Neikha Kevizali, Puzari Amrit
Department of Chemistry, National Institute of Technology Nagaland, Chumoukedima, Nagaland 797103, India.
Langmuir. 2024 Oct 22;40(42):21957-21975. doi: 10.1021/acs.langmuir.4c03126. Epub 2024 Oct 9.
Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information.
金属有机框架材料（MOFs）是一类混合多孔材料，作为一种具有多种应用的重要材料而备受关注。目前，MOFs被广泛应用，尤其是在能源和催化领域。这些材料的合成面临着相当大的挑战，并且由于其复杂的结构以及在材料科学领域的广泛应用，对它们的计算分析也极为复杂。密度泛函理论（DFT）帮助研究人员理解反应和机理，但它成本高昂且耗时，并且需要更大的系统来进行这些计算。为了克服这些问题，人们采用机器学习（ML）技术，将其应用于MOFs的合成、结构和性质预测的材料数据集。这些预测快速、高效且准确，并且不需要大量计算。在这篇综述中，我们讨论了用于MOF的ML模型及其在结构和性质预测中与人工智能（AI）的结合。AI在该领域的优势将加速研究，特别是在以最少信息合成具有多种性质和应用的新型MOFs方面。
