21 results found
Duran-Olivencia M, Kalliadasis S, 2021, Understanding soaring corona virus cases and the effect of contagion policies in the UK, Vaccines, Vol: 9, Pages: 1-7, ISSN: 2076-393X
The number of new daily SARS-CoV-2 infections is frantically risingin almost every country of the EU. The phenomenological explanationoffered is a new mutation of the virus, first identified in the UK.We use publicly available data in combination with a controlled SIRmodel, which captures the effects of preventive measures on the activecases, to show that the current wave of infections is consistentwith a single transmission rate. This suggests that the new SARSCoV-2 variant is as transmissible as previous strains. Our findingsindicate that the relaxation of preventive measures is closely relatedwith the ongoing surge in cases. We simulate the effects of newrestrictions and vaccination campaigns in 2021, demonstrating thatlockdown policies are not fully effective to flatten the curve. For effectivemitigation, it is critical that the public keeps on high alert untilvaccination reaches a critical threshold.
Duran-Olivencia M, Kalliadasis S, 2021, More than a year after the onset of the CoVid-19 pandemic in the UK: lessons learned from a minimalistic model capturing essential features including social awareness and policy making, Publisher: MedRxiv
The number of new daily SARS-CoV-2 infections experienced an abrupt increase during the last quarter of 2020 in almost every European country. The phenomenological explanation offered was a new mutation of the virus, first identified in the UK. We use publicly available data in combination with a time-delayed controlled SIR model, which captures the effects of preventive measures and concomitant social response on the spreading of the virus. The model, which has a unique transmission rate, enables us to reproduce the waves of infection occurred in the UK. This suggests that the new SARS-CoV-2 UK variant is as transmissible as previous strains. Our findings reveal that the sudden surge in cases was in fact related to the relaxation of preventive measures and social awareness. We also simulate the combined effects of restrictions and vaccination campaigns in 2021, demonstrating that lockdown policies are not fully effective to flatten the curve; fully effective mitigation can only be achieved via a vigorous vaccination campaign. As a matter of fact, incorporating recent data about vaccine efficacy, our simulations advocate that the UK might have overcome the worse of the CoVid-19 pandemic, provided that the vaccination campaign maintains a rate of approximately 140k jabs per day.
Russo A, Duran-Olivencia MA, Yatsyshin P, et al., 2020, Memory effects in fluctuating dynamic density-functional theory: theory and simulations, Journal of Physics A: Mathematical and Theoretical, Vol: 53, ISSN: 1751-8113
This work introduces a theoretical framework to describe the dynamics of reacting multi-species fluid systems in-and-out of equilibrium. Our starting point is the system of generalised Langevin equations which describes the evolution of the positions and momenta of the constituent particles. One particular difficulty that this system of generalised Langevin equations exhibits is the presence of a history-dependent (i.e. non-Markovian) term, which in turn makes the system's dynamics dependent on its own past history. With the appropriate definitions of the local number density and momentum fields, we are able to derive a non-Markovian Navier–Stokes-like system of equations constituting a generalisation of the Dean–Kawasaki model. These equations, however, still depend on the full set of particles phase-space coordinates. To remove this dependence on the microscopic level without washing out the fluctuation effects characteristic of a mesoscopic description, we need to carefully ensemble-average our generalised Dean–Kawasaki equations. The outcome of such a treatment is a set of non-Markovian fluctuating hydrodynamic equations governing the time evolution of the mesoscopic density and momentum fields. Moreover, with the introduction of an energy functional which recovers the one used in classical density-functional theory and its dynamic extension (DDFT) under the local-equilibrium approximation, we derive a novel non-Markovian fluctuating DDFT (FDDFT) for reacting multi-species fluid systems. With the aim of reducing the fluctuating dynamics to a single equation for the density field, in the spirit of classical DDFT, we make use of a deconvolution operator which makes it possible to obtain the overdamped version of the non-Markovian FDDFT. A finite-volume discretization of the derived non-Markovian FDDFT is then proposed. With this, we validate our theoretical framework in-and-out-of-equilibrium by comparing results against atomistic simulations. Fi
Russo A, Durán-Olivencia MA, Kalliadasis S, et al., 2019, Macroscopic relations for microscopic properties at the interface between solid substrates and dense fluids, Journal of Chemical Physics, Vol: 150, ISSN: 0021-9606
Strongly confined fluids exhibit inhomogeneous properties due to atomistic structuring in close proximity to a solid surface. State variables and transport coefficients at a solid-fluid interface vary locally and become dependent on the properties of the confining walls. However, the precise mechanisms for these effects are not known as of yet. Here, we make use of nonequilibrium molecular dynamics simulations to scrutinize the local fluid properties at the solid-fluid interface for a range of surface conditions and temperatures. We also derive microscopic relations connecting fluid viscosity and density profiles for dense fluids. Moreover, we propose empirical ready-to-use relations to express the average density and viscosity in the channel as a function of temperature, wall interaction strength, and bulk density or viscosity. Such relations are key to technological applications such as micro-/nanofluidics and tribology but also natural phenomena.
