Atomistic Modeling of Materials for Fusion Energy Using Machine Learned Interatomic Potentials

Developing materials for fusion reactors is one of the leading challenges in developing fusion as a viable energy source. Plasma exposure and high temperatures at the plasma-material interface and neutron, helium, and tritium effects in structural materials will degrade the materials in the plasma-facing and structural components. Many of the processes that alter the microstructure of the material, leading to macroscopic damage and changes in material properties, occur at the atomistic scale, making molecular dynamics (MD) an essential tool in understanding the fundamental processes which drive radiation damage in these materials. However, the accuracy of these simulations is determined by the interatomic potential used, which defines the interatomic forces used in modeling the dynamics of the material. This is especially true for simulating fusion reactor materials where the conditions are far from equilibrium and challenging to develop accurate classical potentials for. Recently, machine learning interatomic potentials (ML-IAPs) have been shown to have high accuracy making them suitable for MD modeling materials in the extreme conditions present in fusion reactors. In this talk, we will discuss the development of machine learned interatomic potentials for fusion materials and subsequent MD modeling of plasma-exposure in these materials.

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