Examinando por Autor "Peralta, J."

Mostrando 1 - 4 de 4
  • Miniatura
    Ítem
    A portable and flexible implementation of the Wang–Landau algorithm in order to determine the density of states
    (Elsevier B.V., 2022-01) Moreno, F.; Davis, S.; Peralta, J.
    In this work we develop an implementation of the Wang–Landau algorithm Wang and Landau (2001) [4]arXiv e-prints. This algorithm allows us to find the density of states (DOS), a function that, for a given system, describes the proportion of states that have a certain energy. The implementation uses the Python and C++ languages for the algorithm itself, and it can take advantage of any library, such as the powerful LAMMPS library, for the computation of energy. Therefore, the resulting implementation is simple and flexible without sacrificing efficiency. This implementation also considers recent developments in the parallelization of the code for faster computation. We establish the soundness and effectiveness of our implementation by studying well-known systems such as the Ising model, the Lennard–Jones and EAM solids. We have found that our implementation can find the DOS with very good precision in a reasonable amount of time. Therefore, we are equipped with a very powerful and flexible implementation that can be easily used in order to study more realistic models of matter. Program summary: Program Title: Republica Wang–Landau (RWL) CPC Library link to program files: https://doi.org/10.17632/gcx9k8wnhh.1 Licensing provisions: GPLv3 Programming language: Python, C++ Nature of problem: An implementation of the WL algorithm that is flexible enough to be used for a large variety of systems. Solution method: This implementation separates the actual Wang–Landau code of the abstract implementation of the system. Therefore, any system can be attached as a walker—a Python class that represents the system.
  • Miniatura
    Ítem
    Apoplastic class III peroxidases PRX62 and PRX69 promote Arabidopsis root hair growth at low temperature
    (Nature Research, 2022-03) Pacheco, J.; Ranocha, P.; Kasulin, L.; Fusari, C.; Servi, L.; Aptekmann, A.; Gabarain, V.; Peralta, J.; Borassi, C.; Marzol, E.; Rodríguez-Garcia, D.; del Carmen Rondón Guerrero, Y.; Sardoy, M.; Ferrero, L.; Botto, J.; Meneses, C.; Ariel, F.; Nadra, A.; Petrillo, E.; Dunand, C.; Estevez, J.
    Root Hairs (RHs) growth is influenced by endogenous and by external environmental signals that coordinately regulate its final cell size. We have recently determined that RH growth was unexpectedly boosted when Arabidopsis thaliana seedlings are cultivated at low temperatures. It was proposed that RH growth plasticity in response to low temperature was linked to a reduced nutrient availability in the media. Here, we explore the molecular basis of this RH growth response by using a Genome Wide Association Study (GWAS) approach using Arabidopsis thaliana natural accessions. We identify the poorly characterized PEROXIDASE 62 (PRX62) and a related protein PRX69 as key proteins under moderate low temperature stress. Strikingly, a cell wall protein extensin (EXT) reporter reveals the effect of peroxidase activity on EXT cell wall association at 10 °C in the RH apical zone. Collectively, our results indicate that PRX62, and to a lesser extent PRX69, are key apoplastic PRXs that modulate ROS-homeostasis and cell wall EXT-insolubilization linked to RH elongation at low temperature.
  • Miniatura
    Ítem
    Deterministic physical systems under uncertain initial conditions: the case of maximum entropy applied to projectile motion
    (Institute of Physics Publishing, 2018-05) Montecinos, A.; Davis, S.; Peralta, J.
    The kinematics and dynamics of deterministic physical systems have been a foundation of our understanding of the world since Galileo and Newton. For real systems, however, uncertainty is largely present via external forces such as friction or lack of precise knowledge about the initial conditions of the system. In this work we focus on the latter case and describe the use of inference methodologies in solving the statistical properties of classical systems subject to uncertain initial conditions. In particular we describe the application of the formalism of maximum entropy (MaxEnt) inference to the problem of projectile motion, given information about the average horizontal range over many realizations. By using MaxEnt we can invert the problem and use the provided information on the average range to reduce the original uncertainty in the initial conditions. Also, additional insight into the initial condition's probabilities, and the projectile path distribution itself, can be achieved based on the value of the average horizontal range. The wide applicability of this procedure, as well as its ease of use, reveals a useful tool with which to revisit a large number of physics problems, from classrooms to frontier research. © 2018 European Physical Society.
  • Miniatura
    Ítem
    Modeling field evaporation degradation of metallic surfaces by first principles calculations: A case study for Al, Au, Ag, and Pd
    (Institute of Physics Publishing, 2018-06) Carrasco, T.; Peralta, J.; Loyola, C.; Broderick, S.R.
    Under the effects of an extreme electric field, the atoms on a metallic surface evaporate by breaking their bonds with the surface. In this work, we present the effects of a high electric field, by the use of computational simulations, for different metallic surface chemistries: Al, Au, Ag, and Pd. To model this bond breaking procedrure (i.e. field evaporation), we use density functional theory through the Quantum-Espresso (QE) simulation package, which incorporates the electric fields by adding a saw-like funcion into the Hamiltonian. This approach, known as dipole correction, was applied to all simulations as is implemented in the QE package. In this work, we calculate the evaporation field (Fe ) for all metallic species, which corresponds to the mean field at which atoms can break their bonds from the surface and evaporate. This result is compared with experimantal data from Atom Probe Tomography (APT) and computational data from prior simulations. © Published under licence by IOP Publishing Ltd.