Durán-Olivencia MA, Gvalani RS, Kalliadasis S, et al., 2019, Instability, rupture and fluctuations in thin liquid films: Theory and computations, Journal of Statistical Physics, Vol: 174, Pages: 579-604, ISSN: 0022-4715
Thin liquid films are ubiquitous in natural phenomena and technological applications. They have been extensively studied via deterministic hydrodynamic equations, but thermal fluctuations often play a crucial role that needs to be understood. An example of this is dewetting, which involves the rupture of a thin liquid film and the formation of droplets. Such a process is thermally activated and requires fluctuations to be taken into account self-consistently. In this work we present an analytical and numerical study of a stochastic thin-film equation derived from first principles. Following a brief review of the derivation, we scrutinise the behaviour of the equation in the limit of perfectly correlated noise along the wall-normal direction, as opposed to the perfectly uncorrelated limit studied by Grün et al. (J Stat Phys 122(6):1261–1291, 2006). We also present a numerical scheme based on a spectral collocation method, which is then utilised to simulate the stochastic thin-film equation. This scheme seems to be very convenient for numerical studies of the stochastic thin-film equation, since it makes it easier to select the frequency modes of the noise (following the spirit of the long-wave approximation). With our numerical scheme we explore the fluctuating dynamics of the thin film and the behaviour of its free energy in the vicinity of rupture. Finally, we study the effect of the noise intensity on the rupture time, using a large number of sample paths as compared to previous studies.
A great deal of experimental evidence suggests that a wide spectrum of phase transitions occur in a multistage manner via the appearance and subsequent transformation of intermediate metastable states. Such multistage mechanisms cannot be explained within the realm of the classical nucleation framework. Hence, there is a strong need to develop new theoretical tools to explain the occurrence and nature of these ubiquitous intermediate phases. Here we outline a unified and self-consistent theoretical framework to describe both classical and nonclassical nucleation. Our framework provides a detailed explanation of the whole multistage nucleation pathway showing in particular that the pathway involves a single energy barrier and it passes through a dense phase, starting from a low-density initial phase, before reaching the final stable state. Moreover, we demonstrate that the kinetics of matter inside subcritical clusters favors the formation of nucleation clusters with an intermediate density, i.e. nucleation precursors. Remarkably, these nucleation precursors are not associated with a local minimum of the thermodynamic potential, as commonly assumed in previous phenomenological approaches. On the contrary, we find that they emerge due to the competition between thermodynamics and kinetics of cluster formation. Thus, the mechanism uncovered for the formation of intermediate phases can be used to explain recently reported experimental findings in crystallization: up to now such phases were assumed a consequence of some complex energy landscape with multiple energy minima. Using fundamental concepts from kinetics and thermodynamics, we provide a satisfactory explanation for the so-called nonclassical nucleation pathways observed in experiments.
Yatsyshin P, Duran-Olivencia MA, Kalliadasis S, 2018, Microscopic aspects of wetting using classical density-functional theory., Journal of Physics: Condensed Matter, Vol: 30, ISSN: 0953-8984
Wetting is a rather efficient mechanism for nucleation of a phase (typically liquid) on the interface between two other phases (typically solid and gas). In many experimentally accessible cases of wetting, the interplay between the substrate structure, and the fluid-fluid and fluid-substrate intermolecular interactions brings about an entire ``zoo" of possible fluid configurations, such as liquid films with a thickness of a few nanometers, liquid nanodrops and liquid bridges. These fluid configurations are often associated with phase transitions occurring at the solid-gas interface and at lengths of just several molecular diameters away from the substrate. In this special issue article, we demonstrate how a fully microscopic classical density-functional framework can be applied to the efficient, rational and systematic exploration of the rich phase space of wetting phenomena. We consider a number of model prototype systems such as wetting on a planar wall, a chemically patterned wall and a wedge. Through density-functional computations we demonstrate that for these simply structured substrates the behaviour of the solid-gas interface is already highly complex and non-trivial.
