Development of Deep Learning Potentials to Investigate Initial Corrosion Mechanisms*

In this study, we develop and assess the applications of Deep Learning Potentials to investigate corrosion mechanisms via Molecular Dynamics (MD) simulations. Particularly, we evaluate the initial stage on a number of advanced structural high entropy and/or concentrated alloys being exposed to corrosive environments. The training and validation processes for the potential involve the use of highly diverse sampling of ab-initio MD simulations and/or electronic structure calculations that include a combination of elements used in the alloy substrates and/or the solutions. A selected sampling of impurities is also included to assess their roles to initiate the surface reactions. To develop the potential, we employ both the invariant and/or equivariant neural network approaches as implemented in several AI-generator codes such as DEEPMD-DPLR, RuNNer, or NEQUIP/ALLEGRO. As a part of the potential development, we also evaluate the effect of long-range electrostatic interactions toward the surface reactions e.g. as explicitly treated in RuNNER or DEEPMD-DPLR codes. The simulation results will also be compared with our experimental works that measure and model the corrosion resistance and its passivation characteristics.