In light of the recently developed complete GJ set of single random variable stochastic, discrete-time Størmer–Verlet algorithms for statistically accurate simulations of Langevin equations [N. Grønbech-Jensen, Mol. Phys. 118, e1662506 (2020)], we investigate two outstanding questions: (1) Are there any algorithmic or statistical benefits from including multiple random variables per time step and (2) are there objective reasons for using one or more methods from the available set of statistically correct algorithms? To address the first question, we assume a general form for the discrete-time equations with two random variables and then follow the systematic, brute-force GJ methodology by enforcing correct thermodynamics in linear systems. It is concluded that correct configurational Boltzmann sampling of a particle in a harmonic potential implies correct configurational free-particle diffusion and that these requirements only can be accomplished if the two random variables per time step are identical. We consequently submit that the GJ set represents all possible stochastic Størmer–Verlet methods that can reproduce time step-independent statistics of linear systems. The second question is thus addressed within the GJ set. Based on numerical simulations of complex molecular systems, as well as on analytic considerations, we analyze apparent friction-induced differences in the stability of the methods. We attribute these differences to an inherent, friction-dependent discrete-time scaling, which depends on the specific method. We suggest that the method with the simplest interpretation of temporal scaling, the GJ-I/GJF-2GJ method, be preferred for statistical applications.
Transient properties of the one-dimensional washboard potential are investigated in order to understand observed modulations in the statistics of escape events. Specifically, we analyze the effects of different kinds of initial conditions on the escape distribution obtained by linearly increasing the tilt of the potential. Despite the complexity of the dynamics leading up to the eventual escape, we find that the overall statistics can be interpreted in terms of the system parameters, which offers illuminating perspectives for driven one-dimensional systems with washboard potentials. We choose parameters sets relevant for Josephson junctions, a commonly studied system due to both its applications and its use as a model system in condensed matter physics.
The process of activation out a one-dimensional potential is investigated systematically in zero and nonzero temperature conditions. The features of the potential are traced through statistical escape out of its wells whose depths are tuned in time by a forcing term. The process is carried out on the damped pendulum system imposing specific initial conditions on the potential variable. While for relatively high values of the dissipation the statistical properties follow a behavior that can be derived from the standard Kramers model, decreasing the dissipation we observe responses/deviations which have regular dependencies on initial conditions, temperature, and loss parameter itself. It is shown that failures of the thermal activation model are originated at low temperatures, and very low dissipation, by the initial conditions and intrinsic, namely T=0, characteristic oscillations of the potential-generated dynamical equation.
In light of the recently published complete set of statistically correct Grønbech–Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.
The effect of fluctuations on the stability of the zero-voltage state in the Josephson junction has been extensively investigated in the last four decades, due to the fundamental interest in this macroscopic quantum system and in view of possible application as a detector and, more recently, as base for quantum logic. Thermal induced escape from the zero-voltage state is well explained by consolidated theories based on the standard junction electrical model. However, at very low temperatures, significant deviations have been experimentally observed, which have triggered additional theories based on quantization of the Josephson junction effective potential and on macroscopic quantum tunneling. By looking at experiments carried out in the last forty years, we show here that the reported experimental data can be well described by standard theories down to zero temperature, provided that the Josephson potential is shifted by a constant amount, related to the junction plasma frequency. An explanation of this shift is given in terms of Anderson equations, relating chemical potential to phases, energies, and particle numbers in a superfluid flow.
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