Research

Advanced Nanocatalyst Design Lab

Data-driven optimization for heterogeneous catalysis

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  • Data-driven optimization for heterogeneous catalysis



    ■ Our lab aims to optimize heterogeneous catalysis by integrating machine learning with experimental and literature data. We build a robust experimental database and leverage machine learning models to capture relationships between catalyst performance and both design and operating parameters. This data-driven approach enables catalyst performance prediction and design, minimizing experimental trials and accelerating optimal catalyst discovery.

    ■ We leverages process simulation to evaluate the industrial applicability of catalytic processes. We use tools such as AspenTech and MATLAB to optimize reaction conditions, improve process efficiency, and assess economic and environmental impacts. This approach enables the design and evaluation of catalysts and processes that maximize sustainability and energy efficiency in industrial applications.