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M. A. Novotny

Publications and source records attributed to M. A. Novotny.

At least 73 records · Page 4Linked to original sources

Extreme Long-time Dynamic Monte Carlo Simulations

We study the extreme long-time behavior of the metastable phase of the three-dimensional Ising model with Glauber dynamics in an applied magnetic field and at a temperature below the critical temperature. For these simulations we use the advanced simulation method of projective dynamics. The algorithm is described in detail, together with its application to the escape from the metastable state. Our results for the field dependence of the metastable lifetime are in good agreement with theoretical expectations and span more than fifty decades in time.

cond-mat.stat-mech↗

Absence of First-order Transition and Tri-critical Point in the Dynamic Phase Diagram of a Spatially Extended Bistable System in an Oscillating Field

It has been well established that spatially extended, bistable systems that are driven by an oscillating field exhibit a nonequilibrium dynamic phase transition (DPT). The DPT occurs when the field frequency is on the order of the inverse of an intrinsic lifetime associated with the transitions between the two stable states in a static field of the same magnitude as the amplitude of the oscillating field. The DPT is continuous and belongs to the same universality class as the equilibrium phase transition of the Ising model in zero field [G. Korniss et al., Phys. Rev. E 63, 016120 (2001); H. Fujisaka et al., Phys. Rev. E 63, 036109 (2001)]. However, it has previously been claimed that the DPT becomes discontinuous at temperatures below a tricritical point [M. Acharyya, Phys. Rev. E 59, 218 (1999)]. This claim was based on observations in dynamic Monte Carlo simulations of a multipeaked probability density for the dynamic order parameter and negative values of the fourth-order cumulant ratio. Both phenomena can be characteristic of discontinuous phase transitions. Here we use classical nucleation theory for the decay of metastable phases, together with data from large-scale dynamic Monte Carlo simulations of a two-dimensional kinetic Ising ferromagnet, to show that these observations in this case are merely finite-size effects. For sufficiently small systems and low temperatures, the continuous DPT is replaced, not by a discontinuous phase transition, but by a crossover to stochastic resonance. In the infinite-system limit the stochastic-resonance regime vanishes, and the continuous DPT should persist for all nonzero temperatures.

cond-mat.stat-mech↗

Determining the Saddle Point in Micromagnetic Models of Magnetization Switching

A numerical model of single-domain nanoscale iron magnets fabricated using scanning-microscope-assisted chemical vapor deposition is simulated using finite-temperature micromagnetics. A Projective-dynamics method is used to determine the magnetization at the saddle point as a function of temperature. This magnetization is found to decrease linearly as the temperature is raised.

cond-mat.stat-mech↗

Going through Rough Times: from Non-Equilibrium Surface Growth to Algorithmic Scalability

Efficient and faithful parallel simulation of large asynchronous systems is a challenging computational problem. It requires using the concept of local simulated times and a synchronization scheme. We study the scalability of massively parallel algorithms for discrete-event simulations which employ conservative synchronization to enforce causality. We do this by looking at the simulated time horizon as a complex evolving system, and we identify its universal characteristics. We find that the time horizon for the conservative parallel discrete-event simulation scheme exhibits Kardar-Parisi-Zhang-like kinetic roughening. This implies that the algorithm is asymptotically scalable in the sense that the average progress rate of the simulation approaches a non-zero constant. It also implies, however, that there are diverging memory requirements associated with such schemes.

cond-mat.stat-mech↗

Dynamics of Magnetization Reversal in Models of Magnetic Nanoparticles and Ultrathin Films

We discuss numerical and theoretical results for models of magnetization switching in nanoparticles and ultrathin films. The models and computational methods include kinetic Ising and classical Heisenberg models of highly anisotropic magnets which are simulated by dynamic Monte Carlo methods, and micromagnetics models of continuum-spin systems that are studied by finite-temperature Langevin simulations. The theoretical analysis builds on the fact that a magnetic particle or film that is magnetized in a direction antiparallel to the applied field is in a metastable state. Nucleation theory is therefore used to analyze magnetization reversal as the decay of this metastable phase to equilibrium. We present numerical results on magnetization reversal in models of nanoparticles and films, and on hysteresis in magnets driven by oscillating external fields.

