Superfluidity and Quantum Melting of para-Hydrogen clusters

Structural and superfluid properties of para-Hydrogen clusters of size up to N=40 molecules, are studied at low temperature (0.5 K < T < 4 K) by Path Integral Monte Carlo simulations. The superfluid fraction displays an interesting, non-monotonic behavior for 22 < N < 30. We interpret this dependence in terms of variations with N of the cluster structure. Superfluidity is observed at low T in clusters of as many as 27 molecules; in the temperature range considered here, quantum melting is observed in some clusters, which freeze at high temperature

Reconstruction of thermally-symmetrized quantum autocorrelation functions from imaginary-time data

In this paper, I propose a technique for recovering quantum dynamical information from imaginary-time data via the resolution of a one-dimensional Hamburger moment problem. It is shown that the quantum autocorrelation functions are uniquely determined by and can be reconstructed from their sequence of derivatives at origin. A general class of reconstruction algorithms is then identified, according to Theorem 3. The technique is advocated as especially effective for a certain class of quantum problems in continuum space, for which only a few moments are necessary. For such problems, it is argued that the derivatives at origin can be evaluated by Monte Carlo simulations via estimators of finite variances in the limit of an infinite number of path variables. Finally, a maximum entropy inversion algorithm for the Hamburger moment problem is utilized to compute the quantum rate of reaction for a one-dimensional symmetric Eckart barrier.Comment: 15 pages, no figures, to appear in Phys. Rev.

Extrapolated High-Order Propagators for Path Integral Monte Carlo Simulations

We present a new class of high-order imaginary time propagators for path-integral Monte Carlo simulations by subtracting lower order propagators. By requiring all terms of the extrapolated propagator be sampled uniformly, the subtraction only affects the potential part of the path integral. The negligible violation of positivity of the resulting path integral at small time steps has no discernable affect on the accuracy of our method. Thus in principle arbitrarily high order algorithms can be devised for path-integral Monte Carlo simulations. We verify this claim is by showing that fourth, sixth, and eighth order convergence can indeed be achieved in solving for the ground state of strongly interacting quantum many-body systems such as bulk liquid $^4$He.Comment: 9 pages and 3 figures. Submitted to J. Chem. Phy

Entropic effects in large-scale Monte Carlo simulations

The efficiency of Monte Carlo samplers is dictated not only by energetic effects, such as large barriers, but also by entropic effects that are due to the sheer volume that is sampled. The latter effects appear in the form of an entropic mismatch or divergence between the direct and reverse trial moves. We provide lower and upper bounds for the average acceptance probability in terms of the Renyi divergence of order 1/2. We show that the asymptotic finitude of the entropic divergence is the necessary and sufficient condition for non-vanishing acceptance probabilities in the limit of large dimensions. Furthermore, we demonstrate that the upper bound is reasonably tight by showing that the exponent is asymptotically exact for systems made up of a large number of independent and identically distributed subsystems. For the last statement, we provide an alternative proof that relies on the reformulation of the acceptance probability as a large deviation problem. The reformulation also leads to a class of low-variance estimators for strongly asymmetric distributions. We show that the entropy divergence causes a decay in the average displacements with the number of dimensions n that are simultaneously updated. For systems that have a well-defined thermodynamic limit, the decay is demonstrated to be n^{-1/2} for random-walk Monte Carlo and n^{-1/6} for Smart Monte Carlo (SMC). Numerical simulations of the LJ_38 cluster show that SMC is virtually as efficient as the Markov chain implementation of the Gibbs sampler, which is normally utilized for Lennard-Jones clusters. An application of the entropic inequalities to the parallel tempering method demonstrates that the number of replicas increases as the square root of the heat capacity of the system.Comment: minor corrections; the best compromise for the value of the epsilon parameter in Eq. A9 is now shown to be log(2); 13 pages, 4 figures, to appear in PR

Structure of Si(114) determined by global optimization methods

In this article we report the results of global structural optimization of the Si(114) surface, which is a stable high-index orientation of silicon. We use two independent procedures recently developed for the determination of surface reconstructions, the parallel-tempering Monte Carlo method and the genetic algorithm. These procedures, coupled with the use of a highly-optimized interatomic potential for silicon, lead to finding a set of possible models for Si(114), whose energies are recalculated with ab-initio density functional methods. The most stable structure obtained here without experimental input coincides with the structure determined from scanning tunneling microscopy experiments and density functional calculations by Erwin, Baski and Whitman [Phys. Rev. Lett. 77, 687 (1996)].Comment: 19 pages, 5 figure

On the efficient Monte Carlo implementation of path integrals

We demonstrate that the Levy-Ciesielski implementation of Lie-Trotter products enjoys several properties that make it extremely suitable for path-integral Monte Carlo simulations: fast computation of paths, fast Monte Carlo sampling, and the ability to use different numbers of time slices for the different degrees of freedom, commensurate with the quantum effects. It is demonstrated that a Monte Carlo simulation for which particles or small groups of variables are updated in a sequential fashion has a statistical efficiency that is always comparable to or better than that of an all-particle or all-variable update sampler. The sequential sampler results in significant computational savings if updating a variable costs only a fraction of the cost for updating all variables simultaneously or if the variables are independent. In the Levy-Ciesielski representation, the path variables are grouped in a small number of layers, with the variables from the same layer being statistically independent. The superior performance of the fast sampling algorithm is shown to be a consequence of these observations. Both mathematical arguments and numerical simulations are employed in order to quantify the computational advantages of the sequential sampler, the Levy-Ciesielski implementation of path integrals, and the fast sampling algorithm.Comment: 14 pages, 3 figures; submitted to Phys. Rev.

