arXiv subjects

Flyura Djurabekova

Publications and source records attributed to Flyura Djurabekova.

At least 19 recordsLinked to original sources

arXiv2026

Effect of Near-surface Thermal Spikes on Radiation Hardness of Gallium Oxide

Gallium oxide (Ga$_{2}$O$_{3}$) stands out as an extraordinary high radiation-tolerant semiconductor, because its lattice displacements induce polymorph transitions, holding material crystalline instead of leading to amorphization. Meanwhile, under extremely severe irradiation conditions many crystals become amorphous, often starting from surfaces where the translation symmetry breaks. Here, we show that the surface amorphization may prevail over the crystallisation in Ga$_{2}$O$_{3}$, however only if the mass and energy of irradiated ions produce sufficiently dense heat spikes in the immediate vicinity of the free surface. Applying machine-learned molecular dynamics simulations together with experimental broad-beam and focused ion-beam irradiations, we conclude that the presence of the free surface enables asymmetric displacements of Ga and O atoms, leading to a local non-stoichiometry. Consequently, when the affected cascade volume is sufficiently large, this compositional imbalance suppresses recrystallization and promotes amorphization. As such, our results are ready to use for tailoring irradiation conditions to either prevent or induce surface amorphization, depending on the requirements of the intended applications in Ga$_{2}$O$_{3}$ or other compound semiconductors.

cond-mat.mtrl-sci↗

arXiv2026

A General-Purpose and Efficient Machine-Learned Potential for SiC from Ambient to Extreme Environments

Silicon carbide (SiC) polymorphs are widely employed as nuclear materials, mechanical components, and wide-bandgap semiconductors. The rapid advancement of SiC-based applications has been complemented by computational modeling studies, including both ab initio and classical atomistic approaches. In this work, we develop a computationally efficient, general-purpose machine-learned interatomic potential (ML-IAP) capable of multimillion-atom molecular dynamics simulations over microsecond timescales. Using the ML-IAP, we map a broad pressure-temperature phase diagram and the threshold displacement energy distributions for the 2H and 3C polymorphs. Across a benchmark covering conditions from ambient to extreme, including high-pressure/high-temperature states and high-energy cascade damage, tabGAP provides a favorable balance of accuracy, robustness, transferability, and computational cost among the tested empirical and ML-IAPs.

cond-mat.mtrl-sci↗

arXiv2026

Surface-mediated reduction of ion-irradiation-induced damage in tungsten revealed by advanced ion channeling analysis

Tungsten is a leading candidate material for plasma-facing components in future fusion reactors. In this work, we integrate advanced ion channeling analysis with large-scale molecular dynamics simulations to uncover a pronounced surface-mediated reduction of radiation damage in single-crystal tungsten at elevated temperatures. We demonstrate how our unique analysis method can clearly resolve a dislocation-free zone near the surface and a transition region with suppressed defect density before reaching the bulk value. A possible explanation for the strong surface effect at elevated temperatures can be obtained by considering the coherent drift motion of dislocation loops toward the surface.

cond-mat.mtrl-sci↗

arXiv2026

Fast machine learned interatomic potential for hydrogen-induced embrittlement in α-Fe

In this work, we present a machine-learned interatomic potential for the $α$-Fe-H system based on the tabulated Gaussian Approximation Potential (tabGAP) formalism. Trained on a Density Functional Theory (DFT) dataset of atomic configurations, energies, forces, and virials, the potential is designed for simulations on the mechanisms of hydrogen embrittlement (HE), the issue of H-induced acceleration of mechanical failure of metals. The proposed potential is shown to outperform the widely used classical and machine-learned interatomic potentials in fundamental properties of the $α$-Fe-H system. We show that the tabGAP model reproduces H-point defect properties, H-dislocation interaction, H-H interaction, and elastic constants with nearly DFT-level accuracy at a computational cost that is competitive with the efficient classical Embedded Atom Method (EAM) potentials. As an application of the tabGAP model we simulate the effect of H on the mobility of the $\tfrac{1}{2}\langle 111\rangle$ screw dislocation, which show that the model predicts an enhancement of the mobility of the screw dislocation via trapped H atoms lowering the energy barrier of kink-pair nucleation, resulting in a decreased critical shear stress of dislocation motion at $300\,\mathrm{K}$.

