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

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.<br>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.

本数据集包含《基于机器学习预测一维沸石中长链烷烃的亨利系数（Henry Coefficients）：在加氢异构化（Hydroisomerization）中的应用》的示例模拟文件。如需获取更多信息，请阅读README文件，详细细节可参阅主稿件。
沸石（Zeolites）中的择形吸附在长链烷烃的催化加氢异构化过程中发挥关键作用，而该过程是由费托（Fischer–Tropsch）产物制备可持续航空燃料的核心环节。精准预测不同沸石骨架中大量烷烃异构体的吸附行为，计算成本极高。为此，我们开发了一套机器学习框架，可快速且精准地预测一维沸石中直链（C₁–C₃₀）与支链（C₄–C₂₀）烷烃的亨利系数。我们基于链长、支化模式与分子图构建特征描述符，在MTT型、MTW型、MRE型及AFI型沸石中评估了多种机器学习模型，包括随机森林（Random Forest）、XGBoost、CatBoost、TabPFN以及D-MPNN。其中TabPFN与D-MPNN的预测精度最高。
主动学习（Active learning）通过高效筛选多样化且结构信息丰富的异构体，进一步提升了模型性能。我们还发现了活性悬崖（activity cliffs）现象：分子结构的微小变化会导致吸附行为的剧烈波动，并证实针对此类样本进行定向过采样可提升模型的鲁棒性。最后，我们将机器学习预测得到的亨利系数与气相热力学相结合，计算得到C₁₆烷烃加氢异构化的反应平衡分布。这种集成化的数据驱动方法可实现择形沸石催化剂的高效筛选与设计，从而减少高成本模拟实验的需求。