We aim to drive breakthroughs in energy-efficient technologies and advance chemical theory, contributing to global sustainability efforts. We work at the intersection of theoretical chemistry, machine learning (ML), and materials science. Our research focuses on (a) the electrification of the chemical industry, (b) sustainable recycling processes, and (c) quantum materials. To address challenges in these fields, we develop novel chemical theories, mathematical representations compatible with ML algorithms, and ML potentials. Additionally, we focus on creating chemistry-informed ML models to expand the materials design space for various applications. These applications range from catalysis to the realization of novel quantum processes in optoelectronics. Group members will gain expertise in developing chemical theory, computational chemistry and materials science methods (such as DFT and molecular dynamics), and state-of-the-art AI/ML algorithms.