arXiv subjects

Flyura Djurabekova

Publications and source records attributed to Flyura Djurabekova.

At least 55 records · Page 3Linked to original sources

arXiv2023

Complex $\mathrm{Ga}_{2}\mathrm{O}_{3}$ Polymorphs Explored by Accurate and General-Purpose Machine-Learning Interatomic Potentials

$\mathrm{Ga}_{2}\mathrm{O}_{3}$ is a wide-bandgap semiconductor of emergent importance for applications in electronics and optoelectronics. However, vital information of the properties of complex coexisting $\mathrm{Ga}_{2}\mathrm{O}_{3}$ polymorphs and low-symmetry disordered structures is missing. In this work, we develop two types of kernel-based machine-learning Gaussian approximation potentials (ML-GAPs) for $\mathrm{Ga}_{2}\mathrm{O}_{3}$ with high accuracy for $β$/$κ$/$α$/$δ$/$γ$ polymorphs and generality for disordered stoichiometric structures. We release two versions of interatomic potentials in parallel, namely soapGAP and tabGAP, for excellent accuracy and exceeding speedup, respectively. We systematically show that both the soapGAP and tabGAP can reproduce the structural properties of all the five polymorphs in an exceptional agreement with ab initio results, meanwhile boost the computational efficiency with $5\times10^{2}$ and $2\times10^{5}$ computing speed increases compared to density functional theory, respectively. The results show that the liquid-solid phase transition proceeds in three different stages, a "slow transition", "fast transition" and "only Ga migration". We show that this complex dynamics can be understood in terms of different behavior of O and Ga sublattices in the interfacial layer.

cond-mat.mtrl-sci↗

arXiv2022

Biased self-diffusion on Cu surface due to electric field gradients

Under strong electric fields, an arc of strong current flowing through plasma can link two metal surfaces even in ultra high vacuum. Despite decades of research, the chain of events leading to vacuum arc breakdowns is hitherto unknown. Previously we showed that a tall and sharp Cu nanotip exposed to strong electric fields heats up by field emission currents and eventually melts, evaporating neutral atoms that can contribute to plasma buildup. In this work, we investigate by means of molecular dynamics simulations whether surface diffusion biased by the presence of an electric field gradient can provide sufficient mass transport of atoms toward the top of the nanotip to maintain supply of neutrals for feeding plasma. To reach the necessary timescales and to add electric field in MD, we utilized a novel combination of collective variable~-driven hyperdynamics acceleration and coupling to a finite element mesh. In our simulations, we observed biased self-diffusion on Cu surfaces, that can contribute to the continuous replenishment of particle-emitting nanotips. This mechanism implies a need to reduce the rate of surface diffusion in devices that are susceptible to vacuum arcs. Finding suitable alloys or surface treatments that hinder the observed biased diffusion could guide the design of future devices, and greatly improve their efficiency.

cond-mat.mtrl-sci↗

arXiv2022

Simple machine-learned interatomic potentials for complex alloys

Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method.

cond-mat.mtrl-sci↗

arXiv2022

Insights into nanoparticle shape transformation by energetic ions using atomistic simulations

The shape of metal nanoparticles embedded in dielectric matrices influences the optical properties of the composite material. Swift heavy ion irradiation can induce a controllable shape transformation in gold and other metals embedded in amorphous silicon dioxide, where the particles elongate along the direction of the ion beam. The details of this transformation are not fully understood, but it is presumably related to nanometer-scale phase transitions induced by individual ion impacts. The phenomenon has been reproduced using atomistic simulations, although the time scale limitations and the lack of accurate interatomic models within the metal-silica interface lead unavoidably to severe simplifications. We improve the realism in the simulations with an accurate model for surface adhesion between gold and silica and by simulating the processes in the matrix between impacts. The simulations with correct adhesion show that the nanoparticles can grow in aspect ratio in the molten state even after silicon dioxide solidifies. Moreover, we demonstrate the active role of the matrix: without explicitly modeling processes in the matrix between impacts, the elongation is limited and does not reach significant aspect ratios seen in experiments. These results significantly improve the theoretical understanding of processes developing in embedded nanoparticles under swift heavy ion irradiation. The knowledge brings forward the ion beam technology as a precise tool for shaping of embedded nanostructures for various optical applications.

