Research project
Electro-catalysis
PhD research of Abhay Nagaraj
We aim to investigate the catalytic reduction of carbon dioxide (CO₂) on transition metal surfaces. This reaction is a key step in the development of sustainable technologies for CO₂ capture and conversion. Our approach leverages Machine-Learned Interatomic Potentials (MLIPs)—a powerful and rapidly evolving tool in computational chemistry. MLIPs enable us to model complex chemical processes with near ab initio accuracy, while dramatically extending the time and length scales accessible compared to traditional quantum mechanical methods. This is particularly valuable for studying heterogeneous catalysis, where the interaction between molecules and surfaces involves subtle electronic and structural effects.
A major challenge in understanding CO₂ reduction lies in modeling the electron transfer processes that drive the reaction. Electrons, being quantum in nature, are notoriously difficult to simulate directly, especially over extended simulations. MLIPs offer an efficient way to study these interactions, allowing us to explore catalytic mechanisms that were previously computationally prohibitive.
By combining ML-based simulations with experimental insights from our ANION collaborators, this research aims to uncover a more detailed picture of the CO₂ reduction pathway and its dependence on surface composition and structure. Ultimately, our goal is to support the design of more efficient catalysts and contribute to the advancement of green, carbon-neutral chemical processes.
Working with Bernd Ensing and the ANION Consortium