Nitric oxide, NO, is a free radical that forms dimers, (NO)2, at its vapor–liquid coexisting temperatures. In this work, we developed an all-atom force field for NO and (NO)2. To assess the performance of this force field, we computed the vapor–liquid equilibrium (VLE) properties of the reactive NO–(NO)2 system, as well as those of pure NO and pure (NO)2, using Continuous Fractional Component Monte Carlo (CFCMC) simulations. We then compared the results with the available experimental data and predictions from two previously developed force fields. For the reactive NO–(NO)2 system, we performed CFCMC simulations in the reactive Gibbs ensemble in which the formation of NO dimers, 2NO ⇌ (NO)2, is considered. The predicted coexistence vapor–liquid densities, dimer mole fractions in the liquid phase, saturated vapor pressures, and heats of vaporization using our force field in the temperature range 120 K to 170 K are in excellent agreement with experimental values. In addition, we conducted a systematic parameter study to analyze the sensitivity of the new force field parameters and the isolated molecule partition functions of (NO)2 on the VLE properties of the reactive NO–(NO)2 system. The results indicate that the VLE properties of the reactive NO–(NO)2 system are affected by both the force field parameters of the involved species as well as the isolated molecule partition functions of (NO)2.

Methane pyrolysis is a promising route for low-emission hydrogen (H₂) production, with solid carbon as a potentially valuable byproduct. Despite this potential, the economic feasibility of Catalytic Methane Pyrolysis (CMP) with fluidized bed reactors (FBR) has been insufficiently studied. This study develops a conceptual CMP plant using two novel isothermal reactor models—based on continuous stirred-tank reactor (CSTR) and plug-flow reactor (PFR) assumptions—to represent the operational extremes of FBRs. Our reactor models incorporate reaction and catalyst deactivation kinetics from experiments with nickel-supported catalysts, and the framework enables process simulations that account for the catalyst rate required to sustain reactor activity. These models address the lack of proper reduced-order FBR models and the reliance on oversimplified assumptions in the literature. In the baseline scenario, the conceptual plant yields an LCOH ranging from $3.89 to $4.79 per kilogram, defining the expected cost bounds for an FBR-based CMP plant. At a H₂ selling price of $5.00 per kilogram, the process achieves favorable payback time and net present value. Monte Carlo and sensitivity analyses indicate that CMP remains cost-competitive under economic uncertainties. Increased carbon sales could make CMP more economical than steam methane reforming, while unsold byproducts may incur costly sequestration. Reactor heating assessment shows methane combustion with carbon capture minimizes both cost and emissions. Overall, this work demonstrates the economic potential of CMP for H₂ production and provides a practical modeling framework for process evaluation.

Group contribution methods (GCMs) provide a practical and computationally efficient approach for predicting thermodynamic properties of hydrocarbons, especially when experimental data are scarce. This review evaluates the evolution of GCMs from classical first-order schemes (e.g. Lydersen method, Joback method) to more advanced second-order frameworks (e.g. CG94, Sharma method), hybrid extensions, and emerging machine learning integrations. While first-order models are simple and widely used, these models struggle with branched and long-chain molecules. Second-order approaches significantly improve structural sensitivity and predictive accuracy, achieving deviations below 2–3% for critical properties and within 1 kcal/mol for formation enthalpies of branched alkanes. Nevertheless, challenges remain in extrapolating to highly complex molecules, underrepresented functional groups, and extreme conditions. Promising directions include reinforcement of second-order GCMs with molecular theory, systematic expansion of experimental and quantum-based datasets, and hybrid GCM–machine learning models that retain interpretability while improving generalisability. We recommend prioritising models that balance accuracy, robustness, simplicity, and transferability to accelerate sustainable process and product designs, particularly in applications such as fuel upgrading including hydroisomerisation, separation processes, and green chemical development.

Porous materials such as zeolites and Metal-Organic Frameworks are widely used for molecular separations based on adsorption and enthalpy/entropy characteristics. Ideal adsorption solution theory (IAST) predicts mixture adsorption behaviour on the basis of pure component isotherms of adsorbents in porous media. Mixture data at all mole fractions are required for breakthrough simulations. The use of IAST avoids the expensive computations of mixtures with Monte Carlo methods. Matching outcomes from computational physics studies to experimentally measurable properties is the foundation of the materials design pipeline. Here, we report the regression of an Invertible Autoencoder (IAE) for the forward and backward mapping of pure and mixture isotherms. The invertible autoencoder is defined as a soft-invertible neural network, which can be used as mapping function. Pure component isotherms are modelled using a 3-site Langmuir-Freundlich model, with a broad range of equilibrium pressure and heterogeneity factors. A synthetic dataset is generated from pure component isotherms and mixture isotherms calculated with RUPTURA. The IAE predicts pure and mixture isotherms with high precision over a large fugacity range, for up to 6 components and 3-site isotherms. This work contributes to inverting the full design pipeline from physical gas separation to adsorbate design, enabling property-guided materials discovery.