cond-mat.mtrl-sci↗

arXiv2026

Coupled simulation of plasma-surface interactions during early stages of vacuum arcing

We describe fully coupled simulations that bridge atomistic cathode dynamics and plasma formation during the earliest stages of vacuum arcing. The model combines molecular dynamics, finite element electrothermal calculations, electron emission and particle-in-cell plasma simulations via dynamic transfer of particles between the surface and plasma domains. Simulations of Cu nanoprotrusions reveal two routes to thermal runaway: direct Joule heating-driven instability and a novel nanoparticle-assisted mechanism, where detached nanoparticles generate neutral vapor that becomes ionized.

physics.plasm-ph↗

arXiv2026

Anisotropic Core-Shell Swift Heavy Ion Tracks in beta-Ga2O3

Swift heavy ion (SHI) irradiation generates nanoscale ion tracks through intense electronic excitation, yet the microscopic mechanisms governing their morphology and phase stability in low symmetry oxides remain poorly understood. Here, a multiscale atomistic simulation framework is used to investigate the formation and recovery of SHI-induced tracks in monoclinic $β$-Ga2O3 over a wide range of electronic energy losses (Se) and crystallographic orientations. A sequence of distinct structural responses is identified with increasing Se: (i) complete lattice recovery at low Se; (ii) recrystallization into a metastable $γ$-Ga2O3 phase at intermediate Se; and (iii) the formation of core-shell ion tracks at high Se, consisting of an amorphous core surrounded by a recrystallized $γ$-phase shell. Despite the essentially isotropic initial energy deposition, the final ion-track morphology exhibits pronounced crystallographic anisotropy, governed by orientation-dependent recovery dynamics. The superior recrystallization along the [010] direction is attributed to its exceptionally high elastic stiffness. Notably, SHI irradiation perpendicular to the (100) plane induces a more severe structural response at low Se ($\le$ 10 keV/nm), however, at higher Se, it yields a smaller residual ion track compared to the other orientations. The simulated ion-track sizes show excellent quantitative agreement with the available experimental measurements over a wide range of Se values. These findings establish a unified atomic-scale picture of core-shell track formation and anisotropic recovery in $β$-Ga2O3.

cond-mat.mtrl-sci↗

arXiv2026

A Self-Evolving Machine-Learning-Based Kinetic Monte Carlo Method for Modelling Thin-Film Growth

We present a kinetic Monte Carlo (KMC) simulation framework parameterized by automatically sampling machine-learning (ML) for modeling thin-film growth atom by atom. Given an interatomic potential energy function, the KMC algorithm builds an ML-based regression model for rate parameters on runtime, being trained on the local atomic environments encountered during the system evolution. New environments are continuously added to the training set in a self-evolving manner at points where the ML model estimates high uncertainty. As the simulation progresses, the ML model gains confidence, and the quick estimation of rates increasingly overtakes the relatively-expensive nudged elastic band calculations, promoting computational efficiency while retaining high fidelity description of the atomic diffusion kinetics. As a test case, we simulate the sub-monolayer growth of Ag on Ag {111}, where we demonstrate adatom islands forming in shapes and densities in accordance with the underlying atomistic interaction model, the theoretical framework, and available experimental results related to thin-film nucleation and growth.

cond-mat.mtrl-sci↗

arXiv2026

Understanding the anisotropic response of $β$-Ga$_2$O$_3$ to ion implantation

While $β$-Ga$_2$O$_3$ is considered a promising wide bandgap semiconductor, the impact of ion-induced defect formation and anisotropic elasticity remains poorly understood. Here, we combine a simulation and experiment X-ray diffraction (XRD) study of the strain-stress dynamics induced by ion implantation into $β$-Ga$_2$O$_3$ single-crystals with different surface orientations. The strain accumulation in the out-of-plane direction is observed by XRD to occur in an anisotropic manner, with compressive strain along the [010] direction and tensile strain along the directions perpendicular to (100) and (001). An anisotropic stress/strain accumulation model is proposed and probed via Molecular Dynamics (MD), showing an excellent agreement with the experiments. For higher damage levels, pole figures obtained both experimentally and by MD via a novel reciprocal-space projection method reveal an orientation-independent $β$-to-$γ$ phase transition, with a fixed crystallographic relationship between the polymorphs. By exploring the strain-stress dynamics in anisotropic systems, this work establishes a method to directly compare macroscale diffraction experiments and atomistic simulations and opens a new path to engineer the properties of such systems utilizing their anisotropic response to ion implantation/irradiation.

