A physics-informed neural network approach to the point defect model for electrochemical oxide film growth
Physics-informed neural networks (PINNs) offer a novel AI-driven framework for integrating physical laws directly into neural network models, facilitating the solution of complex multiphysics problems in materials engineering. This study systematically explores the application of PINNs to simulate oxide film layer growth in halide-free solutions using the point defect model (PDM). We identify and analyze four key failure modes in this context: imbalanced loss components across different physical processes, numerical instabilities due to variable scale disparities, challenges in enforcing boundary conditions within multiphysics systems, and convergence to mathematically valid but physically meaningless solutions. To overcome these challenges, we implement and validate established techniques including nondimensionalization for training stabilization, Neural Tangent Kernel-based adaptive loss balancing, robust enforcement of boundary conditions and hybrid training with sparse data. Our results demonstrate the effectiveness of these strategies in enhancing the reliability and physical fidelity of PINNs, achieving sub $1\%$ relative error as compared to Finite Element Benchmarks with the hybrid model. Thereby showing that PINNs can be used for high fidelity electrochemical simulations with minimal data requirements and highlight necesary factors for fully autonomous PINN simulations.