Computation of the excess entropy (Formula presented.) from the second-order density expansion of the entropy holds strictly for infinite systems in the limit of small densities. For the reliable and efficient computation of (Formula presented.) it is important to understand finite-size effects. Here, expressions to compute (Formula presented.) and Kirkwood–Buff (KB) integrals by integrating the Radial Distribution Function (RDF) in a finite volume are derived, from which (Formula presented.) and KB integrals in the thermodynamic limit are obtained. The scaling of these integrals with system size is studied. We show that the integrals of (Formula presented.) converge faster than KB integrals. We compute (Formula presented.) from Monte Carlo simulations using the Wang–Ramírez–Dobnikar–Frenkel pair interaction potential by thermodynamic integration and by integration of the RDF. We show that (Formula presented.) computed by integrating the RDF is identical to that of (Formula presented.) computed from thermodynamic integration at low densities, provided the RDF is extrapolated to the thermodynamic limit. At higher densities, differences up to (Formula presented.) are observed.

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).

Accurate prediction of thermodynamic properties of hydrocarbons is essential for chemical process modelling. Conventional group contribution methods often are used to predict these properties. However, these methods often require extensive parameter sets to handle structural complexities. A refined group contribution method for predicting thermodynamic properties of hydrocarbon isomers with reduced complexity and improved accuracy is presented and discussed. By combining the structural framework of Constantinou and Gani (CG94) with a sensitivity-based selection of second-order groups, a reduced yet highly effective set of twelve second-order groups is identified. This reduced set retains the predictive power comparable to more complex models while significantly reducing the number of parameters. Linear regression is applied to model enthalpies and Gibbs free energies of formation for a wide temperature range. To test broader applicability, the model is further extended to properties that require nonlinear regression, including critical temperatures, critical pressures, acentric factors, and liquid densities. For all cases, the proposed model achieves high predictive accuracy, demonstrating its robustness and generalizability. This methodology balances interpretability, efficiency, and performance, making it suitable for both research and industrial thermodynamic modelling.

Accurate conductivity predictions of KOH(aq) are crucial for electrolysis applications. OH– is transferred in water by the Grotthuss transfer mechanism, thereby increasing its mobility compared to that of other ions. Classical and ab initio molecular dynamics struggle to capture this enhanced mobility due to limitations in computational costs or in capturing chemical reactions. Most studies to date have provided only qualitative descriptions of the structure during Grotthuss transfer, without quantitative results for the transfer rate and the resulting transport properties. Here, machine learning molecular dynamics is used to investigate 50,000 transfer events. Analysis confirmed earlier works that Grotthuss transfer requires a reduction in accepted and a slight increase in donated hydrogen bonds to the hydroxide, indicating that hydrogen-bond rearrangements are rate-limiting. The computed self-diffusion coefficients and electrical conductivities are consistent with experiments for a wide temperature range, outperforming classical interatomic force fields and earlier AIMD simulations.

Hydrate-based H2 storage is based on the mechanism of trapping H2 in water-based structures that are environmentally friendly and cost-efficient. Understanding the effects of common promoters on hydrate-based H2 storage at the molecular level is crucial for designing efficient storage systems, and for discovering novel promoters. Here, a series of μs-scale molecular dynamics simulations are performed to investigate the nucleation of binary H2 hydrates from gas-liquid two-phase solutions in the presence of various promoters, i.e., CH4, CO2, C2H6, C3H8, C5H10, and THF. The simulation results indicate that the H2 and promoter molecules first dissolve in water from the gas phase and then are absorbed on the cage-faces, promoting the nucleation and growth of binary H2 hydrates. THF is the most effective promoter for hydrate-based H2 storage, exhibiting high performance in converting H2 from the gas phase to hydrates. It is followed by CH4, C2H6, and CO2; C3H8 and C5H10 molecules are less effective H2 hydrate promoters. The presence of large promoter molecules enhances multi-occupied cage formation. The molecular insight into the nucleation of binary H2 hydrates with various promoters provided here not only contributes to a broader understanding of hydrate-based H2 storage but is expected to motivate further experimental and computational studies.

