This comprehensive review explored the breakthrough potential of metal–organic framework (MOFs) and high entropy materials (HEMs) for advancing energy conversion. Solar light utilization for clean energy conversion become the potential strategy to overcome the energy crisis, in recent years. MOFs and HEMs are multifunctional nanostructured materials receiving accelerated attention in energy conversion. MOFs consist of metal ions (M) combined to organic linkers and have nanoscale geometry, tunable cage with porous size, flexible skeletons, ultrahigh surface area, large surface-to-volume ratio, abundant active sites, fast charge transportation and crystallinity which enhanced the water oxidation efficiency. HEMs have multi-component random distribution with disordered structure which extending the catalytic active-sites range and forming stable monophase solid-solution architecture. The unique entropy stabilization effect increased the active sites by preventing HEMs agglomeration which in turn improved the stability. In this review, we summarized MOFs and HEMs based electrocatalytic materials in oxygen evolution reaction (OER) and how morphology regulation and tuning the structure of material enhanced the activity and increased the active sites, respectively. Recently, machine learning (ML) models reveal the role of descriptors influencing the overpotential and exposed the origin of MOFs and HEMs catalytic activity. These ML models reduced the costs and served as a guide for designing efficient catalysts. This review delves a roadmap for the advancement of MOFs and HEMs based electrocatalytic materials, shaping the future of fuel technologies with the cutting edge of ML models.

中文翻译：

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用于水氧化的高性能电催化材料的最新进展和最新的机器学习优势：到 2024 年的概述


本综述探讨了金属有机框架 （MOF） 和高熵材料 （HEM） 在推进能量转换方面的突破性潜力。近年来，利用太阳能进行清洁能源转换成为克服能源危机的潜在策略。MOF 和 HEMs 是多功能纳米结构材料，在能量转换中受到加速关注。MOF 由金属离子 （M） 与有机接头结合而成，具有纳米级几何形状、多孔尺寸的可调笼、柔性骨架、超高表面积、大表面体积比、丰富的活性位点、快速电荷传输和结晶度，从而提高了水的氧化效率。HEM 具有多组分随机分布和无序结构，扩展了催化活性位点范围并形成稳定的单相固溶体结构。独特的熵稳定效应通过阻止 HEMs 团聚来增加活性位点，从而提高稳定性。在这篇综述中，我们总结了基于 MOF 和 HEMs 的电催化材料在析氧反应 （OER） 中的表现，以及形态调控和调整材料结构如何分别增强活性和增加活性位点。最近，机器学习 （ML） 模型揭示了描述符影响过电位的作用，并揭示了 MOF 和 HEMs 催化活性的来源。这些 ML 模型降低了成本，并作为设计高效催化剂的指南。这篇综述深入探讨了基于 MOF 和 HEMs 的电催化材料的发展路线图，利用最先进的 ML 模型塑造燃料技术的未来。