cond-mat.mtrl-sci↗

Dynamic Monte Carlo Simulations for a Square-Lattice Ising Ferromagnet with a Phonon Heat Bath

We derive a direct connection between Monte Carlo time and physical time in terms of physical parameters, using a quantum Hamiltonian with a $d$-dimensional phonon heat bath interacting with a square-lattice Ising ferromagnet. Based on the calculated transition rates, we perform dynamic Monte Carlo simulations using absorbing Markov chains to measure the lifetimes of the metastable state at low temperatures. We also calculate the lifetimes analytically using absorbing Markov chains. The phonon dynamic gives field-dependent prefactors in the lifetimes at low temperatures, that are different from the piecewise field-independent prefactors obtained from the Glauber dynamic.

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A Tutorial on Advanced Dynamic Monte Carlo Methods for Systems with Discrete State Spaces

Advanced algorithms are necessary to obtain faster-than-real-time dynamic simulations in a number of different physical problems that are characterized by widely disparate time scales. Recent advanced dynamic Monte Carlo algorithms that preserve the dynamics of the model are described. These include the $n$-fold way algorithm, the Monte Carlo with Absorbing Markov Chains (MCAMC) algorithm, and the Projective Dynamics (PD) algorithm. To demonstrate the use of these algorithms, they are applied to some simplified models of dynamic physical systems. The models studied include a model for ion motion through a pore such as a biological ion channel and the metastable decay of the ferromagnetic Ising model. Non-trivial parallelization issues for these dynamic algorithms, which are in the class of parallel discrete event simulations, are discussed. Efforts are made to keep the article at an elementary level by concentrating on a simple model in each case that illustrates the use of the advanced dynamic Monte Carlo algorithm.

cond-mat.mtrl-sci↗

Low-Temperature Long-Time Simulations of Ising Ferromagnets using the Monte Carlo with Absorbing Markov Chains method

The Monte Carlo with Absorbing Markov Chains (MCAMC) method is introduced. This method is a generalization of the rejection-free method known as the $n$-fold way. The MCAMC algorithm is applied to the study of the very low-temperature properties of the lifetime of the metastable state of Ising ferromagnets. This is done both for square-lattice and cubic-lattice nearest-neighbor models. Comparison is made with exact low-temperature predictions, in particular the low-temperature predictions that the metastable lifetime is discontinuous at particular values of the field. This discontinuity for the square lattice is not seen in finite-temperatures studies. For the cubic lattice, it is shown that these `exact predictions' are incorrect near the fields where there are discontinuities. The low-temperature formula must be modified and the corrected low-temperature predictions are not discontinuous in the energy of the nucleating droplet.

cond-mat.stat-mech↗

Langevin Simulation of Thermally Activated Magnetization Reversal in Nanoscale Pillars

Numerical solutions of the Landau-Lifshitz-Gilbert micromagnetic model incorporating thermal fluctuations and dipole-dipole interactions (calculated by the Fast Multipole Method) are presented for systems composed of nanoscale iron pillars of dimension 9 nm x 9 nm x 150 nm. Hysteresis loops generated under sinusoidally varying fields are obtained, while the coercive field is estimated to be 1979 $\pm$ 14 Oe using linear field sweeps at T=0 K. Thermal effects are essential to the relaxation of magnetization trapped in a metastable orientation, such as happens after a rapid reversal of an external magnetic field less than the coercive value. The distribution of switching times is compared to a simple analytic theory that describes reversal with nucleation at the ends of the nanomagnets. Results are also presented for arrays of nanomagnets oriented perpendicular to a flat substrate. Even at a separation of 300 nm, where the field from neighboring pillars is only $\sim$ 1 Oe, the interactions have a significant effect on the switching of the magnets.

