Probing the PMI Properties of Tungsten-based High-entropy Alloys as Plasma-facing Components

High-entropy alloys (HEAs) are a novel class of complex materials that contain several principal elements in equal or near-equal atomic percent. W-based HEAs exhibit high structure stability under irradiation and outstanding mechanical properties at elevated temperatures. However, we still don’t fully understand the synergistic radiation and plasma interactions with this new class of material and how their properties change under extreme conditions. Our objective in this project is to probe the feasibility of understanding and predicting how irradiation-driven composition of W-based HEA affects its PMI properties, especially surface evolution. In this work, we adopted an energy landscape modeling technique to develop a Deep Neural Network that incorporates the potential energy functions of a W-HEA composed of WTaCrV. Machine learning was employed to use the deep potential MD scheme to perform MD modeling that has the same accuracy as the first-principle models. This approach was validated and compared with results from using EAM potentials. To capture the ion-induced mixing, BCA calculations of N and Ar irradiation were performed using DYNAMIX. Irradiation experiments of N and Ar with fluences of [10¹⁵-10¹⁷] cm^-2, 45° incidence, and 1 KeV were also performed in IGNIS-I at UIUC.