cond-mat.mtrl-sci↗

arXiv2026

Machine-Learned Interatomic Potentials for Structural and Defect Properties of YBa$_2$Cu$_3$O$_{7-δ}$

High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's superconductivity depends strongly on oxygen stoichiometry and defect structure, atomistic simulations can provide crucial insight into radiation-damage mechanisms and pathways to maintain material performance. In this work, we develop and benchmark four Machine-Learned Interatomic Potentials (MLPs) for YBCO: the Atomic Cluster Expansion (ACE), the Message-Passing Atomic Cluster Expansion (MACE), the Gaussian Approximation Potential (GAP), and the Tabulated Gaussian Approximation Potential (tabGAP), trained on an extensive Density Functional Theory (DFT) database explicitly designed to include irradiation-damaged-like configurations. The resulting models achieve DFT-level accuracy across a wide range of atomic environments, faithfully capturing the interatomic forces relevant to radiation damage processes. Among the tested models, MACE delivers the highest accuracy, although at greater computational cost, while ACE and tabGAP provide an excellent balance between efficiency and fidelity. These machine-learned potentials establish a robust foundation for large-scale molecular dynamics simulations of radiation-induced defect evolution in complex superconducting materials

cond-mat.supr-con↗

arXiv2026

Anisotropic Kinetics of Ion-Irradiation-Induced Phase Transition in Gallium Oxide

Radiation-tolerant semiconductors have traditionally been engineered by the principle of suppressing defect accumulation and amorphization, based on the assumption that radiation damage is inherently stochastic. Here we show that, in monoclinic $β$-\ce{Ga2O3}, a promising ultrawide-bandgap semiconductor, surface crystallographic orientation deterministically governs radiation tolerance through highly anisotropic kinetics of the $β$-to-$γ$ phase transition. Using machine-learning molecular dynamics coupled with a local configurational-entropy descriptor, we quantitatively map anisotropic $β$-to-$γ$ transition kinetics, showing that the critical dose, transition-layer depth, and kinetic stability of the $γ$-phase are fundamentally governed by surface orientation. Under ion irradiation, non-channeling surfaces such as (100), (001), and (-201) undergo severe surface amorphization, whereas the strongly channeling (010) surface resists damage accumulation and promotes subsurface $γ$-phase nucleation. During thermal annealing recovery process, these initial states follow two distinct recovery pathways: the channeling (010) surface reverts directly from $γ$-to-$β$, whereas non-channeling surfaces follow a sequential amorphous-to-$γ$-to-$β$ transition pathway. This work establishes surface orientation as a fundamental design principle for achieving radiation tolerance through controlled polymorphic transitions, providing a universal framework for engineering functional materials capable of withstanding extreme irradiation environments.

cond-mat.mtrl-sci↗

arXiv2026

Insights Into Radiation Damage in YBa$_2$Cu$_3$O$_{7-δ}$ From Machine-Learned Interatomic Potentials

Accurate prediction of radiation damage in YBa$_2$Cu$3$O${7-δ}$ (YBCO) is essential for assessing the performance of high-temperature superconducting (HTS) tapes in compact fusion reactors. Existing empirical interatomic potentials have been used to model radiation damage in stoichiometric YBCO, but fail to describe oxygen-deficient compositions, which are ubiquitous in industrial Rare-Earth Barium Copper Oxide conductors and strongly influence superconducting properties. In this work, we demonstrate that modern machine-learned interatomic potentials enable predictive modelling of radiation damage in YBCO across a wide range of oxygen stoichiometries, with higher fidelity than previous empirical models. We employ two recently developed approaches: an Atomic Cluster Expansion (ACE) potential and a tabulated Gaussian Approximation Potential (tabGAP). Both models accurately reproduce Density Functional Theory (DFT) energies, forces, and threshold displacement energy distributions, providing a reliable description of atomic-scale collision processes. Molecular dynamics simulations of 5 keV cascades predict enhanced peak defect production and recombination relative to a widely used empirical potential, indicating different cascade evolution. By explicitly varying oxygen deficiency, we show that total defect production depends only weakly on stoichiometry, offering insight into the robustness of radiation damage processes in oxygen-deficient YBCO. Finally, fusion-relevant 300 keV cascade simulations reveal amorphous regions with dimensions comparable to the superconducting coherence length, consistent with electron microscopy observations of neutron-irradiated HTS tapes. These results establish machine-learned interatomic potentials as efficient and predictive tools for investigating radiation damage in YBCO across relevant compositions and irradiation conditions.