Duran Olivencia MA, Yatsyshin P, Goddard B, et al., 2017, General framework for fluctuating dynamic density functional theory, New Journal of Physics, Vol: 19, ISSN: 1367-2630
We introduce a versatile bottom-up derivation of a formal theoretical framework to describe (passive) soft-matter systems out of equilibrium subject to fluctuations. We provide a unique connection between the constituent-particle dynamics of real systems and the time evolution equation of their measurable (coarse-grained) quantities, such as local density and velocity. The starting point is the full Hamiltonian description of a system of colloidal particles immersed in a fluid of identical bath particles. Then, we average out the bath via Zwanzig's projection-operator techniques and obtain the stochastic Langevin equations governing the colloidal-particle dynamics. Introducing the appropriate definition of the local number and momentum density fields yields a generalisation of the Dean-Kawasaki (DK) model, which resembles the stochastic Navier-Stokes (NS) description of a fluid. Nevertheless, the DK equation still contains all the microscopic information and, for that reason, does not represent the dynamical law of observable quantities. We address this controversial feature of the DK description by carrying out a nonequilibrium ensemble average. Adopting a natural decomposition into local-equilibrium and nonequilibrium contribution, where the former is related to a generalised version of the canonical distribution, we finally obtain the fluctuating-hydrodynamic equation governing the time-evolution of the mesoscopic density and momentum fields. Along the way, we outline the connection between the ad-hoc energy functional introduced in previous DK derivations and the free-energy functional from classical density-functional theory (DFT). The resultant equation has the structure of a dynamical DFT (DDFT) with an additional fluctuating force coming from the random interactions with the bath. We show that our fluctuating DDFT formalism corresponds to a particular version of the fluctuating NS equations, originally derived by Landau and Lifshitz. Our framework thus provi
Martinez-Casado FJ, Ramos-Riesco M, Rodriguez-Cheda JA, et al., 2017, Lead(II) soaps: crystal structures, polymorphism, and solid and liquid mesophases, PHYSICAL CHEMISTRY CHEMICAL PHYSICS, Vol: 19, Pages: 17009-17018, ISSN: 1463-9076
Durán-Olivencia MA, Goddard BD, Kalliadasis S, 2016, Dynamical Density Functional Theory for Orientable Colloids Including Inertia and Hydrodynamic Interactions, Journal of Statistical Physics, Vol: 164, Pages: 785-809, ISSN: 1572-9613
Over the last few decades, classical density-functional theory (DFT) and its dynamic extensions (DDFTs) have become powerful tools in the study of colloidal fluids. Recently, previous DDFTs for spherically-symmetric particles have been generalised to take into account both inertia and hydrodynamic interactions, two effects which strongly influence non-equilibrium properties. The present work further generalises this framework to systems of anisotropic particles. Starting from the Liouville equation and utilising Zwanzig’s projection-operator techniques, we derive the kinetic equation for the Brownian particle distribution function, and by averaging over all but one particle, a DDFT equation is obtained. Whilst this equation has some similarities with DDFTs for spherically-symmetric colloids, it involves a translational-rotational coupling which affects the diffusivity of the (asymmetric) particles. We further show that, in the overdamped (high friction) limit, the DDFT is considerably simplified and is in agreement with a previous DDFT for colloids with arbitrary-shape particles.
