Accelerating the Selection of Covalent Organic Frameworks with Automated Machine Learning

Covalent organic frameworks (COFs) have the advantages of high thermal stability and large specific surface and have great application prospects in the fields of gas storage and catalysis. This article mainly focuses on COFs' working capacity of methane (CH₄). Due to the vast number of possible COF structures, it is time-consuming to use traditional calculation methods to find suitable materials, so it is important to apply appropriate machine learning (ML) algorithms to build accurate prediction models. A major obstacle for the use of ML algorithms is that the performance of an algorithm may be affected by many design decisions. Finding appropriate algorithm and model parameters is quite a challenge for nonprofessionals. In this work, we use automated machine learning (AutoML) to analyze the working capacity of CH₄ based on 403,959 COFs. We explore the relationship between 23 features such as the structure, chemical characteristics, atom types of COFs, and the working capacity. Then, the tree-based pipeline optimization tool (TPOT) in AutoML and the traditional ML methods including multiple linear regression, support vector machine, decision tree, and random forest that manually set model parameters are compared. It is found that the TPOT can not only save complex data preprocessing and model parameter tuning but also show higher performance than traditional ML models. Compared with traditional grand canonical Monte Carlo simulations, it can save a lot of time. AutoML has broken through the limitations of professionals so that researchers in nonprofessional fields can realize automatic parameter configuration for experiments to obtain highly accurate and easy-to-understand results, which is of great significance for material screening.