cond-mat.supr-con↗

arXiv2025

Beyond dpa: an atomistic framework for a quantitative description of radiation damage in YBa2Cu3O7

Radiation damage in high-temperature cuprate superconductors represents one of the main technological challenges for their deployment in harsh environments, such as fusion reactors and accelerator facilities. Their complex crystal structure makes modeling irradiation effects in this class of materials a particularly demanding task, for which existing damage models remain inadequate. In this work, we develop an atomistic-based approach for describing primary radiation damage in YBa2Cu3O7, by coupling Molecular Dynamics and Binary Collision Approximation simulations in a way that makes them complementary. When integrated with Primary Knock-on Atom spectra obtained from Monte Carlo codes, our results establish a framework for multiscale modeling of radiation damage, enabling quantitative estimates of several damage descriptors, such as defect production, defect clustering, and the effective damaged volume for any specific irradiation conditions where collision cascades dominate. This computational approach is suitable for the prediction of irradiation effects in any complex functional oxide, with applications ranging from aerospace to nuclear fusion and high-energy physics.

cond-mat.supr-con↗

arXiv2025

Fundamentals of Vacuum Breakdown in High-Field Systems

This review consolidates experimental, theoretical, and simulation work examining the behavior of high-field devices and the fundamental process of vacuum arc initiation, commonly referred to as breakdown. Detailed experimental observations and results relating to a wide range of aspects of high-field devices, including conditioning, field and temperature dependence of breakdown rate, and the ability to sustain high electric fields as a function of device geometry and materials, are presented. The different observations are then addressed theoretically, and with simulation, capturing the sequence of processes that lead to vacuum breakdown and explaining the major observed experimental dependencies. The core of the work described in this review was carried out by a broad multi-disciplinary collaboration in an over a decade-long program to develop high-gradient, 100 MV/m-range, accelerating structures for the CLIC project, a possible future linear-collider high-energy physics facility. Connections are made to the broader linear collider, high-field, and breakdown communities.

physics.app-ph↗

arXiv2025

Influence of an external static magnetic field on prebreakdown electron emission and heating

High magnetic fields can increase the occurrence of vacuum arcing, suggesting that both electric and magnetic fields can play a role in the vacuum arcing process. The mechanism of vacuum arcing in high magnetic fields is believed to involve both the cathode and the anode, with the cathode serving as the originator of field-emitting nanoprotrusions or tips, while the anode serves a secondary role. Significant heating of the anode surface can be achieved by magnetic focusing of the emitted electron beam, leading to increased heat flux due to greater current density. We simulated the emitted electron beam in different configurations of the electric and magnetic fields using the particle-in-cell (PIC) and finite element methods (FEM). The heating caused by the impacting electron beam was simulated for magnetic fields ranging from 0 T to 30 T. The directions of the electric and magnetic fields were found to play a major role in the focusing of the electron beam. We found that a sufficient temperature increase on the anode surface for evaporation can be reached at magnetic fields on the order of 10-30 T, suggesting the possibility of plasma initiation on the anode side.

physics.acc-ph↗

arXiv2025

The diffusion-driven orthorhombic to tetragonal transition in YBa$_2$Cu$_3$O$_7$ derived with a machine learning interatomic potential