One of the most promising energy carriers for transport applications are hydrogen-based energy carriers. NaBH₄ is a hydrogen energy carrier and produces hydrogen bubbles when it is dissolved in water. The formation of hydrogen bubbles hinders experimental measurements of the thermodynamic and transport properties of aqueous NaBH₄ solutions at elevated temperatures. Accurate knowledge of these properties is essential for the NaBH₄ hydrolysis reactor modeling and design. Molecular dynamics (MD) simulations provide the option to study the thermodynamic and transport properties of NaBH₄ aqueous solutions without hindering hydrogen bubble formation. In this work, a new force field is developed for BH₄^-, namely, the Delft force field of BH₄^- (DFF/BH₄^-), which, combined with additional force fields, can accurately describe experimental densities and viscosities of 0 to 5 m (mol salt/kg water) NaBH₄, 0 to 3 m NaB(OH)₄, and 1 m NaOH aqueous solutions at 295 K within 1.8% and 10.8% maximum deviation, respectively. Empirical fitting correlations are created for densities, viscosities, and self-diffusivities obtained from the MD simulations of 0 to 5 m NaBH₄, 0 to 5 m NaB(OH)₄, and 0 to 1 m NaOH aqueous solutions at 295-363 K for NaBH₄ hydrolysis reactor modeling and design purposes.

Experimental screening of Metal Organic Frameworks (MOFs) for separation applications can be costly and time-consuming. Computational methods can provide many benefits in this process, as expensive compounds and a wide range of operating conditions can be tested while crucial mechanistic insights are gained. TAMOF-1, a recently developed MOF, stands out for its exceptional stability, robustness and cost-effective synthesis. Its good CO2 uptake capacity makes it a promising agent for flue gas separation applications. In this work, we combine experiments with simulations at the atomistic and numerical level to investigate the adsorption and separation of CO2 and N2. Using Monte Carlo simulations, we accurately reproduce experimental adsorption isotherms and elucidate the adsorption mechanisms. TAMOF-1 effectively separates CO2 from N2 because of preferential binding sites near Cu2+ atoms. To assess separation performance in equilibrium at different conditions along the entire isotherm pressure range, adsorbed mole fractions, selectivities, and the trade-off between selectivity and uptake (TSN) are calculated. The dynamic separation performance is assessed by breakthrough experiments and numerical simulations, demonstrating efficient dynamic separation of CO2 and N2, with CO2 being retained in the column.

Heat pumps, which recycle waste heat, are a promising technology for reducing CO₂ emissions. Efficiently using low-grade waste heat remains challenging due to the limitations of standard heat exchangers and the need for more effective working fluids. This work introduces a multi-scale methodology that combines force field-based Monte Carlo simulations, quantum mechanics, and equations of state to explore the potential of formic acid as a new reactive fluid in thermodynamic cycles. Formic acid exhibits dimerization behavior, forming cyclic dimers in the gas phase, which can enhance the thermodynamic efficiency of heat recovery systems. The dimerization reaction of formic acid is crucial because it integrates chemical energy into thermodynamic processes, potentially improving the performance of heat pumps and other energy systems. The study implements umbrella sampling in Monte Carlo simulations to compute the thermodynamic properties of HCOOH dimerization, including equilibrium constants, enthalpy, and entropy. Results from two different methods to study dimer formation, namely the dimer counter method and the potential of mean force method, show strong agreement with the enthalpy of dimerization of −60.46 kJ mol⁻¹ and −62.91 kJ mol⁻¹, and entropy of −137.36 J mol⁻¹K⁻¹ and −146.98 J mol⁻¹K⁻¹, respectively. A very good agreement of the Monte Carlo results with Quantum Mechanics and experimental data validates the accuracy of the simulations. For phase equilibrium properties, the Peng–Robinson equation of state, coupled with advanced mixing rules, was applied and compared to Monte Carlo simulations in the Gibbs ensemble. This approach enabled the determination of the Global Phase Equilibrium of the system, vaporization enthalpy, phase composition, vapor and liquid densities of the coexisting phases, and entropy as a function of temperature. The agreement between the thermodynamic model and Monte Carlo simulations confirms the reliability of the methodology in capturing the phase behavior of the system. The findings demonstrate a promising approach for discovering and characterizing new reactive fluids, contributing to more efficient and sustainable energy technologies.

