Adsorption simulations often assume a rigid framework, which can be exploited by replacing the expensive framework-adsorbate energy/force evaluation by interpolation of a precomputed energy grid. We present the implementation in RASPA3 of a triquintic interpolation algorithm by Boateng and Bradach and compare it to the tricubic algorithm of Lekien and Marsden. We extended the scheme to interpolation in fractional space to facilitate interpolation of non-rectangular frameworks and evaluated the accuracy. We find that the use of grids is advantageous for larger systems and/or large cutoffs, but generally the efficiency gains are modest (a factor of 2–5).

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

In this paper, the finite-time tracking control problem of a class of multi-agent systems with nonaffine functions and uncertain nonlinearity is investigated, which is different from the existing on high-order multi-agent systems with pure feedback forms. The multi-agent systems considered in this paper, moreover, the nonaffine functions and uncertain nonlinearities are completely unknown, and the input of each follower agent is quantized through a hysteresis quantizer. Based on the help of the fuzzy logic systems approximator, an adaptive fuzzy finite-time tracking control protocol with adaptive update laws is presented by the backstepping technique. On the basis of the finite-time stability strategy and Bhat and Bernstein theorem, the finite-time stability of designed control protocol is fully analyzed. Under the proposed control protocol, it is indicated that the tracking error of each follower agent can press on a small neighborhood in a finite time. Finally, the effectiveness of designed control protocol in this paper is analyzed by numerical examples.