2025.05.15 by 中野渡 淳 Sunao NakanowatariIs an unknown catalyst likely to be high-performing, low-performing, potentially either, lacking sufficient information for prediction, or seemingly low-performing but with unexpected potential (serendipity)? In this study, we developed a system capable of inferring these possibilities from existing catalyst data. Using this system, we demonstrated the potential of a data-driven, efficient, and systematic approach to catalyst development that balances the exploitation of existing knowledge, exploration of unknowns, and discovery of serendipitous outcomes.
2025.04.11 by 張 葉平 Yohei ChoPhotocatalysis involves a series of sequential steps, including light absorption, excited carrier diffusion, and surface redox reactions, making the identification of the rate-limiting step challenging. This study demonstrates that the surface imbalance of excited carriers, manifested as variations in reaction temperature, can be utilized to distinguish whether the supply or consumption of excited carriers governs the reaction rate. This finding is anticipated to not only directly enhance photocatalytic performance but also significantly improve the precision of design hypotheses that were previously often ambiguous.
2025.02.27 by 和田 透 Toru WadaMolecular catalysts for synthesizing polyethylene and polypropylene exhibit catalytic activity upon contact with activators. Methylaluminoxane (MAO) is the most widely used activator, but its structure remains unclear, hindering the understanding of catalyst activation mechanisms and the development of new activators. In this study, we analyzed the molecular structure of MAO using synchrotron X-ray total scattering and identified it as plate-like molecules with diameters of approximately 2 nm. These findings are crucial for elucidating the working mechanism of MAO and expected to contribute to the development of new activators and plastic materials.
2025.01.21 by 藤原 綾 Aya FujiwaraConventional catalyst discovery has been limited to the exploration and optimization of areas surrounding known high-performance catalysts, making it challenging to grasp the overall landscape of the design space or transfer insights across different catalyst systems. In this study, we aimed to overcome these limitations by employing two original techniques on high-throughput experimentation and feature engineering. As a result, comprehensive catalyst knowledge was obtained for the five catalyst systems applied to oxidative coupling of methane. This enabled a systematic understanding of catalyst design rules and facilitated knowledge transfer across catalyst systems during machine learning.
2025.01.05 by Wentao DUThe traditional approach to designing solid catalysts has been time-consuming and labor-intensive, relying on trials and errors guided by prior knowledge and hypotheses. Recently, combining machine learning with high-throughput experimentation, researchers are exploring uncharted chemistry without preconceptions or biases. Our study exemplifies this shift, focusing on methane dry reforming—a key reaction for sustainable energy and greenhouse gas mitigation. We constructed an unbiased dataset of 256 γ-Al2O3-supported catalysts, each with random combinations of 17 elements from the periodic table. This eliminates assumptions about optimal elements, allowing us to discover surprising trends and catalysts through machine learning.