cond-mat.mtrl-sci↗

Thermal and Dynamic Effects in Langevin Simulation of Hysteresis in Nanoscale Pillars

Dynamic quantities related to hysteresis have been measured in micromagnetic simulations of single-domain nanoscale magnets at nonzero temperature. The hysteresis-loop area and magnetization-field correlation display the characteristics of resonance, and the resonance frequency is found to be temperature dependent. The period-averaged magnetization displays symmetry breaking at high frequencies.

cond-mat.mtrl-sci↗

Thermal Magnetization Reversal in Arrays of Nanoparticles

The results of large-scale simulations investigating the dynamics of magnetization reversal in arrays of single-domain nanomagnets after a rapid reversal of the applied field at nonzero temperature are presented. The numerical micromagnetic approach uses the Landau-Lifshitz-Gilbert equation including contributions from thermal fluctuations and long-range dipole-dipole demagnetizing effects implemented using a fast-multipole expansion. The individual model nanomagnets are 9 nm x 9 nm x 150 nm iron pillars similar to those fabricated on a surface with STM-assisted chemical vapor deposition [S. Wirth, et al., J. Appl. Phys {\bf 85}, 5249 (1999)]. Nanomagnets oriented perpendicular to the surface and spaced 300 nm apart in linear arrays are considered. The applied field is always oriented perpendicular to the surface. When the magnitude of the applied field is less than the coercive value, about 2000 Oe for an individual nanomagnet, magnetization reversal in the nanomagnets can only occur by thermally activated processes. Even though the interaction from the dipole moment of neighboring magnets in this geometry is only about 1 Oe, less than 1% of the coercive field, it can have a large impact on the switching dynamics. What determines the height of the free-energy barrier is the difference between the coercive and applied fields, and 1 Oe can be a significant fraction of that. The magnetic orientations of the neighbors are seen to change the behavior of the nanomagnets in the array significantly.

cond-mat.mtrl-sci↗

Dynamic Phase Transition, Universality, and Finite-size Scaling in the Two-dimensional Kinetic Ising Model in an Oscillating Field

We study the two-dimensional kinetic Ising model below its equilibrium critical temperature, subject to a square-wave oscillating external field. We focus on the multi-droplet regime where the metastable phase decays through nucleation and growth of many droplets of the stable phase. At a critical frequency, the system undergoes a genuine non-equilibrium phase transition, in which the symmetry-broken phase corresponds to an asymmetric stationary limit cycle for the time-dependent magnetization. We investigate the universal aspects of this dynamic phase transition at various temperatures and field amplitudes via large-scale Monte Carlo simulations, employing finite-size scaling techniques adopted from equilibrium critical phenomena. The critical exponents, the fixed-point value of the fourth-order cumulant, and the critical order-parameter distribution all are consistent with the universality class of the two-dimensional equilibrium Ising model. We also study the cross-over from the multi-droplet to the strong-field regime, where the transition disappears.

cond-mat.stat-mech↗

Fine-grid Simulations of Thermally Activated Switching in Nanoscale Magets

Numerical integration of the Landau-Lifshitz-Gilbert equation with thermal fluctuations is used to study the dynamic response of single-domain nanomagnets to rapid changes in the applied magnetic field. The simulation can resolve magnetization patterns within nanomagnets and uses the Fast Multipole method to calculate dipole-dipole interactions efficiently. The thermal fluctuations play an essential part in the reversal process whenever the applied field is less than the zero-temperature coercive field. In this situation pillar-shaped nanomagnets are found to reverse through a local curling mode that involves the formation and propagation of a domain wall. Tapering the ends of the pillars to reduce pole-avoidance effects changes the energies involved but not the fundamental process. The statistical distribution of switching times is well described by the independent nucleation and subsequent growth of regions of reversed magnetization at both ends of the pillar.

cond-mat.mtrl-sci↗

Non-equilibrium Surface Growth and Scalability of Parallel Algorithms for Large Asynchronous Systems

