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Heather J. Kulik is a professor in the Department of Chemistry at the Massachusetts Institute of Technology. Her research group focuses on leveraging multi-scale modeling and machine learning to discover new molecules and mechanisms across a range of materials, from metal-organic frameworks to enzymes. Recent advancements in theoretical chemistry have transformed the efficiency and accuracy of calculating materials properties and catalytic rates, yet challenges remain in understanding and computational design of new materials. Kulik's group aims to tackle these challenges by developing systematically improvable methods that enable mechanistic prediction and the computational design of open-shell transition metal chemistry. They utilize multi-objective Bayesian optimization to accelerate materials discovery and devise computational workflows with representations for ML-accelerated discoveries. Additionally, their work in enzyme catalysis emphasizes the importance of modeling large enzymes and high-throughput screening workflows that enhance mechanistic understanding. By combining traditional quantum chemical studies with innovative artificial intelligence models, they strive to improve the predictive accuracy of computational chemistry applications.