Academic Journal
High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model
| العنوان: | High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model |
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| المؤلفون: | Xuefeng Bai, Yi Li, Yabo Xie, Qiancheng Chen, Xin Zhang, Jian-Rong Li |
| المصدر: | Green Energy & Environment, Vol 10, Iss 1, Pp 132-138 (2025) |
| بيانات النشر: | KeAi Communications Co., Ltd., 2025. |
| سنة النشر: | 2025 |
| المجموعة: | LCC:Renewable energy sources LCC:Ecology |
| مصطلحات موضوعية: | Metal-organic frameworks, High-throughput screening, Machine learning, Explainable model, CO2 cycloaddition, Renewable energy sources, TJ807-830, Ecology, QH540-549.5 |
| الوصف: | The high porosity and tunable chemical functionality of metal-organic frameworks (MOFs) make it a promising catalyst design platform. High-throughput screening of catalytic performance is feasible since the large MOF structure database is available. In this study, we report a machine learning model for high-throughput screening of MOF catalysts for the CO2 cycloaddition reaction. The descriptors for model training were judiciously chosen according to the reaction mechanism, which leads to high accuracy up to 97% for the 75% quantile of the training set as the classification criterion. The feature contribution was further evaluated with SHAP and PDP analysis to provide a certain physical understanding. 12,415 hypothetical MOF structures and 100 reported MOFs were evaluated under 100 °C and 1 bar within one day using the model, and 239 potentially efficient catalysts were discovered. Among them, MOF-76(Y) achieved the top performance experimentally among reported MOFs, in good agreement with the prediction. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2468-0257 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2468025724000323; https://doaj.org/toc/2468-0257 |
| DOI: | 10.1016/j.gee.2024.01.010 |
| URL الوصول: | https://doaj.org/article/38fabfddce154176aa7403f8893bb44a |
| رقم الانضمام: | edsdoj.38fabfddce154176aa7403f8893bb44a |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 24680257 |
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| DOI: | 10.1016/j.gee.2024.01.010 |