The scalability of massively parallel algorithms is a fundamental question in computer science. We study the scalability and the efficiency of a conservative massively parallel algorithm for discrete-event simulations where the discrete events are Poisson arrivals. The parallel algorithm is applicable to a wide range of problems, including dynamic Monte Carlo simulations for large asynchronous systems with short-range interactions. The evolution of the simulated time horizon is analogous to a growing and fluctuating surface, and the efficiency of the algorithm corresponds to the density of local minima of this surface. In one dimension we find that the steady state of the macroscopic landscape is governed by the Edwards-Wilkinson Hamiltonian, which implies that the algorithm is scalable. Preliminary results for higher-dimensional logical topologies are discussed.

cond-mat.stat-mech↗

From Massively Parallel Algorithms and Fluctuating Time Horizons to Non-equilibrium Surface Growth

We study the asymptotic scaling properties of a massively parallel algorithm for discrete-event simulations where the discrete events are Poisson arrivals. The evolution of the simulated time horizon is analogous to a non-equilibrium surface. Monte Carlo simulations and a coarse-grained approximation indicate that the macroscopic landscape in the steady state is governed by the Edwards-Wilkinson Hamiltonian. Since the efficiency of the algorithm corresponds to the density of local minima in the associated surface, our results imply that the algorithm is asymptotically scalable.

cond-mat.stat-mech↗

Micromagnetic simulations of thermally activated magnetization reversal of nanoscale magnets

Numerical integration of a stochastic Landau-Lifshitz-Gilbert equation is used to study dynamic processes in single-domain nanoscale magnets at nonzero temperatures. Special attention is given to including thermal fluctuations as a Langevin term, and the Fast Multipole Method is used to calculate dipole-dipole interactions. It is feasible to simulate these dynamics on the nanosecond time scale for spatial discretizations that involve on the order of 10000 nodes using a desktop workstation. The nanoscale magnets considered here are single pillars with large aspect ratio. Hysteresis-loop simulations are employed to study the stable and metastable configurations of the magnetization. Each pillar has magnetic end caps. In a time-dependent field the magnetization of the pillars is observed to reverse via nucleation, propagation, and coalescence of the end caps. In particular, the end caps propagate into the magnet and meet near the middle. A relatively long-lived defect is formed when end caps with opposite vorticity meet. Fluctuations are more important in the reversal of the magnetization for fields weaker than the zero-temperature coercive field, where the reversal is thermally activated. In this case, the process must be described by its statistical properties, such as the distribution of switching times, averaged over a large number of independent thermal histories.

cond-mat.mtrl-sci↗

Simulations of metastable decay in two- and three-dimensional models with microscopic dynamics

We present a brief analysis of the crossover phase diagram for the decay of a metastable phase in a simple dynamic lattice-gas model of a two-phase system. We illustrate the nucleation-theoretical analysis with dynamic Monte Carlo simulations of a kinetic Ising lattice gas on square and cubic lattices. We predict several regimes in which the metastable lifetime has different functional forms, and provide estimates for the crossovers between the different regimes. In the multidroplet regime, the Kolmogorov-Johnson-Mehl-Avrami theory for the time dependence of the order-parameter decay and the two-point density correlation function allows extraction of both the order parameter in the metastable phase and the interfacial velocity from the simulation data.

cond-mat.mtrl-sci↗

Advanced Dynamic Algorithms for the Decay of Metastable Phases in Discrete Spin Models: Bridging Disparate Time Scales

An overview of advanced dynamical algorithms capable of spanning the widely disparate time scales that govern the decay of metastable phases in discrete spin models is presented. The algorithms discussed include constrained transfer-matrix, Monte Carlo with Absorbing Markov Chains (MCAMC), and projective dynamics (PD) methods. The strengths and weaknesses of each of these algorithms are discussed, with particular emphasis on identifying the parameter regimes (system size, temperature, and field) in which each algorithm works best.

cond-mat.mtrl-sci↗