Predicting geometric properties of metal-organic frameworks by fusing 3D and graph convolutional neural networks

Metal-organic frameworks (MOFs) have emerged in recent years as a substantial class of crystalline structures with extremely high porosity, inner surface area, and variability of the organic and inorganic components. Calculating geometric properties of MOFs is done through Monte Carlo simulations which are both time-consuming and tedious. Fast and accurate prediction of these is a first step to enabling the synthesis of new and novel structures. We propose a fusion model that combines a 3D convolutional neural network and a graph convolutional neural network to predict geometric properties of MOFs such as Henry’s constant, surface area, pore limiting diameter, and largest cavity diameter. The model utilizes both 3D grid and graph-structured representations of MOFs to predict the geometric properties. We used the CoRE MOF 2019 dataset with expanded geometric properties such as Henry's constant and surface area. Our model quickly predicts the geometric properties of MOFs and will aid in the high-throughput characterization of MOFs.