Lutsko JF, Van Driessche AES, Duran-Olivencia MA, et al., 2016, Step Crowding Effects Dampen the Stochasticity of Crystal Growth Kinetics, PHYSICAL REVIEW LETTERS, Vol: 116, ISSN: 0031-9007
Duran-Olivencia MA, Lutsko JF, 2015, Unification of classical nucleation theories via a unified It(o)over-cap-Stratonovich stochastic equation, PHYSICAL REVIEW E, Vol: 92, ISSN: 1539-3755
Lutsko JF, Durán-Olivencia MA, 2015, A two-parameter extension of classical nucleation theory., Journal of Physics: Condensed Matter, Vol: 27, ISSN: 0953-8984
A two-variable stochastic model for diffusion-limited nucleation is developed using a formalism derived from fluctuating hydrodynamics. The model is a direct generalization of the standard classical nucleation theory (CNT). The nucleation rate and pathway are calculated in the weak-noise approximation and are shown to be in good agreement with direct numerical simulations for the weak-solution/strong-solution transition in globular proteins. We find that CNT underestimates the time needed for the formation of a critical cluster by two orders of magnitude and that this discrepancy is due to the more complex dynamics of the two variable model and not, as often is assumed, a result of errors in the estimation of the free energy barrier.
Duran Olivencia MA, Lutsko JF, 2015, Mesoscopic nucleation theory for confined systems: a one-parameter model., Physical Review E - Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, Vol: 91, ISSN: 1063-651X
Classical nucleation theory has been recently reformulated based on fluctuating hydrodynamics [J. F. Lutsko and M. A. Durán-Olivencia, Classical nucleation theory from a dynamical approach to nucleation, J. Chem. Phys. 138, 244908 (2013). The present work extends this effort to the case of nucleation in confined systems such as small pores and vesicles. The finite available mass imposes a maximal supercritical cluster size and prohibits nucleation altogether if the system is too small. We quantity the effect of system size on the nucleation rate. We also discuss the effect of relaxing the capillary-model assumption of zero interfacial width resulting in significant changes in the nucleation barrier and nucleation rate.
Sleutel M, Lutsko J, Van Driessche AES, et al., 2014, Observing classical nucleation theory at work by monitoring phase transitions with molecular precision, NATURE COMMUNICATIONS, Vol: 5, ISSN: 2041-1723
Lutsko JF, Gonzalez-Segredo N, Duran-Olivencia MA, et al., 2014, Crystal Growth Cessation Revisited: The Physical Basis of Step Pinning, CRYSTAL GROWTH & DESIGN, Vol: 14, Pages: 6129-6134, ISSN: 1528-7483
Duran-Olivencia MA, Otalora F, 2013, A Brownian model for crystal nucleation, JOURNAL OF CRYSTAL GROWTH, Vol: 380, Pages: 247-255, ISSN: 0022-0248
Sancho-Tomas M, Fermani S, Duran-Olivencia MA, et al., 2013, Influence of Charged Polypeptides on Nucleation and Growth of CaCO3 Evaluated by Counterdiffusion Experiments, CRYSTAL GROWTH & DESIGN, Vol: 13, Pages: 3884-3891, ISSN: 1528-7483
Rodriguez-Ruiz I, Manuel Delgado-Lopez J, Duran-Olivencia MA, et al., 2013, pH-Responsive Delivery of Doxorubicin from Citrate-Apatite Nanocrystals with Tailored Carbonate Content, LANGMUIR, Vol: 29, Pages: 8213-8221, ISSN: 0743-7463
Lutsko JF, Duran-Olivencia MA, 2013, Classical nucleation theory from a dynamical approach to nucleation, JOURNAL OF CHEMICAL PHYSICS, Vol: 138, ISSN: 0021-9606
Russo A, Durán-Olivencia MA, Kevrekidis IG, et al., Deep learning as closure for irreversible processes: A data-driven generalized Langevin equation
The ultimate goal of physics is finding a unique equation capable ofdescribing the evolution of any observable quantity in a self-consistent way.Within the field of statistical physics, such an equation is known as thegeneralized Langevin equation (GLE). Nevertheless, the formal and exact GLE isnot particularly useful, since it depends on the complete history of theobservable at hand, and on hidden degrees of freedom typically inaccessiblefrom a theoretical point of view. In this work, we propose the use of deepneural networks as a new avenue for learning the intricacies of the unknownsmentioned above. By using machine learning to eliminate the unknowns from GLEs,our methodology outperforms previous approaches (in terms of efficiency androbustness) where general fitting functions were postulated. Finally, our workis tested against several prototypical examples, from a colloidal systems andparticle chains immersed in a thermal bath, to climatology and financialmodels. In all cases, our methodology exhibits an excellent agreement with theactual dynamics of the observables under consideration.
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