Cyclopentyl methyl ether (CPME) is a promising green solvent due to its eco-friendly properties; it is produced by adding methanol (MeOH) to cyclopentene. Separation of the resulting product mixture containing CPME and MeOH is critical, and it requires vapor-liquid equilibrium (VLE) data. In this work, isobaric VLE data were measured experimentally using an ebulliometer in a 60.0–101.3 kPa pressure range for a binary system of CPME + MeOH. VLE data were modeled using excess Gibbs (G (Formula presented.)) energy-based models such as Wilson, NRTL, and UNIQUAC. The formation of an azeotrope was analyzed. Flash point, surface tension, Gibbs adsorption, and thickness of surface layer were estimated using the Wilson model, which can help in determining molecule interaction and overall behavior of the system. Atmospheric and high-pressure distillation columns were designed using Aspen Plus to study the separation of CPME + MeOH, and an economic evaluation of the same was carried out.

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 the days prior to the Thermodynamics2024 conference in Delft (The Netherlands), the annual RASPA workshop/school took place at Delft University of Technology with 55 participants (both industry and academics) from all over the world. RASPA is a popular open-source molecular simulation software package for studying adsorption and diffusion in fluids and nanoporous materials, and it is especially popular in the metal-organic frameworks and zeolite communities. The main contributors to RASPA (and organisers of the RASPA workshop/school in Delft) are David Dubbeldam, Sofia Calero, Randall Q. Snurr, and Thijs J.H. Vlugt. In this short paper, we briefly explain the history of RASPA and the RASPA workshops, as well as our strategy to teach the workshop participants how to use RASPA for their specific research projects.

Molecular-based equations of state for describing the thermodynamics of chain molecules are often based on mean-field like arguments that reduce the problem of describing the interactions between chains to a simpler one involving only nonbonded monomers. While for dense liquids such arguments are known to work well, at low density they are typically less appropriate due to an incomplete description of the effect of chain connectivity on the local environment of the chains’ monomer segments. To address this issue, we develop three semi-empirical approaches that significantly improve the thermodynamic description of chain molecules at low density. The approaches are developed for chain molecules with repulsive intermolecular forces; therefore, they could be used as reference models for developing equations of the state of real fluids based on perturbation theory. All three approaches are extensions of Wertheim’s first-order thermodynamic perturbation theory (TPT1) for polymerization. The first model, referred to as TPT1-v, incorporates a second-virial correction that is scaled to zero at liquid-like densities. The second model, referred to as TPT1-y, introduces a Helmholtz-energy contribution to account for correlations between next-nearest-neighbor segments within chain molecules. The third approach, called TPT-E, directly modifies TPT1 without utilizing an additional Helmholtz energy contribution. By employing TPT1 at the core of these approaches, we ensure an accurate description of mixtures and enable a seamless extension from chains of tangentially bonded hard-sphere segments of equal size to hetero-segmented chains, fused chains, and chains of soft repulsive segments (which are influenced by temperature). The low-density corrections implemented in TPT1 are designed to preserve these good characteristics, as confirmed through comparisons with novel molecular simulation results for the pressure of various chain fluids. TPT1-v exhibits excellent transferability across different chain types, but it relies on knowing the second virial coefficient of the chain molecules, which is non-trivial to obtain and determined here using Monte Carlo simulation. The TPT1-y model, on the other hand, achieves comparable accuracy to TPT1-v while being fully predictive, requiring no input besides the geometry of the chain molecules.

The series of metal–organic frameworks M-MOF-74 gained popularity in the field of capture and separation of CO₂ due to the presence of numerous, highly reactive open-metal sites. The description of effective interactions between guest molecules and open-metal sites without accounting for polarization effects is challenging but it can significantly reduce the computational cost of simulations. In this study, we propose a non-polarizable force field for CO₂, and H₂ adsorption in M-MOF-74 (M = Ni, Cu, Co, Fe, Mn, Zn) by scaling the Coulombic interactions of M-MOF-74 atoms, and Lennard-Jones interaction potentials between the center of mass of H₂ and the open-metal centers. The presented force field is based on UFF and DREIDING parameters, characterized by high transferability and efficiency. The quantum behavior of H₂ at cryogenic temperatures is considered by incorporating Feynman–Hibbs quantum corrections. To validate the force field, the experimental isotherms of CO₂ at 298 K and 10⁻¹ – 10²kPa, the isotherms of H₂ at 77 K and 10⁻⁵ – 10²kPa, the corresponding enthalpy of adsorption, and the binding geometries in the M-MOF-74 series were reproduced using Monte Carlo simulations in the grand-canonical ensemble. The computed loadings, heats of CO₂ and H₂ adsorption, and binding geometries in M-MOF-74 are in very good agreement with the experimental values. The temperature transferability of the force field from 77 K to 87 K, and 298 K was shown for adsorption of H₂. The validated force field was used to study the adsorption and separation of CO₂/H₂ mixtures at 298 K. The adsorption of H₂ practically does not occur when CO₂ is present in the mixture. As indicated from simulated breakthrough curves, the breakthrough time of CO₂ in M-MOF-74 follows the same order as the uptake and the heat of CO₂ adsorption: Ni ¿ Co ¿ Fe ¿ Mn ¿ Zn ¿ Cu. Increasing the feed mole fraction of CO₂ in the breakthrough simulations from 0.1 to 0.9 speeds up the saturation of the adsorbent, leading to a faster exit of CO₂ with the column effluent. The application of the non-polarizable force field allows full investigation of the capture and separation of CO₂ in M-MOF-74, and can be expanded to study multi-component mixtures or industrial reactions in future research.

