NON-TECHNICAL SUMMARY A deep understanding of how electrons behave in materials has powered some of the most transformative technologies of modern life, from personal computing and mobile devices to weather prediction and artificial intelligence. These advances were made possible through breakthroughs in semiconductor technology, for example the invention of the transistor. Today, researchers are exploring a new class of semiconductor interfaces, known as two-dimensional "moiré materials", that may drive the next generation of innovations. These materials have the potential to support new technologies such as ultra-efficient spintronic devices and may one day become the building blocks of fault-tolerant quantum computers. However, scientific understanding of these materials is still in its early stages. In particular, existing theoretical models often rely on simplified, qualitative approaches that cannot fully capture the complex behaviors observed in experiments. This project aims to develop more accurate, predictive tools to model these systems by combining machine learning (ML) techniques with advanced quantum simulation methods. The goal is to discover new electronic phases of matter, characterize their properties, and generate reliable benchmark data to support future research. In addition to advancing materials science, the project will broaden access to computational research. The principal investigator (PI) will mentor undergraduate students in areas such as