Simulating Aluminum Corrosion Using DFT Trained Deep Neural Network Potentials

Current materials challenges necessitate simulations at length and time scales that often exceed the capabilities of the state of the art in density functional theory (DFT). Many effective Hamiltonian methods that can scale beyond these limits, such as cluster expansion, tight-binding, and interatomic potentials, often require a significant amount of expertise to train and employ. Machine learning methods such as deep neural networks exchange expertise for data-volume in what are typically expert-driven processes. Here we demonstrate the power of a deep neural network potential (DNP) to model the stability of various phases and terminations of Al2O3 on Al. This model builds off previous work using DFT to demonstrate that the relative stability of alpha-, gamma-, and amorphous Al2O3 changes with the film thickness, but was limited to one coherency constraint. With our DNP, we are able to find lower strain but less coherent interfaces for all three phases altering the layer thickness at which relative stability shifts. More importantly, we see strong correlations with interface chemistry suggesting that the environment chemical state can play a strong role in the nucleation and early stages of Al2O3 film growth.