Artificial intelligence assisted with designing metal‐organic frameworks (MOFs)

Abstract

Abstract This article discusses the role of artificial intelligence (AI) in the design and engineering of porous inorganic nanomaterials, with a special focus on metal‐organic frameworks (MOFs). MOFs are highly porous nanomaterials with a large surface area, making them ideal for various applications, including gas storage, catalysis, and drug/gene delivery. Machine learning algorithms can analyze large datasets of MOF structures and properties to identify trends and correlations, and this information can be used to predict the properties of new MOFs. AI can also optimize MOF properties for specific applications, predict the optimal synthesis conditions for a given MOF structure, and design new ligands and metal ions for MOF synthesis. Mathematical models and tools, such as molecular dynamics simulations and density functional theory calculations, can be used in conjunction with AI algorithms to improve the accuracy and efficiency of MOF synthesis. The article also explores whether AI can design a new MOF, highlighting the complex nature of the question and the different perspectives that need to be considered.