cond-mat.mtrl-sci↗

arXiv2022

Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron

We develop and compare four interatomic potentials for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and three-body terms, and a Gaussian approximation potential with the SOAP descriptor. All potentials are trained to the same diverse database of body-centered cubic and liquid structures computed with density functional theory. The four presented potentials represent different levels of complexity and span three orders of magnitude in computational cost. The first three potentials are tabulated and evaluated efficiently using cubic spline interpolations, while the fourth one is implemented without additional optimization. We compare and discuss the advantages of each implementation, transferability and applicability in terms of the balance between required accuracy versus computational cost.

cond-mat.mtrl-sci↗

arXiv2022

Polarization characteristics of adatoms self-diffusing on metal surfaces under high electric fields

Although atomic diffusion on metal surfaces under high electric fields has been studied theoretically and experimentally since the 1970s, its accurate and quantitative theoretical description remains a significant challenge. In our previous work, we developed a theoretical framework that describes the atomic dynamics on metal surfaces in the presence of an electric field in terms of the local polarization characteristics of the surface at the vicinity of a moving atom. Here, we give a deeper analysis of the physics underlying this framework, introducing and rigorously defining the concept of the effective polarization characteristics (permanent dipole moment $μ$ and polarizability $α$) of a moving atom on a metal surface, which are shown to be the relevant atomic quantities determining the dynamics of a moving atom via a compact equation. We use density functional theory (DFT) to calculate $μ$ and $α$ of a W adatom moving on a W {110} surface, where additional adatoms are present in its vicinity. We analyze the dependence of $μ$ and $α$ and hence the migration barriers under electric fields on the local atomic environments (LAE) of an adatom. We find that the LAE significantly affects $μ$ and $α$ of a moving atom in the limited cases we studied, which implies that further systematic DFT calculations are needed to fully parameterize surface diffusion in terms of energy barriers for long-term large scale simulations, such as our recently developed Kinetic Monte Carlo model for surface diffusion under electric field.

cond-mat.mtrl-sci↗

arXiv2021

Linear voltage recovery after a breakdown in a pulsed dc system

Breakdowns may occur in high-voltage applications even in ultrahigh vacuum conditions. Previously, we showed that it is important to pay attention to the post-breakdown voltage recovery in order to limit the appearance of secondary breakdowns associated with the primary ones. This can improve the overall efficiency of the high-voltage device. In this study, we focus on the optimization of the linear post-breakdown voltage recovery, with the principle aim of alleviating the problem of the secondary breakdowns. We investigate voltage recovery scenarios with different starting voltages and slopes of linear voltage increase by using a pulsed dc system. We find that a higher number of pulses during the voltage recovery produces fewer secondary BDs and a lower overall breakdown rate. Lowering the number of pulses led to more dramatic voltage recovery resulting in higher breakdown rates. A steeper voltage increase rate lead to a more localized occurrence of the secondary breakdowns near the end of the voltage recovery period. It was also found that the peak BD probability is regularly observed around 1 s after the end of the ramping period and that its value decreases exponentially with the amount of energy put into the system during the ramping. The value also decays exponentially with a half-life of (1.4$\pm$0.3) ms if the voltage only increased between the voltage recovery steps.

cond-mat.mtrl-sci↗

arXiv2021

Modeling refractory high-entropy alloys with efficient machine-learned interatomic potentials: defects and segregation

We develop a fast and accurate machine-learned interatomic potential for the Mo-Nb-Ta-V-W quinary system and use it to study segregation and defects in the body-centred cubic refractory high-entropy alloy MoNbTaVW. In the bulk alloy, we observe clear ordering of mainly Mo-Ta and V-W binaries at low temperatures. In damaged crystals, our simulations reveal clear segregation of vanadium, the smallest atom in the alloy, to compressed interstitial-rich regions like radiation-induced dislocation loops. Vanadium also dominates the population of single self-interstitial atoms. In contrast, due to its larger size and low surface energy, niobium segregates to spacious regions like the inner surfaces of voids. When annealing samples with supersaturated concentrations of defects, we find that in complete contrast to W, interstitial atoms in MoNbTaVW cluster to create only small ($\sim 1$ nm) experimentally invisible dislocation loops enriched by vanadium. By comparison to W, we explain this by the reduced but three-dimensional migration of interstitials, the immobility of dislocation loops, and the increased mobility of vacancies in the high-entropy alloy, which together promote defect recombination over clustering.

cond-mat.mtrl-sci↗

arXiv2021

Phase Transition of Two-Dimensional Ferroelectric and Paraelectric Ga2O3 Monolayer: A Density Functional Theory and Machine-Learning Study

