Cu Diffusion in Amorphous Ta2O5 Studied with a Simplified Neural Network Potential

Understanding atomistic details of diffusion processes in amorphous structures is becoming increasingly important due to the recent advances in various information and energy devices. Atomistic simulations based on the density functional theory (DFT) represent a powerful approach; however, the development of a method characterized by both high reliability and computational efficiency remains a challenge. In this study, a simple neural network (NN) interatomic potential is constructed from the results of DFT simulations to investigate the diffusion of a single Cu atom in amorphous Ta2O5. The proposed technique is as accurate as the DFT in predicting hopping paths and the corresponding barrier energies in a given amorphous structure. Although the developed NN-based approach exhibited some limitations since it was constructed specifically for Cu, the obtained results showed that the NN potential was able to satisfactorily describe the Cu diffusion behavior. Thus, the Cu diffusion activation energy calculated at low temperatures (between 500 and 800 K) using kinetic Monte Carlo simulations and the NN potential matched the experimental data reasonably well.