arXiv subjects

Andrea E. Sand

Publications and source records attributed to Andrea E. Sand.

13 recordsLinked to original sources

arXiv2026

Validation of an Ab Initio-Informed Electronic Stopping Model for Large-Scale Atomistic Simulations

Accurately modeling nonadiabatic electronic energy dissipation in atomistic simulations of radiation damage remains a key challenge. Existing models often rely on arbitrary thresholds and neglect trajectory-dependent, band-structure, and nonlinear effects. A model developed by Tamm \textit{et al.} (\href{https://doi.org/10.1103/PhysRevLett.120.185501}{doi:10.1103/PhysRevLett.120.185501}) offers a nonempirical description of electronic dissipation for atomistic simulations, but has not been directly benchmarked in the electronic stopping regime. We present the first such benchmark via the comparison of atomistic simulations with experimental ion transmission experiments. The model accurately captures the magnitude and trajectory-dependence of the predicted energy losses. These results support the reliability of the model for predictive large-scale atomistic simulations of radiation cascades.

cond-mat.mtrl-sci↗

arXiv2026

Ab initio informed electronic stopping models for light ion propagation in metals

Understanding ion-matter interactions at the atomistic level is key to advancing materials for the semiconductor industry, space systems, and nuclear fusion technologies. However, most atomistic frameworks still rely on simplified descriptions of how ions transfer energy to the electronic subsystem, overlooking the sensitivity of this process to the actual ion path. Existing electron-ion interaction models, such as the tensorial unified two-temperature model, were developed to study self-irradiation scenarios, but their suitability for light-ion irradiation remains unexplored. Here, we propose that for light projectiles, stepping back from the tensorial formulation toward a simpler, local model of electronic stopping provides a more efficient and physically transparent trajectory-dependent description. We parameterize and validate both models for hydrogen and helium in tungsten using ab initio electronic stopping data and large-scale ion range simulations, benchmarked against existing experimental data. This provides a consistent framework for including nonadiabatic electronic stopping in atomistic simulations of light-ion energy dissipation.

cond-mat.mtrl-sci↗

arXiv2026

Impact of Electronic Energy Dissipation on Primary Radiation Damage Formation in Silicon

In this work, we investigate the role of ion-electron coupling in simulations of radiation damage formation in silicon using molecular dynamics simulations within a two-temperature model. We explore predictions of a threshold-free approach to the coupling that accounts for both the electronic stopping and electron-phonon coupling using a local electron density-based formalism. We compare two different coupling functions across a range of primary knock-on atom energies using two interatomic potentials. Our results demonstrate that the functional form of the ion-electron coupling plays a critical role in determining defect production efficiency, clustering, and recombination, and must therefore be carefully considered for accurate modeling of radiation damage formation. Furthermore, we find that the impact of the coupling in particular on the recombination of defects during the cooling phase of the cascade depends on the choice of interatomic potential, emphasizing the importance of physically grounded descriptions for both electronic effects and atom-atom interactions for reliable radiation damage predictions.

cond-mat.mtrl-sci↗

arXiv2025

Trajectory-Dependent Electronic Energy Losses in Ion Range Simulations

The energy losses of energetic ions in materials depend on both nuclear and electronic interactions. In channeling geometries, the stopping effect of these interactions can be highly reduced, resulting in deeper ion penetration. Comprehensive, trajectory-dependent models for ion-material interactions are therefore crucial for the accurate prediction of ion range profiles. We present the implementation of a recent electron density-dependent energy-loss model in the efficient molecular dynamics-based MDRANGE code. The model captures \textit{ab initio} electron dynamics using a parametrized ion energy loss function, based on calculations for explicit trajectories using real-time time-dependent density functional theory. We demonstrate the efficient simulation of trajectory-dependent ion range profiles with this comprehensive model for electronic energy losses. Our results indicate that accurate trajectory-dependent ion range profiles can be simulated using well-fitted parametrizations of this model. This method offers a unique tool for validation of the fitted energy-loss functions using energetic ion ranges, which can be measured experimentally but are beyond the capability of full MD simulations due to the computational expense.

cond-mat.mtrl-sci↗

arXiv2025

SiC-TGAP: A machine learning interatomic potential for radiation damage simulations in 3C-SiC

