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Elucidating Thermodynamically Driven Structure-Property Relations for Zeolite Adsorption Using Neural Networks
Christopher Rzepa1, Devin Dabagian1, Daniel W Siderius2
1Department of Chemical and Biomolecular Engineering, Lehigh University, Bethlehem, Pennsylvania 18015, United States.
JACS Au
|December 30, 2024
Summary
Linear trends in zeolite adsorption properties often fail for confined systems. Machine learning models accurately predict adsorption thermodynamics using only molecular and zeolite descriptors, identifying key structural features.
Area of Science:
- Materials Science
- Computational Chemistry
- Chemical Engineering
Background:
- Designing zeolites for catalysis, gas storage, and separations requires understanding molecular confinement.
- Previous studies proposed linear correlations for adsorption thermodynamics, but their broad applicability was unproven.
Purpose of the Study:
- To investigate the generalizability of linear adsorption models across diverse molecules and zeolites.
- To develop predictive models for adsorption thermodynamics in confined zeolite systems.
- To identify key molecular and zeolite structural features influencing adsorption.
Main Methods:
- Conducted >3500 molecular simulations of adsorbate-zeolite combinations.
- Developed and validated nonlinear predictive models, specifically bootstrapped neural networks.
- Utilized SHAP analysis to determine feature importance for adsorption property prediction.
Main Results:
- Linear adsorption trends were found to collapse in highly confined zeolite systems.
- No universal linear models were identified to predict adsorption properties from molecular and zeolite structures.
- Nonlinear models accurately predicted entropy of adsorption, isosteric heat, and Henry's constant.
- Framework features, particularly pore diameter, were more critical for predicting entropy of adsorption than adsorbate features.
Conclusions:
- Linear models are insufficient for predicting adsorption in confined zeolites.
- Machine learning models using geometric and physical descriptors offer accurate predictions of adsorption thermodynamics.
- Zeolite pore structure significantly influences adsorption entropy, while molecular size is key for Henry's constant.
