Representation Learning for Long-Chain Hydrocarbon Adsorption in Zeolites

Zeolites are a class of crystalline nanoporous materials known for their ability to discriminate molecules based on size and shape. Such molecular shape selectivity arises from the precise 3-dimensional arrangement of zeolite framework atoms and the resulting non-covalent interactions. In this work, Henry’s constants (k H) for n-octadecane adsorption in all-silica zeolites were used as the target property in a systematic effort to optimize and evaluate various machine-learning representations widely used for materials modeling, including convolutional neural networks (ConvNets) with 3D volumetric grids (ZeoNet, as proposed J. Mater. Chem. A 2023, 11, 17570), ConvNets with 2D multi-view images, Vision Transformers with 3D volumetric grids, PointNet and EdgeConv with point clouds of atomic coordinates and solvent-accessible surface, and graph-based neural networks (CGCNN, MEGNet, M3GNet, and MACE). ZeoNet was 1 found to vastly outperform other representations, achieving a correlation coefficient of r 2 = 0.973 and a mean-squared error (MSE) of 4.4 in ln k H. In comparison, the best-performing graph model for this task, M3GNet, obtained r 2 = 0.888 and MSE = 18.5, reflecting the difficulty of graph models to capture subtle structural variations and long-ranged spatial correlations. ZeoNet also exhibits excellent transferability to other hydrocarbon molecules, including mono-and di-branched C18 isomers and linear C24 and C30 alkanes. Fine-tuning of pre-trained ZeoNet using training sets of 2100 samples can achieve the same level of performance as ZeoNet trained from scratch using over 10,000 samples. Finally, multi-task learning, which trains a single model with a shared representation and multiple prediction heads, was shown to improve the accuracy in predicting adsorption selectivity without compromising the prediction of Henry’s constants for individual molecules.