ChemAuto

I am a theoretical and computational chemist working across molecular chemistry, materials science, and data-driven modeling. My work combines chemical bonding theory, quantum chemistry, and machine learning to understand and design molecules and materials.

Using orbital engineering and chemical perturbation, I have explored relationships between molecules, clusters, and solids, leading to predictions of novel compounds. I have developed multiscale models to address challenges in electronic materials, catalysis, and water purification—such as CVD growth of MoS₂ and functionalized hBN for molecular sieving.

Currently, I focus on automated reaction discovery by integrating machine learning with quantum chemical methods. This approach enables the construction of complex reaction networks and supports the broader goal of in silico design of reaction vessels.