Dilina Perera

Phonon-magnon interactions in body centered cubic iron: A combined molecular and spin dynamics study

Combining an atomistic many-body potential with a classical spin Hamiltonian parameterized by first principles calculations, molecular-spin dynamics computer simulations were performed to investigate phonon-magnon interactions in body centered cubic iron. Results obtained for spin-spin and density-density dynamic structure factors show that noticeable softening and damping of magnon modes occur due to the presence of lattice vibrations. Furthermore, as a result of the phonon-magnon coupling, additional longitudinal spin wave excitations are observed, with the same frequencies as the longitudinal phonon modes.

Collective dynamics in atomistic models with coupled translational and spin degrees of freedom

Using an atomistic model that simultaneously treats the dynamics of translational and spin degrees of freedom, we perform combined molecular and spin dynamics simulations to investigate the mutual influence of the phonons and magnons on their respective frequency spectra and lifetimes in ferromagnetic bcc iron. By calculating the Fourier transforms of the space- and time-displaced correlation functions, the characteristic frequencies and the linewidths of the vibrational and magnetic excitation modes were determined. Comparison of the results with that of the stand-alone molecular dynamics and spin dynamics simulations reveals that the dynamic interplay between the phonons and magnons leads to a shift in the respective frequency spectra and a decrease in the lifetimes. Moreover, in the presence of lattice vibrations, additional longitudinal magnetic excitations were observed with the same frequencies as the longitudinal phonons.

Combined molecular and spin dynamics simulation of bcc iron with lattice vacancies

Using an atomistic model that treats both translational and spin degrees of freedom, we have performed combined molecular and spin dynamics simulations to study dynamic properties of BCC iron with varying vacancy concentrations. Atomic interactions are described by an empirical many-body potential while spin interactions are handled with a Heisenberg-like Hamiltonian with a coordinate dependent exchange interaction. By calculating the Fourier transform of spatial and temporal correlation functions, vibrational and magnetic excitations have been studied. The creation of vacancies in the material has shown splitting of the characteristic transverse spin-wave excitations, indicating the production of additional excitation modes. By merging two vacancies to form a nearest neighbor pair, we find that these modes become more distinct. Investigation of longitudinal spin-wave excitations reveals interactions between constituent components of the split transverse excitations.

Empirical investigation of the low temperature energy function of the Restricted Boltzmann Machine using a 1000 qubit D-Wave 2X

D-Wave 2X with more than 1000 qubits was applied to the relatively rugged energy landscape of trained Restricted Boltzmann Machines (RBMs). The D-Wave machine has a Chimera interconnect architecture. A native RBM restricted to the Chimera graph was found difficult to train for large number of RBM units. To overcome this difficulty, a RBM embedding that combined qubits in order to significantly increase the connectivity between hidden and visible units of the RBM was investigated. The results for the lowest-energy and some of the higher-energy states found by D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm. In many cases, the D-Wave machine successfully found the same RBM lowest energy state as that found by SA. In the relatively simple cases of training patterns investigated in this work, the lowest energy state also corresponded to one of the training patterns. Sometimes the D-Wave machine returned a state corresponding to one of the higher energy states found by SA. The inherently non-perfect embedding of the RBM into the Chimera lattice used in this work (i.e., multiple qubits combined into a single RBM unit were not guaranteed to have perfectly aligned qubits) and the existence of small bias fields in the D-Wave hardware were found to be responsible for the discrepancy in the D-Wave and the SA results. In some but not all of the investigated cases, introduction of a small bias field into the energy function or optimization of the chain-strength parameter in the D-Wave embedding successfully addressed difficulties of the particular RBM embedding.

Combined molecular dynamics-spin dynamics simulations of bcc iron

Using a classical model that treats translational and spin degrees of freedom on an equal footing, we study phonon-magnon interactions in BCC iron with combined molecular and spin dynamics methods. The atomic interactions are modeled via an empirical many-body potential while spin dependent interactions are established through a Hamiltonian of the Heisenberg form with a distance dependent magnetic exchange interaction obtained from first principles electronic structure calculations. The temporal evolution of translational and spin degrees of freedom was determined by numerically solving the coupled equations of motion, using an algorithm based on the second order Suzuki-Trotter decomposition of the exponential operators. By calculating Fourier transforms of space- and time-displaced correlation functions, we demonstrate that the the presence of lattice vibrations leads to noticeable softening and damping of spin wave modes. As a result of the interplay between lattice and spin subsystems, we also observe additional longitudinal spin wave excitations, with frequencies which coincide with that of the longitudinal lattice vibrations.

