Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset

Metal organic frameworks (MOFs) have already been widely used in the field of gas adsorption and separation. The combination of various organic ligands and metal ions results in large MOF libraries with different topological structures. However, such large structural databases make it difficult to screen out MOF materials with high adsorption performance towards a given gas to establish statistical pictures through experiments or molecular simulations. In this work, we studied and built machine learning (ML) models based on the hypothetical MOF (hMOF) database. A gradient-boosted regression trees (GBRT) model with 1000 samples can predict the methane adsorption capacity of more than 130,000 hMOFs with high accuracy. A fast high-throughput screening on the entire database was performed using the constructed regression model, which extracts several molecular fragments from the top 1% of materials with the largest methane adsorption capacity. Careful analysis of the chemical structure of these fragments indicates that MOF materials containing cyano, hydroxyl, carboxyl and/or aromatic rings may possess high adsorption performance. The electrostatic potential distribution of these fragments was obtained at the ab initio computational level and was highlighted to be another important descriptor. We further extended the GBRT predictor to diverse MOF libraries including tobacco MOF (tobmof) and CoRE MOF. The molecular fragments extracted from the ML-based screening on the real CoRE MOF database as well as the corresponding electrostatic properties were compared with the results of hMOFs, which provides new insight into the design of MOF materials.