Heterogeneous catalysts design using first-principles and machine-learning methods

Abstract

Recently, as interest in eco-friendly and sustainable energy increases, the need for research on energy conversion catalysts is emerging. Heterogeneous catalysts are economically and environmentally desirable for industrial applications due to their high recycling rates. However, they show low efficiency and typically use expensive noble metals. Therefore, it is necessary to develop an economic and highly efficient heterogeneous catalyst. To develop a catalyst with desired properties, it is necessary to understand the mechanism and identify the factors that determine the activity. In this dissertation, we understand the reaction mechanism of catalyst, activity, and selectivity determining factor using the first principle calculations. And based on this, we discover stable and high-efficiency catalysts using data-based machine learning methods.