An evaluation of the performance of Restricted Boltzmann Machines as a model for anomaly network intrusion detection

Abstract The continuous increase in the number of attacks on computer networks has raised serious concerns regarding the importance of establishing a methodology that can learn and adapt to new and novel attacks, such a model should be able to act or react to such threats within a timely manner, so that measures are undertaken to counter any potential breaches within the network. Training a model to distinguish between normal and anomalous network behavior is a difficult task due to the high dimensionality of the network traffic data. One of the key requirements of a successful Anomaly Network Intrusion Detection Systems (A-NIDS) is the ability to recognize new patterns of attacks that it has never before seen. This objective can be achieved through incorporating machine leaning techniques in the learning model of the A-NIDS. In this study, we demonstrate the use of a powerful machine learning technique called the Restricted Boltzmann Machine (RBM) to distinguish between normal and anomalous NetFlow traffic. We evaluate our approach through testing it on the newly renowned Information Security Center of Excellence (ISCX) dataset. Our results indicate that RBMs can be trained successfully to classify normal and anomalous NetFlow traffic. Unlike previous studies, we employ measures of true positives and negatives along with the accuracy to test the effectiveness of RBM as a classifier for A-NIDS. We also utilize the usage of a balanced set to reduce any biases that appear during the RBM training.

Determination of the Lowest-Energy States for the Model Distribution of Trained Restricted Boltzmann Machines Using a 1000 Qubit D-Wave 2X Quantum Computer

The possibility of using a quantum computer D-Wave 2X with more than 1000 qubits to determine the global minimum of the energy landscape of trained restricted Boltzmann machines is investigated. In order to overcome the problem of limited interconnectivity in the D-Wave architecture, the proposed RBM embedding combines multiple qubits to represent a particular RBM unit. The results for the lowest-energy (the ground state) and some of the higher-energy states found by the D-Wave 2X were compared with those of the classical simulated annealing (SA) algorithm. In many cases, the D-Wave machine successfully found the same RBM lowest-energy state as that found by SA. In some examples, the D-Wave machine returned a state corresponding to one of the higher-energy local minima found by SA. The inherently nonperfect embedding of the RBM into the Chimera lattice explored in this work (i.e., multiple qubits combined into a single RBM unit were found not to be guaranteed to be all aligned) and the existence of small, persistent biases in the D-Wave hardware may cause a discrepancy between the D-Wave and the SA results. In some of the investigated cases, introduction of a small bias field into the energy function or optimization of the chain-strength parameter in the D-Wave embedding successfully addressed difficulties of the particular RBM embedding. With further development of the D-Wave hardware, the approach will be suitable for much larger numbers of RBM units.

Magnetic phase transition in coupled spin-lattice systems: A replica-exchange Wang-Landau study

Coupled, dynamical spin-lattice models provide a unique test ground for simulations investigating the finite-temperature magnetic properties of materials under the direct influence of the lattice vibrations. These models are constructed by combining a coordinate-dependent interatomic potential with a Heisenberg-like spin Hamiltonian, facilitating the treatment of both the atomic coordinates and the spins as explicit phase variables. Using a model parameterized for bcc iron, we study the magnetic phase transition in these complex systems via the recently introduced, massively parallel replica-exchange Wang-Landau Monte Carlo method. Comparison with the results obtained from rigid lattice (spin-only) simulations shows that the transition temperature as well as the amplitude of the peak in the specific heat curve is marginally affected by the lattice vibrations. Moreover, the results were found to be sensitive to the particular choice of interatomic potential.

Replica-exchange Wang Landau sampling: pushing the limits of Monte Carlo simulations in materials sciences

We describe the study of thermodynamics of materials using replica-exchange Wang Landau (REWL) sampling, a generic framework for massively parallel implementations of the Wang Landau Monte Carlo method. To evaluate the performance and scalability of the method, we investigate the magnetic phase transition in body-centered cubic (bcc) iron using the classical Heisenberg model parameterized with first principles calculations. We demonstrate that our framework leads to a significant speedup without compromising the accuracy and precision and facilitates the study of much larger systems than is possible with its serial counterpart.

Magnetic Materials at finite Temperatures: thermodynamics and combined spin and molecular dynamics derived from first principles calculations

We present a unified approach to describe the combined behavior of the atomic and magnetic degrees of freedom in magnetic materials. Using Monte Carlo simulations directly combined with first principles the Curie temperature can be obtained ab initio in good agreement with experimental values. The large scale constrained first principles calculations have been used to construct effective potentials for both the atomic and magnetic degrees of freedom that allow the unified study of influence of phonon-magnon coupling on the thermodynamics and dynamics of magnetic systems. The MC calculations predict the specific heat of iron in near perfect agreement with experimental results from 300K to above Tc and allow the identification of the importance of the magnon-phonon interaction at the phase-transition. Further Molecular Dynamics and Spin Dynamics calculations elucidate the dynamics of this coupling and open the potential for quantitative and predictive descriptions of dynamic structure factors in magnetic materials using first principles derived simulations.