Silicon carbide (SiC) has long been a subject of study for its application in harsh environments. Existing empirical interatomic potentials for 3C-SiC show significant discrepancies in predicting the properties that are crucial in describing the evolution of defects generated in collision cascades. We present a Gaussian approximation potential model for 3C-SiC (TGAP) trained by two-body and the turboSOAP many-body descriptors. The dataset covers crystalline, liquid and amorphous phases. To accurately capture defect dynamics, twenty-one defect types have been included in the dataset. TGAP captures the experimentally observed decomposition of carbon atoms in the liquid phase at atmospheric pressure, while also accurately reproducing the radial distribution function of the high-temperature homogeneous liquid phase across a range of densities. Moreover, it predicts the melting point in very good agreement with density functional theory and experiments. The potential is equipped with the Nordlund-Lehtola-Hobler repulsive potential to capture the high repulsion of recoils in the collision cascades. TGAP provides an accurate tool for atomistic simulation of radiation damage in cubic SiC.

cond-mat.mtrl-sci↗

arXiv2025

Improved capabilities of the TurboGAP code for radiation induced cascade simulations: an illustration with silicon

TurboGAP is a software package designed for efficient molecular dynamics simulations using Gaussian Approximation Potential (GAP) machine-learning interatomic potentials (MLIP). In this work, we enhance the capabilities of TurboGAP for radiation damage simulations by implementing a two-temperature molecular dynamics model, based on electron density-dependent coupling of electronic and atomic subsystems. Additionally, we implement adaptive calculation of the timestep and grouping of atoms for cell-border cooling. Our implementation incorporates electronic stopping power either through a traditional friction-based model or a more realistic first-principles-derived model. By combining the computational efficiency of TurboGAP with the accuracy of GAP MLIP, we perform cascade simulations in silicon with primary knock-on atom (PKA) energies up to 10 keV. Our simulations scale to systems containing up to 1 million atoms. We study the generation and clustering of radiation-induced defects. We also calculate ion-beam mixing and compare our results with the experimental data, discussing how the GAP-MLIP along with the inclusion of a realistic electronic stopping model improves the prediction of experimental mixing values.

physics.app-ph↗

arXiv2024

Electronic effects in radiation-induced collision cascades in nickel

The accurate treatment of electronic effects in multi-million atom simulations of radiation-induced collision cascades is crucial for reliable predictions of primary radiation damage. In this work, we explore the performance of a recently developed two-temperature molecular dynamics model implementing an electron density-dependent coupling of electronic and atomic subsystems for cascade simulations in nickel. We show that the parameter-free model realistically captures the instantaneous energy losses during all stages of the highly non-equilibrium cascade process. Simulations predict two distinct coupling regimes, corresponding to the rapid electronic stopping energy losses in the early stages of the cascade and to the electron-phonon coupling mechanism in the later stages, without the use of separate coupling terms. The intermediate stage of the cascade dynamics displays a complex energy transfer between the subsystems, which cannot be validated by comparison to either electronic stopping or electron-phonon coupling theories. We therefore compare the predicted atomic mixing, which is sensitive to the energy losses during the intermediate cascade stage, with experimental ion beam mixing measurements. We find good agreement with the experiments, validating the coupling model for the intermediate stage of cascades. Predictions of final defect numbers and cluster sizes are found in line with the predictions of conventional electronic stopping-based methods, while significantly reducing the theoretical uncertainty in the predictions of conventional models stemming from arbitrary choices of thresholds for different coupling terms. Our results represent a notable improvement in cascade damage predictions in nickel, providing validation of the electron density-dependent coupling model for radiation damage simulations in general.

cond-mat.mtrl-sci↗

arXiv2022

Microstructure of a heavily irradiated metal exposed to a spectrum of atomic recoils

At temperatures below the onset of vacancy migration, metals exposed to energetic ions develop dynamically fluctuating steady-state microstructures. Statistical properties of these microstructures in the asymptotic high exposure limit are not universal and vary depending on the energy and mass of the incident ions. We develop a model for the microstructure of an ion-irradiated metal under athermal conditions, where internal stress fluctuations dominate the kinetics of structural evolution. The balance between defect production and recombination depends sensitively not only on the total exposure to irradiation, defined by the dose, but also on the energy of the incident particles. The model predicts the defect content in the high dose limit as an integral of the spectrum of primary knock-on atom energies, with the finding that low energy ions produce a significantly higher amount of damage than high energy ions.

