arXiv subjects

Jesper Byggmästar

Publications and source records attributed to Jesper Byggmästar.

At least 19 recordsLinked to original sources

arXiv2026

Comprehensive study of massively overlapping cascades in common elemental metals

Massively overlapping cascades simulations were carried out in 21 elemental metals: Be, Al, Ti, V, Cr, Fe, Co, Ni, Cu, Zr, Nb, Mo, Rh, Pd, Ag, Hf, Ta, W, Pt, Au and Pb. These elements have simple FCC, BCC and HCP lattice structures. For each element, 2000 cumulative 5 keV cascades were simulated using molecular dynamics. The overlapping cascades were simulated using various classical analytical interatomic potentials as well as with machine-learning interatomic potentials for many of the elements. The general conclusion is that results from massively overlapping cascades simulations are very sensitive to the choice of interatomic potential across the periodic table. Furthermore, we see that FCC metals all form stacking fault tetrahedra due to irradiation, and also typically include interstitial type Shockley partial and Frank type dislocations. In BCC materials typically form interstitial 1/2$\langle$1 1 1$\rangle$ dislocations, but vacancy type 1/2$\langle$1 1 1$\rangle$ dislocations were also observed in V and Nb. HCP materials tend to form complex dislocation structures mainly consisting of a-type dislocations that can be of both vacancy or interstitial type. Correlations between saturated defect concentrations and fundamental underlying material properties are explored.

cond-mat.mtrl-sci↗

arXiv2026

Machine-learned interatomic potential for sputtering of tungsten-boron surfaces

Boronization, where boron is deposited onto tungsten surfaces, is a key technique to reduce plasma contamination, such as oxygen in Tokamak fusion reactors. The exact interaction between the boron atoms and the tungsten surface, and the effect of the harsh environment on these surfaces are however not fully understood, partially due to the lack of accurate atomistic simulations and interatomic potentials. Here, we develop a machine-learned interatomic potential for sputtering studies of W-B structures and deposition of boron onto tungsten surfaces. The machine-learned potential is trained to density functional theory data and allows accurate large-scale molecular dynamics simulations. Our aim is to understand how boron behaves when deposited on tungsten and how tungsten and boron are sputtered under irradiation. We observe that both the surface configuration/orientation and the surface composition affect the sputtering, and that depositing boron onto tungsten surfaces produces a dense boron layer. The developed potential shows good accuracy for both surface and bulk properties and can be used for simulations of mixed W and B systems.

cond-mat.mtrl-sci↗

arXiv2026

Machine-learned interatomic potential for titanium carbide MXenes: Application to ion irradiation simulations

A computationally efficient and accurate machine-learned (ML) interatomic potential is developed for bare Ti$_{n+1}$C$_n$ MXenes. With a diverse set of structures computed with density functional theory, the trained ML potential demonstrates good accuracy and robustness to a wide range of bond distances and environments, making it a useful tool for molecular dynamics simulations of MXenes subjected to mechanical load or irradiation. The ML potential is applied to simulations of light and heavy ion irradiation, gathering insight into the statistics and probabilities of sputtering, reflection, defect creation, and implantation into bare Ti$_{n+1}$C$_n$ MXene sheets. The results provide guidelines for defect engineering of MXenes through ion irradiation and implantation. Additionally, the ML potential development provides a landmark recipe for enabling machine-learning-driven atomistic simulations of other MXenes.

cond-mat.mtrl-sci↗

arXiv2026

Four regimes of primary radiation damage in tungsten

We observe for the first time in silico the transition to a linear regime in the primary damage production in tungsten. As the critical plasma-facing material in fusion reactors, radiation damage in tungsten has been studied extensively in experiments and simulations. Irradiation experiments routinely produce recoils in the MeV range while full atomistic modelling has been limited to a few hundred keV. Here we bridge these scales with extremely large-scale and accurate machine-learning-driven molecular dynamics simulations with recoil energies up to 2 MeV in systems up to one billion atoms. We reveal four regimes of primary damage as a function of damage energy, with a transition to a high-energy regime that deviates from all previous models. Curiously, the start of the high-energy regime coincides with the highest possible recoil energy to tungsten atoms from fusion-emitted neutrons (300 keV).

cond-mat.mtrl-sci↗

arXiv2026

Transformation-mediated twinning governs plasticity in body-centered cubic nanocrystals under extreme loading

