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DOI: 10.4121/7fdc96f0-69ff-4c1a-be2b-8aa6a0a812cb

Sharma, Shrinjay; Yang, Ping; Liu, Yachan; Rossi, Kevin; Bai, Peng et. al. (2025): Sample simulation files for "Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization". Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/7fdc96f0-69ff-4c1a-be2b-8aa6a0a812cb.v1

Categories

• Physical Chemistry (incl. Structural)
• Organic Chemistry
• Theoretical and Computational Chemistry
• Environmentally Sustainable Energy Activities
• Chemical Engineering
• Engineering
• Energy
• Chemical Sciences

Keywords

Alkanes Henry coefficients Hydroisomerization Isomers Machine learning Thermodynamics Zeolites

Licence

CC BY 4.0

Interoperability

• RO-Crate Metadata

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by Shrinjay Sharma , Ping Yang , Yachan Liu , Kevin Rossi , Peng Bai , Marcello S. Rigutto , Erik Zuidema, Umang Agarwal , Richard Baur, Sofia Calero , David Dubbeldam , Thijs Vlugt

Sample simulation files for "Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization". Please read the README file for more information, and refer to the main manuscript for more details.

Shape-selective adsorption in zeolites plays a pivotal role in catalytic hydroisomerization of long-chain alkanes, a key process in producing sustainable aviation fuels from Fischer–Tropsch products. Accurately predicting adsorption behavior for the large number of alkane isomers in different zeolite frameworks is computationally intensive. To address this, we have developed a machine learning framework that rapidly and accurately predicts Henry coefficients of linear (C₁–C₃₀) and branched (C₄–C₂₀) alkanes in one-dimensional zeolites. Using descriptors based on chain length, branching patterns, and molecular graphs, we evaluate multiple ML models, including Random Forest, XGBoost, CatBoost, TabPFN, and D-MPNN in MTT-, MTW-, MRE-, and AFI-type zeolites. TabPFN and D-MPNN offer the highest predictive accuracy. Active learning further boosts model performance by efficiently selecting diverse and structurally informative isomers. We also uncover activity cliffs, where small changes in molecular structure lead to sharp variations in adsorption, and demonstrate that targeted oversampling of these cases improves model robustness. Finally, we combine the ML-predicted Henry coefficients with gas-phase thermodynamics to compute reaction equilibrium distributions for C₁₆hydroisomerization. This integrated, data-driven approach enables efficient screening and design of shape-selective zeolite catalysts, thereby reducing the need for costly simulations.

History

• 2025-10-02 first online, published, posted

Publisher

4TU.ResearchData

Format

data/xlsx, data/txt, script/py, script/cpp

Associated peer-reviewed publication

Machine Learning-Based Predictions of Henry Coefficients for Long-Chain Alkanes in One-Dimensional Zeolites: Application to Hydroisomerization

Organizations

1. TU Delft, Faculty Mechanical Engineering, Department of Process and Energy, Engineering Thermodynamics
2. TU Eindhoven, Department of Applied Physics and Science Education
3. University of Massachusetts Amherst, Department of Chemical Engineering
4. TU Delft, Faculty of Mechanical Engineering, Department of Materials Science and Engineering
5. TU Delft The Hague Campus, Climate Safety and Security Centre
6. Shell Global Solutions International B.V.
7. Shell Chemical LP
8. University of Amsterdam, Van’t Hoff Institute of Molecular Sciences