ArticleJACS Au2024
Elucidating Thermodynamically Driven Structure-Property Relations for Zeolite Adsorption Using Neural Networks.
Article in JACS Au, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.Nature communications · 2026Article
- Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
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Authors and funding
7 authors.
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Abstract
Understanding the origin and effect of the confinement of molecules and transition states within the micropores of a zeolite can enable targeted design of such materials for catalysis, gas storage, and membrane-based separations. Linear correlations of the thermodynamic parameters of molecular adsorption in zeolites have been proposed; however, their generalizability across diverse molecular classes and zeolite structures has not been established. Here, using molecular simulations of >3500 combinations of adsorbates and zeolites, we show that linear trends hold in many cases; however, they collapse for highly confined systems. Further, there are no simple predictors of the slope of the linear correlations, thereby indicating that there are no universal linear models relating molecule and zeolite pore structures with adsorption properties. We show that nonlinear models, in particular bootstrapped neural networks, that only use geometric and physical descriptors of the adsorbate and zeolite as features can predict the entropy of adsorption, isosteric heat, and Henry's constant (log(
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