Optimization of Atomic Kinetics Monte Carlo Program TensorKMC Based on Machine Learning Atomic Potential Functions

The nuclear reactor pressure vessel is a crucial component in a nuclear power plant,but it is susceptible to damage from irradiation during its use.This damage greatly affects its service life and poses a potential safety hazard.The atomic kinetics Monte Carlo(AKMC) method is an effective theoretical method for studying the irradiation damage of materials.It can be combined with numerical computer simulations to study the microstructural evolution of pressure vessels.Since irradiated damaged materials have defects,the modeling of interatomic interactions must consider non-spherical symmetric interactions.However,the TensorKMC method does not account for the angular interactions of atoms in its calculations.To address this issue,this paper proposes a fingerprint modeling method that includes angular interactions.It can be perfectly combined with the triple encoding of TensorKMC,and the computational process of angular fingerprinting can be simplified by using multiple weight.We have implemented this method in the TensorKMC program.The test results show that the angular fingerprint has a significant impact on the accuracy of the potential function.The higher the maximum angular momentum,the more accurate the potential function is.However,the simulation time consumed by the program will increase significantly.We also test the activation functions for the atomic potential function of TensorKMC.The results show that the gradient-smooth Softplus and SquarePlus have a significant advantage over the ReLU used in the initial version of TensorKMC in fitting the high-dimensional potential surface.The ReLU has a performance advantage at low maximum angular momentum,but as the maximum angular momentum increases,the different activation functions have almost no particular effect on the overall simulation time.Therefore,we recommend using gradient-smooth activation functions in practical studies.