Plasticity in body-centered cubic (BCC) nanocrystals is often associated with twin nucleation phenomena under extreme loading conditions. Here, we reveal unconventional twinning pathways that operate at the intersection of crystal plasticity and structural phase transitions. We show that the classical shear-driven twinning mode becomes progressively suppressed with increasing pressure, giving rise to transformation-mediated twinning pathways involving transient HCP or FCC phases. In BCC Fe, Ta, and Nb nanocrystals of moderate elastic stiffness, plasticity is consistently initiated by an elastic instability that triggers a dual-shuffle process mediated by stable or metastable hexagonal closed-packed (HCP) phases. This pathway operates independently of the characteristic {112} twin boundary planes and is driven by compression, challenging the conceptual paradigm for metal plasticity in which plastic deformation arises from shear stresses resolved on specific planes. By contrast, in the archetypal elastically stiffer BCC Mo and W nanocrystals, plastic deformation proceeds via two alternative twinning pathways associated with shear-driven elastic instabilities mediated by highly-distorted face-centered cubic (FCC) phases. Comprehensive analyses of the energy landscapes to the competing nanoscale twinning modes provide mechanistic insight into their activation, establishing a unified framework for transformation-mediated twinning in BCC nanocrystals across a broad range of loading conditions.

cond-mat.mtrl-sci↗

arXiv2026

Nine-element machine-learned interatomic potentials for multiphase refractory alloys

New refractory alloys are being continuously designed and characterised for applications requiring good high-temperature mechanical properties and stability. Computational design from atomistic simulations is limited by interatomic potentials missing key elements, being too inaccurate, or computationally too slow for large-scale simulations. Here we present development of a refractory alloy database and two computationally efficient and general-purpose machine-learned potentials (tabGAP and NEP). We also design a cross-sampling strategy for effective sampling of training data using predictions from two potentials with completely different underlying architecture. The potentials support arbitrary alloy compositions of elements in groups four to six in the periodic table (Ti, Zr, Hf, V, Nb, Ta, Cr, Mo, W). The database is diverse yet multitargeted to enable simulations of refractory metals and alloys across different pure-metal, solid-solution, intermetallic, and glassy phases. We demonstrate the usefulness of the potentials by reproducing known pressure-, temperature-, and solute-induced phase transitions, grain boundary segregation, and simulations of radiation damage in the WTaCrVHf metallic glass.

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

Recent Advances in Metallic Glasses

This paper reviews recent advances in the field of metallic glasses, focusing on the development of novel experimental techniques and in silico models. We discuss progress in experimental characterization, additive manufacturing, multiscale modeling approaches, and the growing role of machine learning in understanding and designing these complex materials. On the experimental side, we highlight measurements of thermophysical properties of supercooled liquids via fast chip calorimetry and enhancements in mechanical properties through rejuvenation treatments. This work underscores the crucial role of short-range order and medium-range order in controlling metallic glass mechanical properties. Recent progress in structural probes allows in situ observations of deformation mechanisms, positioning the field well to further advance our understanding of mechanical properties. Additive manufacturing of metallic glasses is discussed as one encouraging new manufacturing route for metallic glasses. We examine laser powder-bed fusion process physics and the central trade-off between amorphicity and densification, including heat affected zone devitrification and defects formation, together with emerging mitigation strategies and applications. On the theoretical and simulation side, we review advances in nanoscale, mesoscale, and continuum modeling of metallic glasses that have led to promising approaches by which multiscale schemes can incorporate data sourced from atomic-scale simulations. These efforts have helped to elucidate the connection between the glass structure and mechanical and rheological responses. We also cover the development of machine learning interatomic potentials for metallic glasses, along with machine learning driven prediction of glass forming ability and inverse design methods. Finally, challenges and directions for future research are presented and discussed.

cond-mat.mtrl-sci↗

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

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↗

arXiv2025

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation

Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasses, their disordered atomic structure makes the development of potentials particularly challenging, resulting in the scarcity of chemistry-specific parametrizations for this important class of materials. We address this gap by introducing an efficient methodology to design machine learning interatomic potentials (MLIPs), benchmarked on the CuZrAl system. Using a Lennard-Jones surrogate model, swap-Monte Carlo sampling, and single-point Density Functional Theory (DFT) corrections, we capture amorphous structures spanning 14 decades of supercooling. These representative configurations, competing with the experimental time scale, enable robust model training across diverse states, while minimizing the need for extensive DFT datasets. The resulting MLIP matches the experimental data and predictions of the classical embedded atom method (EAM) for structural, dynamical, energetic, and mechanical properties. This approach offers a scalable path to develop accurate MLIPs for complex metallic glasses, including emerging multi-component and high-entropy systems.