cond-mat.mtrl-sci↗

arXiv2021

Comparison of SIA Defect Morphologies from Different Interatomic Potentials for Collision Cascades in W

The morphology of defects formed in collision cascades is an essential aspect of the subsequent evolution of the microstructure. The morphological composition of a defect decides its stability, interaction, and migration properties. We compare the defect morphologies in the primary radiation damage caused by high energy collision cascades simulated using three different interatomic potentials in W. An automated method to identify morphologies of defects is used. While most defects form 1/2\3 dislocation loops, other specific morphologies include \1 dislocation loops, multiple loops clustered together, rings corresponding to C15 configuration and its constituent structures, and a combination of rings and dislocations. The analysis quantifies the distribution of defects among different morphologies and the size distribution of each morphology. We show that the disagreement between predictions of the different potentials regarding defect morphology is much stronger than the differences in predicted defect numbers.

cond-mat.mtrl-sci↗

arXiv2020

Graph Theory Based Approach to Characterize Self Interstitial Defect Morphology

The defect morphology is an essential aspect of the evolution of crystals' microstructure and its response to stress. Existing methods either only report defect concentration or characterize only some of the defect morphologies. The need for an efficient and comprehensive algorithm to study defects is becoming more evident with the increase in the amount of simulation data and improvements in data-driven algorithms. We present a method to characterize a defect's morphology precisely by reducing the problem into graph theoretical concepts of finding connected components and cycles. The algorithm can identify the different homogenous components within a defect cluster having mixed morphology. We apply the method to classify morphologies of over a thousand point defect clusters formed in high energy W collision cascades. We highlight our method's comparative advantage for its completeness, computational speed, and quantitative details.

physics.comp-ph↗

arXiv2019

Atomistic-Object Kinetic Monte Carlo simulations of irradiation damage in tungsten

We describe the development of a new object kinetic Monte Carlo code where the elementary defect objects are off-lattice atomistic configurations. Atomic-level transitions are used to transform and translate objects, to split objects and to merge them together. This gradually constructs a database of atomic configurations -- a set of relevant defect objects and their possible events generated on-the-fly. Elastic interactions are handled within objects with empirical potentials at short distances, and between spatially distinct objects using the dipole tensor formalism. The model is shown to evolve mobile interstitial clusters in tungsten faster than an equivalent molecular dynamics simulation, even at elevated temperatures. We apply the model to the evolution of complex defects generated using molecular dynamics simulations of primary radiation damage in tungsten. We show that we can evolve defect structures formed in cascade simulations to experimentally observable timescales of seconds while retaining atomistic detail. We conclude that the first few nanoseconds of simulation following cascade initiation would be better performed using molecular dynamics, as this will capture some of the near-temperature-independent evolution of small highly-mobile interstitial clusters. We also conclude that, for the 20keV PKA cascades annealing simulations considered here, internal relaxations of sessile objects difficult to capture using conventional object KMC with idealised object geometries establish the conditions for long timescale evolution.

cond-mat.mtrl-sci↗

arXiv2018

Pattern Matching and Classification of Clusters in Collision Cascades

The structure of defect clusters formed in a displacement cascade plays a significant role in the micro-structural evolution during irradiation. Molecular dynamics simulations have been widely used to study collision cascades and subsequent clustering of defects. We present a novel method to pattern match and classify defect clusters. A cluster is characterized by the geometrical and topological histograms of its angles and distances which can then be used as similarity metrics. The technique is demonstrated by matching similar clusters for different cluster shapes like ring, crowdions etc. in a database of cascade damage configurations in Fe and W at different energies. We further use graph based dimensionality reduction techniques and unsupervised machine learning on the features of all the clusters present in the database to find classes of clusters. The classification successfully separates out many already known categories of clusters such as crowdions, planar crowdion pairs, rings and perpendicular crowdions. The dimensionality and size of different classes provides a broad categorization of classes. The distribution of different classes of shapes among cascades of different elements and energies shows the exclusivity of shapes to elements and energies. We discuss the key points and computational efficiency of the algorithms along with the various prominent results of their application. We discuss the motivation for using machine learning and statistics for the problems and compare different techniques. The algorithms along with the supporting analysis and visualizations give an unsupervised approach for classification and study of defect clusters in cascades. The distribution of cluster shapes and structures along with the shape properties like diffusivity, stability, etc. can be used as input to higher scale models in a multi-scale radiation damage study.

physics.comp-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↗