arXiv subjects

Jesper Byggmastar

Publications and source records attributed to Jesper Byggmastar.

3 recordsLinked to original sources

arXiv2025

Utilizing a machine-learned potential to explore enhanced radiation tolerance in the MoNbTaVW high-entropy alloy

High-entropy alloys (HEAs) based on tungsten (W) have emerged as promising candidates for plasma-facing components in future fusion reactors, owing to their excellent irradiation resistance. In this study, we construct an efficient machine-learned interatomic potential for the MoNbTaVW quinary system. This potential achieves computational speeds comparable to the embedded-atom method (EAM) potential, allowing us to conduct a comprehensive investigation of the primary radiation damage through molecular dynamics simulations. Threshold displacement energies (TDEs) in the MoNbTaVW HEA are investigated and compared with pure metals. A series of displacement cascade simulations at primary knock-on atom energies ranging from 10 to 150 keV reveal significant differences in defect generation and clustering between MoNbTaVW HEA and pure W. In HEAs, we observe more surviving Frenkel pairs (FPs) but fewer and smaller interstitial clusters compared to W, indicating superior radiation tolerance. We propose extended damage models to quantify the radiation dose in the MoNbTaVW HEA, and suggest that one reason for their enhanced resistance is subcascade splitting, which reduces the formation of interstitial clusters. Our findings provide critical insights into the fundamental irradiation resistance mechanisms in refractory body-centered cubic alloys, offering guidance for the design of future radiation-tolerant materials.

cond-mat.mtrl-sci↗

arXiv2023

Large-scale machine-learning molecular dynamics simulation of primary radiation damage in tungsten

Simulating collision cascades and radiation damage poses a long-standing challenge for existing interatomic potentials, both in terms of accuracy and efficiency. Machine-learning based interatomic potentials have shown sufficiently high accuracy for radiation damage simulations, but most existing ones are still not efficient enough to model high-energy collision cascades with sufficiently large space and time scales. To this end, we here extend the highly efficient neuroevolution potential (NEP) framework by combining it with the Ziegler-Biersack-Littmark (ZBL) screened nuclear repulsion potential, obtaining a NEP-ZBL framework. We train a NEP-ZBL model for tungsten and demonstrate its accuracy in terms of the elastic properties, melting point, and various energetics of defects that are relevant for radiation damage. We then perform large-scale molecular dynamics simulations with the NEP-ZBL model with up to 8.1 million atoms and 240 ps (using a single 40-GB A100 GPU) to study the difference of primary radiation damage in both bulk and thin-foil tungsten. While our findings for bulk tungsten are consistent with existing results simulated by embedded atom method (EAM) models, the radiation damage differs significantly in foils and shows that larger and more vacancy clusters as well as smaller and fewer interstitial clusters are produced due to the presence of a free surface.

physics.comp-ph↗

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↗