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Disorder-induced ordering and unprecedentedly high radiation tolerance in $\gamma$-phase of gallium oxide is a recent spectacular discovery at the intersection of the fundamental physics and electronic applications. Importantly, by far, these data were collected with initial samples in form of the thermodynamically stable $\beta$-phase of this material. Here, we investigate these phenomena starting instead from already metastable $\alpha$-phase and explain radically new trend occurring in the system. We argue that in contrast to that in $\beta$-to-$\gamma$ disorder-induced transitions, the O sublattice in $\alpha$-phase exhibits hexagonal close-packed structure, so that to activate $\alpha$-to-$\gamma$ transformation significant structural rearrangements are required in both Ga and O sublattices. Moreover, consistently with theoretical predictions, $\alpha$-to-$\gamma$ phase transformation requires accumulation of the substantial tensile strain to initiate otherwise impossible lattice glides. Thus, we explain the experimentally observed trends in term of the combination of disorder and strain governing the process. Finally, and perhaps most amazingly, we demonstrate atomically abrupt $\alpha$/$\gamma$ interfaces paradoxically self-organized out of the stochastic disorder., Comment: 9 pages, 4 figures, under peer review

Refractory high-entropy alloys show promising resistance to irradiation, yet little is known about the fundamental nature of radiation-induced defect formation. Here, we simulate threshold displacement energies in equiatomic MoNbTaVW using an accurate machine-learned interatomic potential, covering the full angular space of crystal directions. The effects of local chemical ordering is assessed by comparing results in randomly ordered and short-range-ordered MoNbTaVW. The average threshold displacement energy in the random alloy is $44.3 \pm 0.15$ eV and slightly higher, $48.6 \pm 0.15$ eV, in the short-range-ordered alloy. Both are significantly lower than in any of the constituent pure metals. We identify the mechanisms of defect creation and find that they are mainly dependent on the masses of the recoiling and colliding elements. Low thresholds are generally found when heavy atoms (W, Ta) displace and replace the lightest atoms (V). The average threshold energies when separated by recoiling element are consequently ordered inversely according to their mass, opposite to the trend in the pure metals where W has by far the highest thresholds. However, the trend in the alloy is reversed when considering the cross sections for defect formation in electron irradiation, due to the mass-dependent recoil energies from the electrons.

Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained with near equilibrium simulations in mind. In this work, we develop ML potentials for Cu, Al and Ni using the Gaussian approximation potential (GAP) method. Specifically, we create the low-dimensional tabulated versions (tabGAP) of the potentials, which allow for two orders of magnitude higher computational efficiency than the GAPs, enabling simulations of large multi-million atomic systems. The ML potentials are trained using diverse curated databases of structures and include fixed external repulsive potentials for short-range interactions. The potentials are extensively validated and used to simulate a wide range of fundamental materials properties, such as stacking faults and threshold displacement energies. Furthermore, we use the potentials to simulate single-crystal uniaxial compressive loading in different crystal orientations with both pristine simulation cells and cells containing pre-existing defects.

When the detection threshold in semiconductor detectors is pushed 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, hindering their sensitivity for rare event signals in this energy range. Using atomistic simulations with classical thermostat and quantum thermal bath, we show that this kind of signal is consistent with energy release from long-term recombination events of complex defects that can be formed by any kind of nuclear recoil radiation events. The recombination events are shown to have a very similar exponential dependence of energy as that observed in experiments. By detailed analysis of recombination events, we show that crossing a low energy barrier ($\lesssim$ 0.1 eV) can trigger larger energy releases in an avalanche-like effect. This explains why large energy release events can occur even down to cryogenic temperatures., Comment: 13 pages, 11 figures

Quantum information technologies hold immense promise, with quantum computers poised to revolutionize problem-solving capabilities. Among the leading contenders are solid-state spin-qubits, particularly those utilizing the spin of phosphorous donors (31 P ). While significant progress has been made in enhancing quantum coherence and qubit control, challenges persist, notably in achieving precise and scalable P placement in Si substrate. This paper investigates by means of molecular dynamics the use of molecular PF2 ions for implantation, aiming to reduce placement uncertainty while maintaining detection efficiency. We examine energy transfer, molecule integrity, implantation profiles, electronic signal components, and stable damage. Among other things we find that the assumption that the molecule only breaks apart immediately due to the presence of an a-SiO2 layer on the surface of the crystal and that the intensity of the electronic signal from ion-solid interactions does not correlate necessarily with the penetration depth of P.