Defects in high temperature superconductors such as YBa$_2$Cu$_3$O$_7$ (YBCO) critically influence their superconducting behavior, as they substantially degrade or even suppress superconductivity. With the renewed interest in cuprates for next-generation superconducting magnets operating in radiation-harsh environments such as fusion reactors and particle accelerators, accurate atomistic modeling of defects and their dynamics has become essential. Here, we present a general-purpose machine-learning interatomic potential for YBCO, based on the Atomic Cluster Expansion (ACE) method and trained on Density Functional Theory (DFT) data, with particular emphasis on defects and their diffusion mechanisms. The potential is validated against DFT calculations of ground-state properties, defect formation energies of oxygen Frenkel pairs and diffusion barriers for their formation. Remarkably, the potential captures the diffusion-driven orthorhombic to tetragonal transition at elevated temperatures, a transformation that is difficult to describe with empirical potentials, elucidating how the formation of oxygen Frenkel pairs in the basal plane governs this order-disorder transition. The ACE potential introduced here enables large-scale, predictive atomistic simulations of defect dynamics and transport processes in YBCO, providing a powerful tool to explore its stability, performance, and functionality under realistic operating conditions. Moreover, this work proves that machine learning interatomic potentials are suitable for studies of quaternary oxides with complex chemistry.

cond-mat.mtrl-sci↗

arXiv2025

Spontaneous damage annealing reactions as a possible source of low energy excess in semiconductor detectors

In semiconductor detectors designed for capturing dark matter particles or neutrinos, when the detection threshold is constantly improved to increasingly low energies, an "excess" signal of apparent energy release events below a few hundred eV is observed in several different kinds of detectors. This becomes a big obstacle to the observation of actual dark matter signals, hindering the detectors' sensitivity for rare events in this energy range. Using atomistic simulations with a classical thermostat and a quantum thermal bath, we show that this kind of signal is consistent with energy release from long-term annealing events of complex defects that can be formed by any kind of nuclear recoil radiation events. Such energy releases are shown to have a very similar exponential dependence on energy release magnitudes as that observed in experiments. By detailed analysis of the annealing events, we show that crossing very low energy barriers can trigger larger energy releases in an avalanche-like effect. This explains why large energy release events can occur even down to cryogenic temperatures, where the significant migration of point defects in silicon is hardly ever possible.

cond-mat.mtrl-sci↗

arXiv2025

Ions leaving no tracks

The paths of swift heavy ions are typically traceable in solids, because of confined electronic interactions along the paths, inducing what is known in literature as 'ion tracks', i.e. nano-sized in cross-section cylindrical zones of modified material extending for microns in length. Such tracks readily form in materials exhibiting low thermal conductivities, in particular insulators or semiconductors, altering the homogeneity of materials. In this work, using recently discovered gamma/beta-Ga2O3 polymorph heterostructures we show that, in contrast to the trends in many other materials, including that in beta-Ga2O3, swift heavy ions leave no tracks in gamma-Ga2O3. We explained this trend in terms of amazingly fast disorder recovery, occurring because of multiple configurations in the gamma-Ga2O3 lattice itself, so that the disorder formed by ion impacts gets rapidly erased, giving a perception of ions leaving no tracks. As such, gamma-Ga2O3, readily integrated with beta-Ga2O3 in polymorph heterostructures, may become a promising semiconductor platform for devices capable to operate in extremely harsh radiation environments.

cond-mat.mtrl-sci↗

arXiv2025

Radiation damage and phase stability of Al$_x$CrCuFeNi$_y$ alloys using a machine-learned interatomic potential

We develop a machine-learned interatomic potential for AlCrCuFeNi high-entropy alloys (HEA) using a diverse set of structures from density functional theory calculated including magnetic effects. The potential is based on the computationally efficient tabulated version of the Gaussian approximation potential method (tabGAP) and is a general-purpose model for molecular dynamics simulation of the HEA system, with additional emphasis on radiation damage effects. We use the potential to study key properties of AlCrCuFeNi HEAs at different compositions, focusing on the FCC/BCC phase stability. Monte Carlo swapping simulations are performed to understand the stability and segregation of the HEA and reveal clear FeCr and Cu segregation. Close to equiatomic composition, a transition from FCC to BCC is detected, following the valence electron concentration stability rule. Furthermore, we perform overlapping cascade simulations to investigate radiation damage production and tolerance. Different alloy compositions show significant differences in defect concentrations, and all alloy compositions show enrichment of some elements in or around defects. We find that, generally, a lower Al content corresponds to lower defect concentrations during irradiation. Furthermore, clear short-range ordering is observed as a consequence of continued irradiation.

cond-mat.mtrl-sci↗