Proof-of-concept engineering workflow demonstrator

When Microsoft needed a proof-of-concept implementation of bespoke engineering workflow software for their customer, BAE Systems, it called on the software engineering skills and experience of the Microsoft Institute for High Performance Computing. BAE Systems was looking into converting their in-house SOLAR software suite to run on the MS Compute Cluster Server product with 64-bit MPI support in conjunction with an extended Windows Workflow environment for use by their engineer

Microsoft institute for high performance computing

An overview of the Microsoft Institute for High Performance Computing at the University of Southampto

Finite field formalism for bulk electrolyte solutions

The manner in which electrolyte solutions respond to electric fields is crucial to understanding the behavior of these systems both at, and away from, equilibrium. The present formulation of linear response theory for such systems is inconsistent with common molecular dynamics (MD) implementations. Using the finite field formalism, suitably adapted for finite temperature MD, we investigate the response of bulk aqueous NaCl solutions to both finite Maxwell ($\mathbf{E}$) and electric displacement ($\mathbf{D}$) fields. The constant $\mathbf{E}$ Hamiltonian allows us to derive the linear response relation for the ionic conductivity in a simple manner that is consistent with the forces used in conventional MD simulations. Simulations of a simple point charge model of an electrolyte solution at constant $\mathbf{E}$ yield conductivities at infinite dilution within 15% of experimental values. The finite field approach also allows us to measure the solvent's dielectric constant from its polarization response, which is seen to decrease with increasing ionic strength. Comparison of the dielectric constant measured from polarization response versus polarization fluctuations enables direct evaluation of the dynamic contribution to this dielectric decrement, which we find to be small but not insignificant. Using the constant $\mathbf{D}$ formulation, we also rederive the Stillinger-Lovett conditions, which place strict constraints on the coupling between solvent and ionic polarization fluctuations.We are grateful for computational support from the UK Materials and Molecular Modelling Hub, which is partially funded by EPSRC (Grant No. EP/P020194), for which access was obtained via the UKCP consortium and funded by EPSRC Grant Ref. No. EP/P022561/1. S.J.C. is supported by a Royal Commission for the Exhibition of 1851 Research Fellowship

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

Molecular simulations of heterogeneous ice nucleation. I. Controlling ice nucleation through surface hydrophilicity

Ice formation is one of the most common and important processes on earth and almost always occurs at the surface of a material. A basic understanding of how the physicochemical properties of a material’s surface affect its ability to form ice has remained elusive. Here, we use molecular dynamics simulations to directly probe heterogeneous ice nucleation at a hexagonal surface of a nanoparticle of varying hydrophilicity. Surprisingly, we find that structurally identical surfaces can both inhibit and promote ice formation and analogous to a chemical catalyst, it is found that an optimal interaction between the surface and the water exists for promoting ice nucleation. We use our microscopic understanding of the mechanism to design a modified surface in silico with enhanced ice nucleating ability

Molecular simulations of heterogeneous ice nucleation. II. Peeling back the layers

Coarse grained molecular dynamics simulations are presented in which the sensitivity of the ice nucleation rate to the hydrophilicity of a graphene nanoflake is investigated. We find that an optimal interaction strength for promoting ice nucleation exists, which coincides with that found previously for a face centered cubic (111) surface. We further investigate the role that the layering of interfacial water plays in heterogeneous ice nucleation and demonstrate that the extent of layering is not a good indicator of ice nucleating ability for all surfaces. Our results suggest that to be an efficient ice nucleating agent, a surface should not bind water too strongly if it is able to accommodate high coverages of water

Furthering our understanding of heterogeneous ice nucleation with molecular simulation

Ice formation is arguably the most common phase transition on the planet and almost always occurs heterogeneously. Despite the importance of ice formation to the climate, medical and geological sciences, as well as the food and transport industries, a clear understanding of how the properties of a material affect its ability to nucleate ice has remained elusive. This has prevented the rational design of new materials to either inhibit or promote ice nucleation. In this thesis, a wide variety of computational techniques are used to try and further our understanding of heterogeneous ice nucleation. This includes: testing long established theories; investigating ice formation in the presence of a known ice nucleating agent; using simplified model surfaces to elucidate the underlying molecular mechanisms of heterogeneous ice nucleation (and design new ice nucleating agents in silico); and developing transition path sampling techniques to look at some of the fundamental aspects of homogeneous nucleation. The accuracy of commonly used approximations to define the potential energy surface of a closely related system, methane hydrate, is also investigated

Nonequivalence of updating rules in evolutionary games under high mutation rates.

Moran processes are often used to model selection in evolutionary simulations. The updating rule in Moran processes is a birth-death process, i. e., selection according to fitness of an individual to give birth, followed by the death of a random individual. For well-mixed populations with only two strategies this updating rule is known to be equivalent to selecting unfit individuals for death and then selecting randomly for procreation (biased death-birth process). It is, however, known that this equivalence does not hold when considering structured populations. Here we study whether changing the updating rule can also have an effect in well-mixed populations in the presence of more than two strategies and high mutation rates. We find, using three models from different areas of evolutionary simulation, that the choice of updating rule can change model results. We show, e. g., that going from the birth-death process to the death-birth process can change a public goods game with punishment from containing mostly defectors to having a majority of cooperative strategies. From the examples given we derive guidelines indicating when the choice of the updating rule can be expected to have an impact on the results of the model