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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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Science and Technology of Advanced Materials |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2025.2454219 |
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| _version_ | 1832544324430069760 |
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| author | Aya Fujiwara Sunao Nakanowatari Yohei Cho Toshiaki Taniike |
| author_facet | Aya Fujiwara Sunao Nakanowatari Yohei Cho Toshiaki Taniike |
| author_sort | Aya Fujiwara |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2d7a23ff5c2243ae8fdff33b67f89bbc |
| institution | Kabale University |
| issn | 1468-6996 1878-5514 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Science and Technology of Advanced Materials |
| spelling | doaj-art-2d7a23ff5c2243ae8fdff33b67f89bbc2025-02-03T10:32:16ZengTaylor & Francis GroupScience and Technology of Advanced Materials1468-69961878-55142025-12-0126110.1080/14686996.2025.2454219Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineeringAya Fujiwara0Sunao Nakanowatari1Yohei Cho2Toshiaki Taniike3Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, JapanSolid 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.https://www.tandfonline.com/doi/10.1080/14686996.2025.2454219Catalyst informaticsmachine learninghigh-throughput experimentationdescriptoroxidative coupling of methane |
| spellingShingle | Aya Fujiwara Sunao Nakanowatari Yohei Cho Toshiaki Taniike Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering Science and Technology of Advanced Materials Catalyst informatics machine learning high-throughput experimentation descriptor oxidative coupling of methane |
| title | Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering |
| title_full | Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering |
| title_fullStr | Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering |
| title_full_unstemmed | Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering |
| title_short | Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering |
| title_sort | acquiring and transferring comprehensive catalyst knowledge through integrated high throughput experimentation and automatic feature engineering |
| topic | Catalyst informatics machine learning high-throughput experimentation descriptor oxidative coupling of methane |
| url | https://www.tandfonline.com/doi/10.1080/14686996.2025.2454219 |
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