机器学习在CO2电催化中的前沿应用
Cutting-Edge Applications of Machine Learning in the Electrochemical Reduction of CO2
DOI: 10.12677/aep.2024.143088, PDF,   
作者: 李 俭, 王斯坦*：辽宁工程技术大学环境科学与工程学院，辽宁 阜新
关键词: CO2电催化；机器学习；金属有机框架；Electrochemical Reduction of CO2； Machine Learning； Metal-Organic Frameworks
摘要: 化石燃料的急剧消耗导致大气中二氧化碳水平上升，引发全球气候变暖和能源危机。将二氧化碳转化为碳基燃料被认为是减缓温室气体效应和缓解能源危机的一种有效途径。金属有机框架材料(MOFs)以其高催化活性和卓越的稳定性在二氧化碳电催化转化中备受瞩目。然而，由于MOFs具有多种结构和组成，传统的试错实验方法来探索其电催化还原性能变得耗时且昂贵。因此，机器学习方法的出现为预测金属有机框架的电催化性能并筛选电催化剂提供了新途径。本综述的目的在于详细介绍机器学习方法在预测电催化剂性能方面的研究进展，重点回顾了机器学习在电催化二氧化碳领域以及在预测金属有机框架(MOFs)在电催化中的应用，以及通过利用关键的描述符进行高通量计算，高效预测各类潜在材料的催化活性和最佳组成。机器学习在金属有机框架(MOFs)电催化二氧化碳领域具有广阔的前景和应用潜力。它们有望推动可持续能源和环境保护领域的发展，为解决全球变暖和不断增长的能源需求等重大挑战提供了潜在的创新解决方案。
Abstract: The drastic consumption of fossil fuels has led to an increase in atmospheric carbon dioxide levels, triggering global warming and an energy crisis. Converting CO2 into carbon-based fuels is considered an effective way to mitigate the greenhouse gas effect and alleviate the energy crisis. Metal-organic framework materials (MOFs) have attracted attention in the electrochemical conversion of CO2 with their high catalytic activity and excellent stability. However, due to the multiple structures and compositions of MOFs, traditional trial-and-error experimental methods to explore their electrochemical reduction properties become time-consuming and expensive. Therefore, the emergence of machine learning methods provides new ways to predict the electrochemical performance of metal-organic frameworks and screen electro catalysts. The aim of this review is to present in detail the research progress of machine learning methods in predicting the performance of electro catalysts, with a focus on reviewing the application of machine learning in the field of electrochemical reduction of carbon dioxide as well as in the prediction of metal-organic frameworks (MOFs) in electrochemistry and efficiently predicting the catalytic activity and optimal composition of various types of potential materials through high-throughput calculations using key descriptors. Machine learning has great promise and application potential in the field of electrochemical reduction of CO2 by metal-organic frameworks (MOFs). They are expected to advance the field of sustainable energy and environmental protection, offering potentially innovative solutions to address major challenges such as global warming and growing energy demand.
文章引用：李俭, 王斯坦. 机器学习在CO2电催化中的前沿应用[J]. 环境保护前沿, 2024, 14(3): 649-662. https://doi.org/10.12677/aep.2024.143088

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