| Abstract Scope |
In this presentation, I will discuss an integrated theoretical, computational, and experimental approach to understanding and tuning defect kinetics to accelerate the discovery of radiation damage-resistant materials. First, I introduce a neural network kinetics framework grounded on an efficient on-lattice structure and chemistry representation, enabling precise prediction and modeling of atomic diffusion and structural evolution in compositionally complex materials. Next, I will present how vacancy defects can be controlled and trapped by chemical compositions in tungsten alloys, with irradiation experiments demonstrating that this trapping effectively suppresses vacancy cluster growth. |
