Machine Learned Interatomic Potential for Dispersion Strengthened Plasma Facing Components
E. L. Sikorski, M. A. Cusentino, M. J. McCarthy, J. Tranchida, M. A., Wood, and A. P. Thompson

TL;DR
This paper develops a machine-learned interatomic potential for W-ZrC alloys, enabling large-scale simulations of their microstructural evolution and mechanical properties at high temperatures relevant to fusion reactors.
Contribution
The authors introduce a spectral neighbor analysis potential trained on diverse ab initio data for W-ZrC, improving accuracy and stability for high-temperature atomistic simulations.
Findings
Validated potential reproduces lattice parameters, surface energies, and thermal expansion.
W(110)-ZrC(111) bicrystal strength decreases with temperature, but Zr-terminated interfaces remain strong at 2500K.
Diffusion of C into W weakens the interface at high temperatures.
Abstract
Tungsten (W) is a material of choice for the divertor material due to its high melting temperature, thermal conductivity, and sputtering threshold. However, W has a very high brittle-to-ductile transition temperature and at fusion reactor temperatures (1000K) may undergo recrystallization and grain growth. Dispersion-strengthening W with zirconium carbide (ZrC) can improve ductility and limit grain growth, but much of the effects of the dispersoids on microstructural evolution and thermomechanical properties at high temperature are still unknown. We present a machine learned Spectral Neighbor Analysis Potential (SNAP) for W-ZrC that can now be used to study these materials. In order to construct a potential suitable for large-scale atomistic simulations at fusion reactor temperatures, it is necessary to train on ab initio data generated for a diverse set of structures, chemical…
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Taxonomy
TopicsFusion materials and technologies · Nuclear Materials and Properties · Ion-surface interactions and analysis
