金属-有机框架(MOFs)材料在气体吸附、催化和分离等领域展现了广阔的应用前景,但通过实验筛选高性能MOFs材料繁琐且耗时。随着人工智能的发展,机器学习为MOFs材料捕获CO2提供了一种高效、精准的研究手段。综述了机器学习模型筛选MOFs流程及其在碳捕获中的应用进展,提出通过自动化方法提取MOFs特征,或应用可解释性方法增强模型可信度和可理解性是未来机器学习辅助MOFs材料碳捕获的研究方向。
Metal-organic frameworks (MOFs) have been extensively utilized in the domains of gas adsorption,catalysis,and separation.However,the experimental screening of high-performance MOFs is arduous and time-consuming.Along with the advancement of artificial intelligence,machine learning offers an efficient and precise research approach for MOFs to capture CO2.In this paper,the screening process of the MOFs machine learning model and its application in carbon capture were reviewed.It was proposed that automated methods for extracting MOFs features or interpretability methods to enhance the reliability and understandability of the model should be the future research orientations of machine learning-assisted MOFs materials for carbon capture.
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基金资助
陕西省重点研发计划(2023-YBNY-260);西安工程大学科研计划项目(310/107020511)
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