cond-mat.mtrl-sci↗

arXiv2025

Segregation, ordering, and precipitation in WTaV-based concentrated refractory alloys

Tungsten-based low-activation high-entropy alloys are possible candidates for next-generation fusion reactors due to their exceptional tolerance to irradiation, thermal loads, and stress. We develop an accurate and efficient machine-learned interatomic potential for the W-Ta-Cr-V system and use it in hybrid Monte Carlo molecular dynamics simulations of ordering and segregation to all common types of defects in WTaCrV. The predictions are compared to atom probe tomography analysis of segregation and precipitation in WTaCrV thin films. By also considering two other alloys, WTaV and MoNbTaVW, we are able to draw general conclusions about preferred segregation in refractory alloys and the reasons behind it, guiding future alloy design and elucidating experimental observations. We show that the experimentally observed CrV precipitates in WTaCrV form semicoherent bcc-to-bcc interfaces with the surrounding matrix, as coherent precipitates are not thermodynamically stable due to excessive lattice mismatch. The predictions from simulations align well with our atom probe tomography analysis as well as previous experimental observations.

cond-mat.mtrl-sci↗

arXiv2024

Design Kinetic Parameters for Improved Resilience of Materials under Irradiation

High entropy alloys (HEAs) have captured much attention in recent years due to their conceivably improved radiation resistance compared to pure metals and traditional alloys. However, among HEAs, there are millions of design possibilities considering all potential compositions. In this study, we develop criteria to design HEAs with improved radiation resilience taking into consideration defect properties to promote interstitial-vacancy recombination. First, we conduct rate theory calculations on defects followed by molecular dynamics (MD) simulations on pure W and W-based multicomponent concentrated alloys. It is found that when the diffusion coefficients for single vacancies and interstitials become similar and the effective migration energies of defects is minimum (maximum diffusivities), defect recombination becomes optimal, and the concentration of defects is significantly reduced. This is supported by MD simulations indicating improved radiation resistance of V- and Cr-based alloys, which satisfy the above-stated criteria. Furthermore, experimental observations also reinforce the proposed approach. This study sheds light on the design criteria for improved radiation resistance and helps material selection without the need of extensive experimental work.

cond-mat.mtrl-sci↗

arXiv2024

Threshold displacement energies in refractory high-entropy alloys

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.

cond-mat.mtrl-sci↗

arXiv2024

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni

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.

cond-mat.mtrl-sci↗

arXiv2024

Large-scale atomistic study of plasticity in amorphous gallium oxide with a machine-learning potential

Compared to the widely investigated crystalline polymorphs of gallium oxide (Ga2O3), knowledge about its amorphous state is still limited. With the help of a machine-learning interatomic potential, we conducted large-scale atomistic simulations to investigate the glass transition and mechanical behavior of amorphous Ga2O3 (a-Ga2O3). During the quenching simulations, amorphization of gallium oxide melt is observed at ultrahigh cooling rates, including a distinct glass transition. The final densities at room temperature have up to 4% variance compared to experiments. The glass transition temperature is evaluated to range from 1234 K to 1348 K at different cooling rates. Structural analysis of the amorphous structure shows evident similarities in structural properties between a-Ga2O3 and amorphous alumina (a-Al2O3), such as radial distribution function, coordination distribution, and bond angle distribution. An amorphous gallium oxide structure that contains approximately one million atoms is prepared for the tension simulation. A highly plastic behavior is observed at room temperature in the tension simulations, comparable to amorphous alumina. With quantitative characterization methods, we show that a-Ga2O3 can possibly has a higher nucleation rate of localized plastic strain events compared to a-Al2O3, which can increase the material's resistance to shear banding formation during deformation.

cond-mat.mtrl-sci↗

arXiv2024

Ultrahigh Stability of O-Sublattice in $β$-Ga$_2$O$_3$

Recently reported remarkably high radiation tolerance of $γ$/$β$-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 $β$-Ga$_2$O$_3$ is able to transform into $γ$-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 $γ$/$β$ 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.

cond-mat.mtrl-sci↗

arXiv2024

Threshold displacement energy map of Frenkel pair generation in $\rm Ga_2O_3$ from machine-learning-driven molecular dynamics simulations

$β$ phase gallium oxide ($β$-$\rm Ga_2O_3$) 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

cond-mat.mtrl-sci↗