Polymorphism contributes to the diversity of nature, so that even materials having identical chemical compositions exhibit variations in properties because of different lattice symmetries. Thus, if stacked together into multilayers, polymorphs may work as an alternative approach to the sequential deposition of layers with different chemical compositions. However, selective polymorph crystallization during conventional thin film synthesis is not trivial; e.g. opting for step-like changes of temperature and/or pressure correlated with switching from one polymorph to another during synthesis is tricky, since it may cause degradation of the structural quality. In the present work, applying the disorder-induced ordering approach we fabricated such multilayered polymorph structures using ion beams. We show that during ion irradiation of gallium oxide, the dynamic annealing of disorder may be tuned towards self-assembling of several polymorph interfaces, consistently with theoretical modelling. Specifically, we demonstrated multilayers with two polymorph interface repetitions obtained in one ion beam assisted fabrication step. Importantly, single crystal structure of the polymorphs was maintained in between interfaces exhibiting repeatable crystallographic relationships, correlating with optical cross-sectional maps. This data paves the way for enhancing functionalities in materials with not previously thought capabilities of ion beam technology., Comment: 9 pages, 4 figure, under review, private communication for supplementary notes

Recently reported remarkably high radiation tolerance of $\gamma$/$\beta$-Ga$_2$O$_3$ double-polymorphic structure brings this ultrawide bandgap semiconductor to the frontiers of power electronics applications that are able to operate in challenging environments. Understanding the mechanism of radiation tolerance is crucial for further material modification and tailoring of the desired properties. In this study, we employ machine-learning-enhanced atomistic simulations to assess the stability of both the gallium (Ga) and oxygen (O) sublattices under various levels of damage. Our study uncovers the remarkable resilience and stability of the O-sublattice, attributing this property to the strong tendency of recovery of the O defects, especially within the stronger disordered regions. Interestingly, we observe the opposite behavior of the Ga defects that display enhanced stability in the same regions of increased disorder. Moreover, we observe that highly defective $\beta$-Ga$_2$O$_3$ is able to transform into $\gamma$-Ga$_2$O$_3$ upon annealing due to preserved lattice organization of the O-sublattice. This result clearly manifests that the ultrahigh stability of the O-sublattice provides the backbone for the exceptional radiation tolerance of the $\gamma$/$\beta$ double-polymorphic structure. These computational insights closely align with experimental observations, opening avenues for further exploration of polymorphism in Ga$_2$O$_3$ and potentially in analogous polymorphic families spanning a broad range of diverse materials of complex polymorphic nature.

Exposure of nitrogen doped diamond to high energy, heavy ions induces formation of vacancy related color centers aligned along the trajectories of the ions. Quasi 1D chains of coupled NV centers with lengths of a few tens of microns can be building blocks for quantum information processing and they provide insights into harsh radiation-matter interactions. Here, we report on color center formation in diamond (1 ppm nitrogen) with 1 GeV gold and uranium ions. Using depth-resolved photoluminescence, we observe direct formation of single vacancy related color centers (GR1 centers) along the ion tracks. Mobile vacancies can form NV-centers with native nitrogen atoms during thermal annealing. Molecular dynamics simulations indicate that both isolated vacancies and defect clusters form along ion trajectory through electronic stopping processes, leading to broad color center profiles that range from the sample surface to a depth of about 25 microns. We quantify the spin properties of NV-centers formed by swift heavy ions through optical detection of magnetic resonance (ODMR) and validate the feasibility of using swift-heavy-ion-generated NV$^{-}$ along quasi 1D chains (for isolated tracks from low fluence irradiations) or in thin sheets of coupled 1D spin chains (formed with higher ion fluences) for NV-based magnetometry and for the exploration of quasi 1D and 2D spin textures in diamond.

Vacuum arcing poses significant challenges for high-field vacuum devices, underscoring the importance of understanding it for their efficient design. A detailed description of the physical mechanisms involved in vacuum arcing is yet to be achieved, despite extensive research. In this work, we further develop the modelling of the physical processes involved in the initiation of vacuum arcing, starting from field emission and leading to plasma onset. Our model concurrently combines particle-in-cell with Monte Carlo collisions (PIC-MCC) simulations of plasma processes with finite element-based calculations of electron emission and the associated thermal effects. Including the processes of evaporation, impact ionization and direct field ionization allowed us to observe the dynamics of plasma buildup from an initially cold cathode surface. We simulated a static nanotip at various local fields to study the thresholds for thermal runaway and plasma initiation, identifying the significance of various interactions. We found that direct field ionization of neutrals has a significant effect at high fields on the order of 10 GV/m. Furthermore, we find that cathode surface interactions such as evaporation, sputtering and bombardment heating play a major role in the initiation of vacuum arcs. Consequently, the inclusion of these interactions in vacuum arc simulations is imperative., Comment: 17 pages, 14 figures

The electrical and optical properties of semiconductor materials are profoundly influenced by the atomic configurations and concentrations of intrinsic defects. This influence is particularly significant in the case of $\beta$-$\rm {Ga_2O_3}$, a vital ultrawide bandgap semiconductor characterized by highly complex intrinsic defect configurations. Despite its importance, there is a notable absence of an accurate method to recognize these defects in large-scale atomistic computational modeling. In this work, we present an effective algorithm designed explicitly for identifying various intrinsic point defects in the $\beta$-$\rm {Ga_2O_3}$ lattice. By integrating particle swarm optimization and hierarchical clustering methods, our algorithm attains a recognition accuracy exceeding 95% for discrete point defect configurations. Furthermore, we have developed an efficient technique for randomly generating diverse intrinsic defects in large-scale $\beta$-$\rm {Ga_2O_3}$ systems. This approach facilitates the construction of an extensive atomic database, crucially instrumental in validating the recognition algorithm through a substantial number of statistical analyses. Finally, the recognition algorithm is applied to a molecular dynamics simulation, accurately describing the evolution of the point defects during high-temperature annealing. Our work provides a useful tool for investigating the complex dynamical evolution of intrinsic point defects in $\beta$-$\rm {Ga_2O_3}$, and moreover, holds promise for understanding similar material systems, such as $\rm {Al_2O_3}$, $\rm {In_2O_3}$, and $\rm {Sb_2O_3}$., Comment: 11 pages, 7 figures

$\beta$ phase gallium oxide ($\beta$-$\rm Ga_2O_3$) demonstrates tremendous potential for electronics applications and offers promising prospects for integration into future space systems with the necessity of high radiation resistance. Therefore, a comprehensive understanding of the threshold displacement energy (TDE) and the radiation-induced formation of Frenkel pairs (FPs) in this material is vital but has not yet been thoroughly studied. In this work, we performed over 5,000 molecular dynamics simulations using our machine-learning potentials to determine the TDE and investigate the formation of FPs. The average TDEs for the two Ga sites, Ga1 (tetrahedral site) and Ga2 (octahedral site), are 22.9 and 20.0 eV, respectively. While the average TDEs for the three O sites are nearly uniform, ranging from 17.0 to 17.4 eV. The generated TDE maps reveal significant differences in displacement behavior between these five atomic sites. Our developed defect identification methods successfully categorize various types of FPs in this material, with more than ten types of Ga FPs being produced during our simulations. O atoms are found to form two main types of FPs and the O split interstitial site on O1 site is most common. Finally, the recombination behavior and barriers of Ga and O FPs indicate that the O FP has a higher possibility of recovery upon annealing. Our findings provide important insights into the studies of radiation damage and defects in $\rm Ga_2O_3$ and can contribute to the design and development of $\rm Ga_2O_3$-based devices

Disordering of solids typically leads to amorphization, but polymorph transitions, facilitated by favorable atomic rearrangements, may temporarily help to maintain long-range periodicity in the solid state. In far-from-equilibrium situations, such as atomic collision cascades, these rearrangements may not necessarily follow a thermodynamically gainful path, but may be kinetically limited. In this Letter, we focused on such crystallization instead of amorphization in collision cascades in gallium oxide (\ce{Ga2O3}). We determined the disorder threshold for irreversible $\beta$-to-$\gamma$ polymorph transition and explained why it results in elevating energy to that of the $\gamma$-polymorph, which exhibits the highest polymorph energy in the system below the amorphous state. Specifically, we demonstrate that upon reaching the disorder transition threshold, the \ce{Ga}-sublattice kinetically favors transitioning to the $\gamma$-like configuration, requiring significantly less migration for \ce{Ga} atoms to reach the lattice sites during post-cascade processes. As such, our data provide a consistent explanation of this remarkable phenomenon and can serve as a toolbox for predictive multi-polymorph fabrication., Comment: 6 pages, 4 figures, under review

Retention of hydrogen isotopes (protium, deuterium and tritium) in tungsten is one of the most severe issues in design of fusion power plants, since significant trapping of tritium may cause exceeding radioactivity safety limits in future reactors. Hydrogen isotopes in tungsten can be detected using the nuclear reaction analysis method in channeling mode (NRA/C). However, the information hidden within the experimental spectra is subject to interpretation. In this work, we propose the methodology to interpret the response of the experimental NRA/C spectra to the specific lattice locations of deuterium by simulations of the NRA/C spectra from atomic structures of deuterium lattice locations as obtained from the first principles calculations. We show that trapping conditions, i.e., states of local crystal structures retaining deuterium, affect the lattice locations of deuterium and the change of lattice locations can be detected by ion channeling method. By analyzing the experimental data, we are able to determine specific information on the deuterium trapping conditions, including the number of deuterium atoms trapped by one vacancy as well as the presence of impurity atoms along with deuterium in vacancies.

The focused ion beam (FIB) is a powerful tool for the fabrication, modification and characterization of materials down to the nanoscale. Starting with the gallium FIB, which was originally intended for photomask repair in the semiconductor industry, there are now many different types of FIB that are commercially available. These instruments use a range of ion species and are applied broadly in materials science, physics, chemistry, biology, medicine, and even archaeology. The goal of this roadmap is to provide an overview of FIB instrumentation, theory, techniques and applications. By viewing FIB developments through the lens of the various research communities, we aim to identify future pathways for ion source and instrumentation development as well as emerging applications, and the scope for improved understanding of the complex interplay of ion-solid interactions. We intend to provide a guide for all scientists in the field that identifies common research interests and will support future fruitful interactions connecting tool development, experiment and theory. While a comprehensive overview of the field is sought, it is not possible to cover all research related to FIB technologies in detail. We give examples of specific projects within the broader context, referencing original works and previous review articles throughout., Comment: This publication is based upon work from the COST Action FIT4NANO CA19140, supported by COST (European Cooperation in Science and Technology) https://www.cost.eu/. Financial support from COST Action CA19140 is acknowledged http://www.fit4nano.eu/ Version 3 has many text and language edits as well as layout tuning but no substantial new content

Radiation tolerance is determined as the ability of crystalline materials to withstand the accumulation of the radiation induced disorder. Nevertheless, for sufficiently high fluences, in all by far known semiconductors it ends up with either very high disorder levels or amorphization. Here we show that gamma/beta double polymorph Ga2O3 structures exhibit remarkably high radiation tolerance. Specifically, for room temperature experiments, they tolerate a disorder equivalent to hundreds of displacements per atom, without severe degradations of crystallinity; in comparison with, e.g., Si amorphizable already with the lattice atoms displaced just once. We explain this behavior by an interesting combination of the Ga- and O- sublattice properties in gamma-Ga2O3. In particular, O-sublattice exhibits a strong recrystallization trend to recover the face-centered-cubic stacking despite the stronger displacement of O atoms compared to Ga during the active periods of cascades. Notably, we also explained the origin of the beta-to-gamma Ga2O3 transformation, as a function of the increased disorder in beta-Ga2O3 and studied the phenomena as a function of the chemical nature of the implanted atoms. As a result, we conclude that gamma/beta double polymorph Ga2O3 structures, in terms of their radiation tolerance properties, benchmark a class of universal radiation tolerant semiconductors.

$\mathrm{Ga}_{2}\mathrm{O}_{3}$ 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 $\beta$/$\kappa$/$\alpha$/$\delta$/$\gamma$ 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., Comment: 13 pages, 7 figures

The Gaussian approximation potential (GAP) is an accurate machine-learning interatomic potential that was recently extended to include the description of radiation effects. In this study, we seek to validate a faster version of GAP, known as tabulated GAP (tabGAP), by modelling primary radiation damage in 50-50 W-Mo alloys and pure W using classical molecular dynamics. We find that W-Mo exhibits a similar number of surviving defects as in pure W. We also observe W-Mo to possess both more efficient recombination of defects produced during the initial phase of the cascades, and in some cases, unlike pure W, recombination of all defects after the cascades cooled down. Furthermore, we observe that the tabGAP is two orders of magnitude faster than GAP, but produces a comparable number of surviving defects and cluster sizes. A small difference is noted in the fraction of interstitials that are bound into clusters.

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.

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., Comment: 22 pages, 7 figures, supplementary material

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.

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.

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 $\mu$ and polarizability $\alpha$) 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 $\mu$ and $\alpha$ of a W adatom moving on a W {110} surface, where additional adatoms are present in its vicinity. We analyze the dependence of $\mu$ and $\alpha$ and hence the migration barriers under electric fields on the local atomic environments (LAE) of an adatom. We find that the LAE significantly affects $\mu$ and $\alpha$ 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., Comment: 13 pages including references and appendix, 9 figures

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., Comment: 10 pages, 6 figures

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.

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., Comment: 10 pages, 7 figures, under review

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., Comment: 14 pages, 8 figures

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.

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., Comment: 14 pages, 11 figures. Submitted to Physical Review Accelerators and Beams

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.

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.

We propose to use the defect creation energy loss in commonly used high energy physics solid state detectors as a tool to statistically identify dark matter signal from background. We simulate the energy loss in the process of defect creation using density functional theory and molecular dynamics methods and calculate the corresponding expected dark matter spectra. We show that in phonon-mediated solid state detectors, the energy loss due to defect creation convolved with the expected dark matter interaction signal results in a significant change in the expected spectra for common detector materials. With recent progress towards $\sim$10 eV threshold low-mass dark matter searches, this variation in expected dark matter spectrum can be used as a direct signature of dark matter interactions with atomic nuclei.

This review concentrates on the specific properties and characteristics of damage structures generated with high-energy ions in the electronic energy loss regime. Irradiation experiments with so-called swift heavy ions (SHI) find applications in many different fields, with examples presented in ion-track nanotechnology, radiation hardness analysis of functional materials, and laboratory tests of cosmic radiation. The basics of the SHI-solid interaction are described with special attention to processes in the electronic subsystem. The broad spectrum of damage phenomena is exemplified for various materials and material classes, along with a description of typical characterization techniques. The review also presents state-of-the-art modeling efforts that try to account for the complexity of the coupled processes of the electronic and atomic subsystems. Finally, the relevance of SHI phenomena for effects induced by fission fragments in nuclear fuels and how this knowledge can be applied to better estimate damage risks in nuclear materials is discussed.

Understanding the microscopic phenomena behind vacuum arc ignition and generation is crucial for being able to control the breakdown rate, thus improving the effectiveness of many high-voltage applications where frequent breakdowns limit the operation. In this work, statistical properties of various aspects of breakdown, such as the number of pulses between breakdowns, breakdown locations and crater sizes are studied independently with almost identical Pulsed DC Systems at the University of Helsinki and in CERN. In high-gradient experiments, copper electrodes with parallel plate capacitor geometry, undergo thousands of breakdowns. The results support the classification of the events into primary and secondary breakdowns, based on the distance and number of pulses between two breakdowns. Primary events follow a power law on the log--log scale with the slope $\alpha \approx 1.33$, while the secondaries are highly dependent on the pulsing parameters., Comment: 11 pages, 13 figures

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.

We have calculated the migration barriers for surface diffusion on Tungsten. Our results form a self-sufficient parameterization for Kinetic Monte Carlo simulations of arbitrarily rough atomic tungsten surfaces, as well as nanostructures such as nanotips and nanoclusters. The parameterization includes first- and second-nearest neighbour atom jump processes, as well as a third-nearest neighbour exchange process. The migration energy barriers of all processes are calculated with the Nudged Elastic Band method. The same attempt frequency for all processes is found sufficient and the value is fitted to Molecular Dynamics simulations. The model is validated by correctly simulating with Kinetic Monte Carlo the energetically favourable W nanocluster shapes, in good agreement with Molecular Dynamics simulations.

We introduce a machine-learning interatomic potential for tungsten using the Gaussian Approximation Potential framework. We specifically focus on properties relevant for simulations of radiation-induced collision cascades and the damage they produce, including a realistic repulsive potential for the short-range many-body cascade dynamics and a good description of the liquid phase. Furthermore, the potential accurately reproduces surface properties and the energetics of vacancy and self-interstitial clusters, which have been long-standing deficiencies of existing potentials. The potential enables molecular dynamics simulations of radiation damage in tungsten with unprecedented accuracy.

In this work we combine density functional theory and quantum transport calculations to study the influence of atomic--scale defects on the work function and field emission characteristics of metal surfaces. We develop a general methodology for the calculation of the field emitted current density from nano-featured surfaces, which is then used to study specific defects on a Cu(111) surface. Our results show that the inclusion of a defect can significantly locally enhance the field emitted current density. However, this increase is attributed solely to the decrease of the work function due to the defect, with the effective field enhancement being minute. Finally, the Fowler--Nordheim equation is found to be valid when the modified value for the work function is used, with only an approximately constant factor separating the computed currents from those predicted by the Fowler--Nordheim equation., Comment: 12 pages, 9 figures

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., Comment: 15 pages, 12 figures

Conditioning of a metal structure in a high-voltage system is the progressive development of resistance to vacuum arcing over the operational life of the system. This is, for instance, seen during the initial operation of radio frequency (rf) cavities in particle accelerators. It is a relevant topic for any technology where breakdown limits performance, and where conditioning continues for a significant duration of system runtime. Projected future linear accelerators require structures with accelerating gradients of up to 100 MV/m. Currently, this performance level is only achievable after a multi-month conditioning period. In this work, a pulsed DC system applying voltage pulses over parallel disk electrodes was used to study the conditioning process, with the objective of obtaining insight into its underlying mechanics, and ultimately, to find ways to shorten the conditioning process. Two kinds of copper electrodes were tested: As-prepared machine-turned electrodes ("hard" copper), and electrodes that additionally had been subjected to high temperature treatments ("soft" copper). The conditioning behaviour of the soft electrodes was found to be similar to that of comparably treated accelerating structures, indicating a similar conditioning process. The hard electrodes reached the same ultimate performance as the soft electrodes much faster, with a difference of more than an order of magnitude in the number of applied voltage pulses. Two distinctly different distributions of breakdown locations were observed on the two types of electrodes. Considered together, our results support the crystal structure dislocation theory of breakdown, and suggest that the conditioning of copper in high field systems such as rf accelerating structures is dominated by material hardening.

Sufficiently high voltage applied between two metal electrodes, even in ultra high vacuum conditions, results in an inevitable discharge that lights up the entire gap, opening a conductive channel through the vacuum and parasitically consuming large amounts of energy. Despite many efforts to understand the processes that lead to this phenomenon, known as vacuum arc, there is still no consensus regarding the role of each electrode in the evolution of such a momentous process as lightning. Employing a high-speed camera, we capture the entire lightning process step-by-step with a nanosecond resolution and find which of the two electrodes holds the main responsibility for igniting the arc. The light that gradually expands from the positively charged electrode (anode), often is assumed to play the main role in the formation of a vacuum arc. However, both the nanosecond-resolution images of vacuum arc evolution and the corresponding theoretical calculations agree that the conductive channel between the electrodes is built in the form of cathodic plasma long before any significant activity develops in the anode. We show evidently that the anode illumination is weaker and plays a minor role in igniting and maintaining the conductive channel.

Combining classical electrodynamics and density functional theory (DFT) calculations, we develop a general and rigorous theoretical framework that describes the energetics of metal surfaces under high electric fields. We show that the behavior of a surface atom in the presence of an electric field can be described by the polarization characteristics of the permanent and field-induced charges in its vicinity. We use DFT calculations for the case of a W adatom on a W{110} surface to confirm the predictions of our theory and quantify its system-specific parameters. Our quantitative predictions for the diffusion of W-on-W{110} under field are in good agreement with experimental measurements. This work is a crucial step towards developing atomistic computational models of such systems for long-term simulations., Comment: 9 pages, 5 figures, 2 tables