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

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 C16 hydroisomerization. This
integrated, data-driven approach enables efficient screening and design
of shape-selective zeolite catalysts, thereby reducing the need for
costly simulations.