Pro-inflammatory endothelial cell dysfunction is associated with intersectin-1s down-regulation

<p>Abstract</p> <p>Background</p> <p>The response of lung microvascular endothelial cells (ECs) to lipopolysaccharide (LPS) is central to the pathogenesis of lung injury. It is dual in nature, with one facet that is pro-inflammatory and another that is cyto-protective. In previous work, overexpression of the anti-apoptotic Bcl-X_L rescued ECs from apoptosis triggered by siRNA knockdown of intersectin-1s (ITSN-1s), a pro-survival protein crucial for ECs function. Here we further characterized the cyto-protective EC response to LPS and pro-inflammatory dysfunction.</p> <p>Methods and Results</p> <p>Electron microscopy (EM) analyses of LPS-exposed ECs revealed an activated/dysfunctional phenotype, while a biotin assay for caveolae internalization followed by biochemical quantification indicated that LPS causes a 40% inhibition in biotin uptake compared to controls. Quantitative PCR and Western blotting were used to evaluate the mRNA and protein expression, respectively, for several regulatory proteins of intrinsic apoptosis, including ITSN-1s. The decrease in ITSN-1s mRNA and protein expression were countered by Bcl-X_L and survivin upregulation, as well as Bim downregulation, events thought to protect ECs from impending apoptosis. Absence of apoptosis was confirmed by TUNEL and lack of cytochrome c (cyt c) efflux from mitochondria. Moreover, LPS exposure caused induction and activation of inducible nitric oxide synthase (iNOS) and a mitochondrial variant (mtNOS), as well as augmented mitochondrial NO production as measured by an oxidation oxyhemoglobin (oxyHb) assay applied on mitochondrial-enriched fractions prepared from LPS-exposed ECs. Interestingly, expression of myc-ITSN-1s rescued caveolae endocytosis and reversed induction of iNOS expression.</p> <p>Conclusion</p> <p>Our results suggest that ITSN-1s deficiency is relevant for the pro-inflammatory ECs dysfunction induced by LPS.</p

Feedback-optimized parallel tempering Monte Carlo

We introduce an algorithm to systematically improve the efficiency of parallel tempering Monte Carlo simulations by optimizing the simulated temperature set. Our approach is closely related to a recently introduced adaptive algorithm that optimizes the simulated statistical ensemble in generalized broad-histogram Monte Carlo simulations. Conventionally, a temperature set is chosen in such a way that the acceptance rates for replica swaps between adjacent temperatures are independent of the temperature and large enough to ensure frequent swaps. In this paper, we show that by choosing the temperatures with a modified version of the optimized ensemble feedback method we can minimize the round-trip times between the lowest and highest temperatures which effectively increases the efficiency of the parallel tempering algorithm. In particular, the density of temperatures in the optimized temperature set increases at the "bottlenecks'' of the simulation, such as phase transitions. In turn, the acceptance rates are now temperature dependent in the optimized temperature ensemble. We illustrate the feedback-optimized parallel tempering algorithm by studying the two-dimensional Ising ferromagnet and the two-dimensional fully-frustrated Ising model, and briefly discuss possible feedback schemes for systems that require configurational averages, such as spin glasses.Comment: 12 pages, 14 figure

Electrospun nanosystems based on PHBV and ZnO for ecological food packaging

The electrospun nanosystems containing poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) and 1 wt% Fe doped ZnO nanoparticles (NPs) (with the content of dopant in the range of 0–1 wt% Fe) deposited onto polylactic acid (PLA) film were prepared for food packaging application. They were investigated by scanning electron microscopy (SEM), energy dispersive X-ray (EDX), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), antimicrobial analysis, and X-ray photoelectron spectrometry (XPS) techniques. Migration studies conducted in acetic acid 3% (wt/wt) and ethanol 10% (v/v) food simulants as well as by the use of treated ashes with 3% HNO3 solution reveal that the migration of Zn and Fe falls into the specific limits imposed by the legislation in force. Results indicated that the PLA/PHBV/ZnO:Fex electrospun nanosystems exhibit excellent antibacterial activity against the Pseudomonas aeruginosa (ATCC-27853) due to the generation of a larger amount of perhydroxyl (¿OOH) radicals as assessed using electron para-magnetic resonance (EPR) spectroscopy coupled with a spin trapping method. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Random Series and Discrete Path Integral methods: The Levy-Ciesielski implementation

We perform a thorough analysis of the relationship between discrete and series representation path integral methods, which are the main numerical techniques used in connection with the Feynman-Kac formula. First, a new interpretation of the so-called standard discrete path integral methods is derived by direct discretization of the Feynman-Kac formula. Second, we consider a particular random series technique based upon the Levy-Ciesielski representation of the Brownian bridge and analyze its main implementations, namely the primitive, the partial averaging, and the reweighted versions. It is shown that the n=2^k-1 subsequence of each of these methods can also be interpreted as a discrete path integral method with appropriate short-time approximations. We therefore establish a direct connection between the discrete and the random series approaches. In the end, we give sharp estimates on the rates of convergence of the partial averaging and the reweighted Levy-Ciesielski random series approach for sufficiently smooth potentials. The asymptotic rates of convergence are found to be O(1/n^2), in agreement with the rates of convergence of the best standard discrete path integral techniques.Comment: 20 pages, 4 figures; the two equations before Eq. 14 are corrected; other typos are remove