Advances in machine learning-based materials research for MOFs:energy gas adsorption separation
Metal-organic frameworks(MOFs)have attracted much attention in the field of gas adsorption and separation due to their high porosity and ultra-high specific surface area,and the database of MOFs has been enriched as a result.The use of high-throughput computational screening methods can provide rich structural properties and performance data,which is beneficial to screening materials with high performance from a large number of metal-organic framework materials.In order to fully explore the information within the data,machine learning is used as an auxiliary tool that can reveal the implicit metal-organic framework structure and property relationships.To gain a greater understanding of the performance trends of metal-organic framework materials in different applications,especially in gas storage and separation,machine learning methods are also widely used.The latest research progress in machine learning prediction and design of metal-organic framework materials applied to the adsorption and separation of combustible gases is reviewed in terms of the descriptors of metal-organic frameworks suitable for machine learning work,and the screening and prediction of material properties by using machine learning methods,which accelerates the pace of the design and development of metal-organic frameworks,and guides the direction and rules of material synthesis,reducing the cost of manpower and material resources.
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