Unveiling the Structural Factors Governing the Diffusion of Ethene in Small-Pore Zeolites through Machine Learning

Investigating diffusion dynamics in nanoscale confined spaces of zeolites has long been of significant interest. Although zeolite structures are known to significantly influence their diffusion properties, determining how a specific structure impacts diffusion performance is challenging. This difficulty arises from a lack of methodologies for quantifying structural factors and linking correlated variables to the diffusion performance. To address this challenge, this study utilizes a data-driven approach to systematically assess diffusion behaviors in small-pore zeolites. Machine learning models were trained and achieved high diffusion predictive performance through newly devised descriptors. Model interpretation and further molecular dynamics simulations highlighted the significant role of the cage architecture in influencing diffusion. Moreover, we discovered that small-pore zeolites with specific stacking sequences, the parallel stacking of 6-rings, acquired enhanced diffusion properties synergistically as the proportion of double-six-ring units increased. This study advances the understanding of diffusion in micropores and offers insights into zeolite design for promoting molecular transport.