Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering

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 fe...

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Bibliographic Details

Published in:	Science and Technology of Advanced Materials
Main Authors:	Aya Fujiwara, Sunao Nakanowatari, Yohei Cho, Toshiaki Taniike
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2025-12-01
Subjects:	Catalyst informatics machine learning high-throughput experimentation descriptor oxidative coupling of methane
Online Access:	https://www.tandfonline.com/doi/10.1080/14686996.2025.2454219