Ga2O3 is a wide-band-gap semiconductor of great interest for applications in electronics and optoelectronics. Two-dimensional (2D) Ga2O3 synthesized from top-down or bottom-up processes can reveal brand new heterogeneous structures and promising applications. In this paper, we study phase transitions among three low-energy stable Ga2O3 monolayer configurations using density functional theory and a newly developed machine-learning Gaussian approximation potential, together with solid-state nudged elastic band calculations. Kinetic minimum energy paths involving direct atomic jump as well as concerted layer motion are investigated. The low phase transition barriers indicate feasible tunability of the phase transition and orientation via strain engineering and external electric fields. Large-scale calculations using the newly trained machine-learning potential on the thermally activated single-atom jumps reveal the clear nucleation and growth processes of different domains. The results provide useful insights to future experimental synthesis and characterization of 2D Ga2O3 monolayers.

cond-mat.mtrl-sci↗

arXiv2021

General scaling laws of space charge effects in field emission

The characteristics of field electron and ion emission change when the space charge formed by the emitted charge is sufficient to suppress the extracting electric field. This phenomenon is well described for planar emitting diodes by the one dimensional (1D) theory. Here we generalize for any 3D geometry by deriving the scaling laws describing the field suppression in the weak space charge regime. We propose a novel corrected equivalent planar diode model, which describes the space charge effects for any geometry in terms of the 1D theory, utilizing a correction factor that adjusts the diode's scaling characteristics. We then develop a computational method, based on the Particle-In-Cell technique, which solves numerically the space charge problem. We validate our theory by comparing it to both our numerical calculations and existing experimental data, either of which can be used to obtain the geometrical correction factor of the corrected equivalent planar diode model.

physics.app-ph↗

arXiv2020

Electron cascades and secondary electron emission in graphene under energetic ion irradiation

Highly energetic ions traversing a two-dimensional material such as graphene produce strong electronic excitations. Electrons excited to energy states above the work function can give rise to secondary electron emission, reducing the amount of energy that remains the graphene after the ion impact. Electrons can either be emitted (kinetic energy transfer) or captured by the passing ion (potential energy transfer). To elucidate this behavior that is absent in three-dimensional materials, we simulate the electron dynamics in graphene during the first femtoseconds after ion impact. We employ two conceptually different computational methods: a Monte Carlo (MC) based one, where electrons are treated as classical particles, and time-dependent density functional theory (TDDFT), where electrons are described quantum-mechanically. We observe that the linear dependence of electron emission on deposited energy, emerging from MC simulations, becomes sublinear and closer to the TDDFT values when the electrostatic interactions of emitted electrons with graphene are taken into account via complementary particle-in-cell simulations. Our TDDFT simulations show that the probability for electron capture decreases rapidly with increasing ion velocity, whereas secondary electron emission dominates in the high velocity regime. We estimate that these processes reduce the amount of energy deposited in the graphene layer by 15\,\% to 65\,\%, depending on the ion and its velocity. This finding clearly shows that electron emission must be taken into consideration when modelling damage production in two-dimensional materials under ion irradiation.

cond-mat.mtrl-sci↗

arXiv2020

Effect of dc voltage pulsing on high-vacuum electrical breakdowns near Cu surfaces

Vacuum electrical breakdowns, also known as vacuum arcs, are a limiting factor in many devices that are based on application of high electric fields near their component surfaces. Understanding of processes that lead to breakdown events may help mitigating their appearance and suggest ways for improving operational efficiency of power-consuming devices. Stability of surface performance at a given value of the electric field is affected by the conditioning state, i.e. how long the surface was exposed to this field. Hence, optimization of the surface conditioning procedure can significantly speed up the preparatory steps for high-voltage applications. In this article, we use pulsed dc systems to optimize the surface conditioning procedure of copper electrodes, focusing on the effects of voltage recovery after breakdowns, variable repetition rates as well as long waiting times between pulsing runs. Despite the differences in the experimental scales, ranging from $10^{-4}$ s between pulses, up to pulsing breaks of $10^5$ s, the experiments show that the longer the idle time between the pulses, the more probable it is that the next pulse produces a breakdown. We also notice that secondary breakdowns, i.e. those which correlate with the previous ones, take place mainly during the voltage recovery stage. We link these events with deposition of residual atoms from vacuum on the electrode surfaces. Minimizing the number of pauses during the voltage recovery stage reduces power losses due to secondary breakdown events improving efficiency of the surface conditioning.

