First-principles calculations based on Density Functional Theory (DFT) have considerably improved the accuracy of predictions and the ability to predict materials with help of machine learning. In this lab, first-principles calculations are used to study material functionalities related to energy conversion and superconductivity. For example, to find optimal catalysts for water electrolysis and fuel cell reactions, we perform extensive Monte Carlo sampling of the surface structures of candidate materials such as ZrO₂ and TiO₂ to predict reaction pathways and activation energies. In parallel, we are conducting a non-equilibrium kinetics study to elucidate the quantum theory of the electrocatalytic reduction process. Key to this study is the accurate first-principles prediction of the electron-lattice interactions, and we are also working on the construction of a non-perturbative method for this purpose. A typical application of electron-lattice interaction calculations is the study of BCS-type superconductivity, where we investigate materials with complex structures and the relationship between structure, magnetism and superconducting transition temperature.

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