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説明:

Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights across different catalyst families. To overcome this fragmentation, we introduce a framework that integrates high-throughput experimentation (HTE) and automatic feature engineering (AFE) with active learning to acquire comprehensive catalyst knowledge. The framework is demonstrated for oxidative coupling of methane (OCM), where active learning is continued until the machine learning model achieves robustness for each of the BaO-, CaO-, La2O3-, TiO2-, and ZrO2-supported catalysts, with 333 catalysts newly tested. The resulting models are utilized to extract catalyst design rules, revealing key synergistic combinations in high-performing catalysts. Moreover, we propose a method for transferring knowledge between supports, showing that features refined on one support can improve predictions on others. This framework advances the understanding of catalyst design and promotes reliable machine learning.

権利情報:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

キーワード: Catalyst informatics, machine learning, high-throughput experimentation, descriptor, oxidative coupling of methane

刊行年月日: 2025-12-31

出版者: Taylor & Francis

掲載誌:

• Science and Technology of Advanced Materials (ISSN: 14686996) vol. 26 2454219

研究助成金:

原稿種別: 著者最終稿 (Accepted manuscript)

MDR DOI: https://doi.org/10.48505/nims.5291

公開URL: https://doi.org/10.1080/14686996.2025.2454219

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更新時刻: 2025-07-16 16:17:04 +0900

MDRでの公開時刻: 2025-01-24 08:30:14 +0900