physics.ins-det↗

arXiv2020

Gaussian approximation potentials for body-centered-cubic transition metals

We develop a set of machine-learning interatomic potentials for elemental V, Nb, Mo, Ta, and W using the Gaussian approximation potential framework. The potentials show good accuracy and transferability for elastic, thermal, liquid, defect, and surface properties. All potentials are augmented with accurate repulsive potentials, making them applicable to radiation damage simulations involving high-energy collisions. We study melting and liquid properties in detail and use the potentials to provide melting curves up to 400 GPa for all five elements.

cond-mat.mtrl-sci↗

arXiv2020

Dynamic coupling between particle-in-cell and atomistic simulations

We propose a method to directly couple molecular dynamics, finite element method and particle-in-cell techniques to simulate metal surface response to high electric fields. We use this method to simulate the evolution of a field emitting tip under thermal runaway by fully including the 3D space-charge effects. We also present a comparison of the runaway process between the two tip geometries of different widths. The results show with high statistical significance that in case of sufficiently narrow field emitters, the thermal runaway occurs in cycles where intensive neutral evaporation alternates with the cooling periods. The comparison with previous works shows, that the evaporation rate in the regime of intensive evaporation is sufficient to ignite a plasma arc above the simulated field emitters.

cs.CE↗

arXiv2020

Growth mechanism for nanotips in high electric fields

In this work we show using atomistic simulations that the biased diffusion in high electric field gradients creates a mechanism whereby nanotips may start growing from small surface asperities. It has long been known that atoms on a metallic surface have biased diffusion if electric fields are applied and that microscopic tips may be sharpened using fields, but the exact mechanisms have not been well understood. Our Kinetic Monte Carlo simulation model uses a recently developed theory for how the migration barriers are affected by the presence of an electric field. All parameters of the model are physically motivated and no fitting parameters are used. The model has been validated by reproducing characteristic faceting patterns of tungsten surfaces that have in previous experiments been observed to only appear in the presence of strong electric fields. The growth effect is found to be enhanced by increasing fields and temperatures.

cond-mat.mtrl-sci↗

arXiv2020

Gradient-Based Training and Pruning of Radial Basis Function Networks with an Application in Materials Physics

Many applications, especially in physics and other sciences, call for easily interpretable and robust machine learning techniques. We propose a fully gradient-based technique for training radial basis function networks with an efficient and scalable open-source implementation. We derive novel closed-form optimization criteria for pruning the models for continuous as well as binary data which arise in a challenging real-world material physics problem. The pruned models are optimized to provide compact and interpretable versions of larger models based on informed assumptions about the data distribution. Visualizations of the pruned models provide insight into the atomic configurations that determine atom-level migration processes in solid matter; these results may inform future research on designing more suitable descriptors for use with machine learning algorithms.

cs.LG↗

arXiv2020

Data sets and trained neural networks for Cu migration barriers

Kinetic Monte Carlo (KMC) is an efficient method for studying diffusion. A limiting factor to the accuracy of KMC is the number of different migration events allowed in the simulation. Each event requires its own migration energy barrier. The calculation of these barriers may be unfeasibly expensive. In this article we present a data set of migration barriers on for nearest-neighbour jumps on the Cu surfaces, calculated with the nudged elastic band (NEB) method and the tethering force approach. We used the data to train artifcial neural networks (ANN) in order to predict the migration barriers for arbitrary nearest-neighbour Cu jumps. The trained ANNs are also included in the article. The data is hosted by the CSC IDA storage service.

physics.comp-ph↗

arXiv2020

Application of artificial neural networks for rigid lattice kinetic Monte Carlo studies of Cu surface diffusion

Kinetic Monte Carlo (KMC) is a powerful method for simulation of diffusion processes in various systems. The accuracy of the method, however, relies on the extent of details used for the parameterization of the model. Migration barriers are often used to describe diffusion on atomic scale, but the full set of these barriers may become easily unmanageable in materials with increased chemical complexity or a large number of defects. This work is a feasibility study for applying a machine learning approach for Cu surface diffusion. We train an artificial neural network on a subset of the large set of $2^{26}$ barriers needed to correctly describe the surface diffusion in Cu. Our KMC simulations using the obtained barrier predictor show sufficient accuracy in modelling processes on the low-index surfaces and display the correct thermodynamical stability of these surfaces.

physics.comp-ph↗