PhD Thesis on Machine Learning Electrostatic Interactions in Materials

The thesis presents a comprehensive study on machine learning interatomic potentials (MLIPs) and their limitations in modeling long-range electrostatics. It introduces two novel QEq-based architectures that enhance the predictive capabilities of MLIPs, providing a foundation for future advancements in the field. The work highlights both the potential and limitations of these approaches in accurately modeling complex materials.