform large scale machine learning-driven molecular dynamics simulations to understand the impact of defects on the structure of graphene. We find that defects cause significantly higher corrugation leading to a strongly wrinkled surface. The magnitude of this structural transformation strongly depends on the defect concentration and specific type of defect. Analyzing the atomic neighborhood of the defects reveals that the extent of these morphological changes depends on the preferred geometrical orientation and the interactions between defects. While our work highlights that defects can strongly affect graphene’s morphology, it also emphasizes the differences between distinct types by linking the global structure to the local environment of the defects

A statistical analysis of sounding derived indices and parameters for extreme and non-extreme thunderstorm events over Cyprus

The main purpose of this study is to provide a simple statistical analysis of several stability indices and parameters for extreme and non-extreme thunderstorm events during the period 1997 to 2001 in Cyprus. For this study, radiosonde data from Athalassa station (35°1´ N, 33°4´ E) were analyzed during the aforementioned period. The stability indices and parameters set under study are the K index, the Total Totals (TT) index, the Convective Available Potential Energy related parameters such as Convective Available Potential Energy (CAPE), Downdraft CAPE (DCAPE) and the Convective Inhibition (CIN), the Vorticity Generator Parameter (VGP), the Bulk Richardson Number (BRN), the BRN Shear and the Storm Relative Helicity (SRH). An event is categorized as extreme, if primarily, CAPE was non zero and secondary, if values of both the K and the TotalTotals (TT) indices exceeded 26.9 and 50, respectively. The cases with positive CAPE but lower values of the other indices, were identified as non-extreme. By calculating the median, the lower and upper limits, as well as the lower and upper quartiles of the values of these indices, the main characteristics of their distribution were determined

Can aerosols be trapped in open flows?

The fate of aerosols in open flows is relevant in a variety of physical contexts. Previous results are consistent with the assumption that such finite-size particles always escape in open chaotic advection. Here we show that a different behavior is possible. We analyze the dynamics of aerosols both in the absence and presence of gravitational effects, and both when the dynamics of the fluid particles is hyperbolic and nonhyperbolic. Permanent trapping of aerosols much heavier than the advecting fluid is shown to occur in all these cases. This phenomenon is determined by the occurrence of multiple vortices in the flow and is predicted to happen for realistic particle-fluid density ratios.Comment: Animation available at http://www.pks.mpg.de/~rdvilela/leapfrogging.htm

Preliminary verification results of the DWD limited area model LME and evaluation of its storm forecasting skill over the area of Cyprus

A preliminary verification and evaluation is made of the forecast fields of the non-hydrostatic limited area model LME of the German Weather Service (DWD), for a recent three month period. For this purpose, observations from two synoptic stations in Cyprus are utilized. In addition, days with depressions over the area were selected in order to evaluate the model's forecast skill in storm forecasting

Active sites in heterogeneous ice nucleation-the example of K-rich feldspars

Ice formation on aerosol particles is a process of crucial importance to Earth's climate and the environmental sciences but it is not understood at the molecular level. This is so partly because the nature of active sites, local surface features where ice growth commences, is still unclear. Here we report direct electron-microscopic observations of deposition growth of aligned ice crystals on feldspar, an atmospherically important component of mineral dust. Our molecular-scale computer simulations indicate that this alignment arises from the preferential nucleation of prismatic crystal planes of ice on high-energy (100) surface planes of feldspar. The microscopic patches of (100) surface, exposed at surface defects such as steps, cracks, and cavities, are thought to be responsible for the high ice nucleation efficacy of K-feldspar particles

Machine learning potentials for complex aqueous made

Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid–liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems