arXiv subjects

Kai Nordlund

Publications and source records attributed to Kai Nordlund.

At least 37 records · Page 2Linked to original sources

arXiv2022

Anisotropic ionization threshold and directional sensitivity in solid state DM detectors

The threshold displacement energy for nuclear recoils depends strongly on the direction of the recoiling nucleus with respect to the crystal lattice. Assuming that similar dependence holds for the ionization threshold for low energy nuclear recoils, we explore the consequences of the resulting directional dependence of the observable event rate in ionization detectors. For low mass dark matter, this effect leads to a daily modulation in the event rate. We discuss how this effect can be utilized to separate the DM signal from the solar neutrino background and how the structure of the modulation signal can be used to identify the type of the DM-nucleon coupling.

hep-ph↗

arXiv2022

Energy loss in low energy nuclear recoils in dark matter detector materials

Recent progress in phonon-mediated detectors with eV-scale nuclear recoil energy sensitivity requires an understanding of the effect of the crystalline defects on the energy spectrum expected from dark matter or neutrino coherent scattering. We have performed molecular dynamics simulations to determine the amount of energy stored in the lattice defects as a function of the recoil direction and energy. This energy can not be observed in the phonon measurement, thus affecting the observed energy spectrum compared to the underlying true recoil energy spectrum. We describe this effect for multiple commonly used detector materials and demonstrate how the predicted energy spectrum from dark matter scattering is modified.

hep-ph↗

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

Revealing hidden defects through stored energy measurements of radiation damage

With full knowledge of a material's atomistic structure, it is possible to predict any macroscopic property of interest. In practice, this is hindered by limitations of the chosen characterisation techniques. For example, electron microscopy is unable to detect the smallest and most numerous defects in irradiated materials. Instead of spatial characterisation, we propose to detect and quantify defects through their excess energy. Differential scanning calorimetry (DSC) of irradiated Ti measures defect densities 5 times greater than those determined using transmission electron microscopy (TEM). Our experiments also reveal two energetically-distinct processes where the established annealing model predicts one. Molecular dynamics (MD) simulations discover the defects responsible and inform a new mechanism for the recovery of irradiation-induced defects. The combination of annealing experiments and simulations can reveal defects hidden to other characterisation techniques, and has the potential to uncover new mechanisms behind the evolution of defects in materials.

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↗

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

Parameter-free quantitative simulation of high dose microstructure and hydrogen retention in ion-irradiated tungsten

Hydrogen isotopes are retained in materials for fusion power applications, changing both hydrogen embrittlement and tritium inventory as the microstructure undergoes irradiation damage. But modelling of highly damaged materials - exposed to over 0.1 displacements per atom (dpa) - where asymptotic saturation is observed, for example tungsten facing the plasma in a fusion tokamak reactor, is difficult because a highly damaged microstructure cannot be treated as weakly interacting isolated defect traps. In this paper we develop computational techniques to find the defect content in highly irradiated materials without adjustable parameters. First we show how to generate converged high dose (>1 dpa) microstructures using a combination of the creation-relaxation algorithm and molecular dynamics simulations of collision cascades. Then we make robust estimates of point defects and void regions with simple developments of the Wigner-Seitz decomposition of lattice sites. We use our estimates of the void surface area to predict the deuterium retention capacity of tungsten as a function of dose. This is then compared to 3He nuclear reaction analysis (NRA) measurements of tungsten samples self-irradiated at 290 K to different damage doses and exposed to deuterium plasma at low energy at 370 K. We show that our simulated microstructures give an excellent match to the experimental data, with both model and experiment showing 1.5-2.0 at.% deuterium retained in tungsten in the limit of high dose.

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

Solar neutrinos and dark matter detection with diurnal modulation

We investigate the diurnal modulation of the event rate for dark matter scattering on solid targets arising from the directionally dependent defect creation threshold energy. In particular, we quantify how this effect would help in separating dark matter signal from the neutrino background. We perform a benchmark analysis for a germanium detector and compute how the reach of the experiment is affected by including the timing information of the scattering events. We observe that for light dark matter just above the detection threshold the magnitude of the annual modulation is enhanced. In this mass range using either the annual or diurnal modulation information provides a similar gain in the reach of the experiment, while the additional reach from using both effects remains modest. Furthermore, we demonstrate that if the background contains a feature exhibiting an annual modulation similar to the one observed by DAMA experiment, the diurnal modulation provides for an additional handle to separate dark matter signal from the background.

