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.