High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model

High-throughput screening of CO2 cycloaddition MOF catalyst with an explainable machine learning model

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...

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Bibliographic Details
Main Authors: Xuefeng Bai, Yi Li, Yabo Xie, Qiancheng Chen, Xin Zhang, Jian-Rong Li
Format: Article
Language:English
Published: KeAi Communications Co., Ltd. 2025-01-01
Series:Green Energy & Environment
Subjects:
Metal-organic frameworks
High-throughput screening
Machine learning
Explainable model
CO2 cycloaddition
Online Access:http://www.sciencedirect.com/science/article/pii/S2468025724000323
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http://www.sciencedirect.com/science/article/pii/S2468025724000323

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