97 research outputs found
Neural network based path collective variables for enhanced sampling of phase transformations
We propose a rigorous construction of a 1D path collective variable to sample
structural phase transformations in condensed matter. The path collective
variable is defined in a space spanned by global collective variables that
serve as classifiers derived from local structural units. A reliable
identification of local structural environments is achieved by employing a
neural network based classification. The 1D path collective variable is
subsequently used together with enhanced sampling techniques to explore the
complex migration of a phase boundary during a solid-solid phase transformation
in molybdenum
From Classical to Quantum and Back: Hamiltonian Adaptive Resolution Path Integral, Ring Polymer, and Centroid Molecular Dynamics
Path integral-based simulation methodologies play a crucial role for the
investigation of nuclear quantum effects by means of computer simulations.
However, these techniques are significantly more demanding than corresponding
classical simulations. To reduce this numerical effort, we recently proposed a
method, based on a rigorous Hamiltonian formulation, which restricts the
quantum modeling to a small but relevant spatial region within a larger
reservoir where particles are treated classically. In this work, we extend this
idea and show how it can be implemented along with state-of-the-art path
integral simulation techniques, such as ring polymer and centroid molecular
dynamics, which allow the approximate calculation of both quantum statistical
and quantum dynamical properties. To this end, we derive a new integration
algorithm which also makes use of multiple time-stepping. The scheme is
validated via adaptive classical--path-integral simulations of liquid water.
Potential applications of the proposed multiresolution method are diverse and
include efficient quantum simulations of interfaces as well as complex
biomolecular systems such as membranes and proteins
Path-integral molecular dynamics simulation of 3C-SiC
Molecular dynamics simulations of 3C-SiC have been performed as a function of
pressure and temperature. These simulations treat both electrons and atomic
nuclei by quantum mechanical methods. While the electronic structure of the
solid is described by an efficient tight-binding Hamiltonian, the nuclei
dynamics is treated by the path integral formulation of statistical mechanics.
To assess the relevance of nuclear quantum effects, the results of quantum
simulations are compared to others where either the Si nuclei, the C nuclei or
both atomic nuclei are treated as classical particles. We find that the
experimental thermal expansion of 3C-SiC is realistically reproduced by our
simulations. The calculated bulk modulus of 3C-SiC and its pressure derivative
at room temperature show also good agreement with the available experimental
data. The effect of the electron-phonon interaction on the direct electronic
gap of 3C-SiC has been calculated as a function of temperature and related to
results obtained for bulk diamond and Si. Comparison to available experimental
data shows satisfactory agreement, although we observe that the employed
tight-binding model tends to overestimate the magnitude of the electron-phonon
interaction. The effect of treating the atomic nuclei as classical particles on
the direct gap of 3C-SiC has been assessed. We find that non-linear quantum
effects related to the atomic masses are particularly relevant at temperatures
below 250 K.Comment: 14 pages, 15 figure
Connecting solvation shell structure to proton transport kinetics in hydrogen-bonded networks via population correlation functions
A theory based on population correlation functions is introduced for connecting solvation topologies and microscopic mechanisms to transport kinetics of charge defects in hydrogen-bonded networks. The theory is tested on the hydrated proton by extracting a comprehensive set of relaxation times, lifetimes, and rates from ab initio molecular dynamics simulations and comparing to recent femtosecond experiments. When applied to the controversial case of the hydrated hydroxide ion, the theory predicts that only one out of three proposed transport models is consistent with known experimental data
By-passing the Kohn-Sham equations with machine learning
Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of
density functional theory to solve electronic structure problems in a wide
variety of scientific fields, ranging from materials science to biochemistry to
astrophysics. Machine learning holds the promise of learning the kinetic energy
functional via examples, by-passing the need to solve the Kohn-Sham equations.
This should yield substantial savings in computer time, allowing either larger
systems or longer time-scales to be tackled, but attempts to machine-learn this
functional have been limited by the need to find its derivative. The present
work overcomes this difficulty by directly learning the density-potential and
energy-density maps for test systems and various molecules. Both improved
accuracy and lower computational cost with this method are demonstrated by
reproducing DFT energies for a range of molecular geometries generated during
molecular dynamics simulations. Moreover, the methodology could be applied
directly to quantum chemical calculations, allowing construction of density
functionals of quantum-chemical accuracy
Stochastic resonance-free multiple time-step algorithm for molecular dynamics with very large time steps
Molecular dynamics is one of the most commonly used approaches for studying
the dynamics and statistical distributions of many physical, chemical, and
biological systems using atomistic or coarse-grained models. It is often the
case, however, that the interparticle forces drive motion on many time scales,
and the efficiency of a calculation is limited by the choice of time step,
which must be sufficiently small that the fastest force components are
accurately integrated. Multiple time-stepping algorithms partially alleviate
this inefficiency by assigning to each time scale an appropriately chosen
step-size. However, such approaches are limited by resonance phenomena, wherein
motion on the fastest time scales limits the step sizes associated with slower
time scales. In atomistic models of biomolecular systems, for example,
resonances limit the largest time step to around 5-6 fs. In this paper, we
introduce a set of stochastic isokinetic equations of motion that are shown to
be rigorously ergodic and that can be integrated using a multiple time-stepping
algorithm that can be easily implemented in existing molecular dynamics codes.
The technique is applied to a simple, illustrative problem and then to a more
realistic system, namely, a flexible water model. Using this approach outer
time steps as large as 100 fs are shown to be possible
Complex c-di-GMP Signaling Networks Mediate Transition between Virulence Properties and Biofilm Formation in Salmonella enterica Serovar Typhimurium
Upon Salmonella enterica serovar Typhimurium infection of the gut, an early line of defense is the gastrointestinal epithelium which senses the pathogen and intrusion along the epithelial barrier is one of the first events towards disease. Recently, we showed that high intracellular amounts of the secondary messenger c-di-GMP in S. typhimurium inhibited invasion and abolished induction of a pro-inflammatory immune response in the colonic epithelial cell line HT-29 suggesting regulation of transition between biofilm formation and virulence by c-di-GMP in the intestine. Here we show that highly complex c-di-GMP signaling networks consisting of distinct groups of c-di-GMP synthesizing and degrading proteins modulate the virulence phenotypes invasion, IL-8 production and in vivo colonization in the streptomycin-treated mouse model implying a spatial and timely modulation of virulence properties in S. typhimurium by c-di-GMP signaling. Inhibition of the invasion and IL-8 induction phenotype by c-di-GMP (partially) requires the major biofilm activator CsgD and/or BcsA, the synthase for the extracellular matrix component cellulose. Inhibition of the invasion phenotype is associated with inhibition of secretion of the type three secretion system effector protein SipA, which requires c-di-GMP metabolizing proteins, but not their catalytic activity. Our findings show that c-di-GMP signaling is at least equally important in the regulation of Salmonella-host interaction as in the regulation of biofilm formation at ambient temperature
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