hep-ph↗

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

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

Crystal Defects: A Portal To Dark Matter Detection

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.

physics.ins-det↗

arXiv2019

Machine-learning interatomic potential for radiation damage and defects in tungsten

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.

physics.comp-ph↗

arXiv2019

Velocity Dependent Dark Matter Interactions in Single-Electron Resolution Semiconductor Detectors with Directional Sensitivity

We investigate the velocity and recoil momentum dependence of dark matter interactions with ordinary matter. In particular we focus on the single-electron resolution semiconductor detectors, which allow experimental assessment of sub-GeV dark matter masses. We find that, within a specific mass range depending on the detector material, the dark matter interactions result in a signal characterized by daily modulation. Furthermore, we find that the detailed structure of this modulation is sensitive to the velocity and momentum dependence of dark matter interactions. We identify the optimal mass range for the prevalence of these effects.

hep-ph↗

arXiv2018

Directional Sensitivity In Light-Mass Dark Matter Searches With Single-Electron Resolution Ionization Detectors

We propose a method using solid state detectors with directional sensitivity to dark matter interactions to detect low-mass Weakly Interacting Massive Particles (WIMPs) originating from galactic sources. In spite of a large body of literature for high-mass WIMP detectors with directional sensitivity, no available technique exists to cover WIMPs in the mass range <1 GeV. We argue that single-electron resolution semiconductor detectors allow for directional sensitivity once properly calibrated. We examine commonly used semiconductor material response to these low-mass WIMP interactions.

physics.ins-det↗

arXiv2017

Cu self-sputtering MD simulations for 0.1-5 keV ions at elevated temperatures

Self-sputtering of copper under high electric fields is considered to contribute to plasma buildup during a vacuum breakdown event frequently observed near metal surfaces, even in ultra high vacuum condition in different electric devices. In this study, by means of molecular dynamics simulations, we analyze the effect of surface temperature and morphology on the yield of self-sputtering of copper with ion energies of 0.1-5 keV. We analyze all three low-index surfaces of Cu, {100}, {110} and {111}, held at different temperatures, 300 K, 500 K and 1200 K. The surface roughness relief is studied by either varying the angle of incidence on flat surfaces, or by using arbitrary roughened surfaces, which result in a more natural distribution of surface relief variations. Our simulations provide detailed characterization of copper self-sputtering with respect to different material temperatures, crystallographic orientations, surface roughness, energies, and angles of ion incidence.

cond-mat.mtrl-sci↗

arXiv2017

Local segregation versus irradiation effects in high-entropy alloys: Steady-state conditions in a driven system

We study order transitions and defect formation in a model high-entropy alloy (CuNiCoFe) under ion irradiation by means of molecular dynamics simulations. Using a hybrid Monte-Carlo/molecular dynamics scheme a model alloy is generated which is thermodynamically stabilized by configurational entropy at elevated temperatures, but partly decomposes at lower temperatures by copper precipation. Both the high-entropy and the multiphase sample are then subjected to simulated particle irradiation. The damage accumulation is analyzed and compared to an elemental Ni reference system. The results reveal that the high-entropy alloy---independent of the initial configuration---installs a certain fraction of short-range order even under particle irradiation. Moreover, the results provide evidence that defect accumulation is reduced in the high-entropy alloy. This is because the reduced mobility of point defects leads to a steady state of defect creation and annihilation. The lattice defects generated by irradiation are shown to act as sinks for Cu segregation.

cond-mat.mtrl-sci↗

arXiv2016

Interatomic Fe-H potential for irradiation and embrittlement simulations

The behavior of hydrogen in iron and iron alloys is of interest in many fields of physics and materials science. To enable large-scale molecular dynamics simulations of systems with Fe-H interactions, we develop, based on density-functional theory calculations, an interatomic Fe-H potential in the Tersoff-Brenner formalism. The obtained analytical potential is suitable for simulations of H in bulk Fe as well as for modeling small FeH molecules, and it can be directly combined with our previously constructed potential for the stainless steel Fe-Cr-C system. This will allow simulations of, e.g., hydrocarbon molecule chemistry on steel surfaces. In the current work, we apply the potential to simulating hydrogen-induced embrittlement in monocrystalline bulk Fe and in an Fe bicrystal with a grain boundary. In both cases, hydrogen is found to soften the material.

cond-mat.mtrl-sci↗