The use of reactive working fluids in thermodynamic cycles is currently being considered as an alternative to inert working fluids, because of the preliminarily attested higher energy-efficiency potential. The current needs to simulate their use in thermodynamic cycles, which may operate in liquid, vapour or vapour-liquid state, are an accurate real-fluid equation of state and ideal gas thermochemical properties of each molecule constituting the mixture, to calculate the equilibrium constant. To this end, the appeal to a multi-scale theoretical methodology is paramount and its definition represents the objective of the present work. This methodology is applied and validated on the system N₂O₄ ⇌ 2NO₂. Firstly, the equations solved for simultaneous two-phase and reaction equilibrium are presented. Secondly, ideal gas thermochemical properties of N₂O₄ and NO₂ are computed at atomic scale by quantum mechanics simulations. Then, to apply the selected cubic equation of state, pure-component properties of the species forming the reactive mixture (critical point coordinates and acentric factor) are required as input. However, these properties are not measurable, since NO₂ and N₂O₄ do not exist in nature as pure components. To get around this difficulty, the methodology relies on molecular Monte Carlo simulations of the pure N₂O₄ and NO₂, as well as on the reactive N₂O₄ ⇌ 2NO₂, enabling the determination of those missing pure-component properties and thus the calculation, on a macroscopic scale, of the reactive mixture properties. Finally, the comparison of calculated mixture properties with available experimental data leads to validate the accuracy of the proposed methodology.

Linear regression (LR) is used to predict thermochemical properties of alkanes at temperatures (0–1000) K to study chemical reaction equilibria inside zeolites. The thermochemical properties of C1 until C10 isomers reported by Scott are used as training data sets in the LR model which is used to predict these properties for alkanes longer than C10 isomers. Second-order groups are used as independent variables which account for the interactions between the neighboring groups of atoms. This model accurately predicts Gibbs free energies, enthalpies, Gibbs free energies of formation, and enthalpies of formation for alkanes which exceeds the chemical accuracy of 1 kcal/mol and outperforms the group contribution methods developed by Benson et al., Joback and Reid, and Constantinou and Gani. Predictions from our model are used to compute the reaction equilibrium distribution of hydroisomerization of C10 and C14 isomers in MTW-type zeolite. Calculation of reaction equilibrium distribution inside zeolites also requires Henry coefficients of the isomers which can be computed using classical force field-based molecular simulations using the RASPA2 software for which we created an automated workflow. The reaction equilibrium distribution for C10 isomers obtained using the LR model and the training data set for this model are in very good agreement. The tools developed in this study will enable the computational study of hydroisomerization of long-chain alkanes (>C10).

Knowledge on the kinetics of gas hydrate dissociation in clay pores at static and dynamic fluid conditions is a fundamental scientific issue for improving gas production efficiency from hydrate deposits using thermal stimulation and depressurization respectively. Here, molecular dynamics simulations were used to investigate poly- and mono-crystalline methane hydrates in Na-montmorillonite clay nanopores. Simulation results show that hydrate dissociation is highly sensitive to temperature and pressure gradients, but their effects differ. Temperature changes increase thermal instability of water and gas molecules, leading to layer-by-layer dissociation from the outer surface. Under flow conditions, laminar flow predominates in nano-pores, and non-Darcy flow occurs due to clay-fluid interactions. Viscous flow disrupts hydrogen bonding at the hydrate surface, enhancing kinetic instability of water. Grain boundaries of polycrystalline hydrates are less stable compared to bulk phases and preferentially decompose, forming new dissociation fronts. This accelerates dissociation compared to monocrystalline hydrates. Fracture occurs at the grain boundaries of polycrystalline hydrate in the fluid, resulting in separate hydrate crystal grains. This fracture process further accelerates hydrate dissociation. In flow systems, methane nanobubbles form in fluid and readily transport with fluid flow. Unlike surface nanobubbles at static conditions, these liquid nanobubbles exhibit mobility. The findings of this study can contribute to a better understanding of the complex phase transition behavior of hydrate in confined environment, and provide theoretical support for improving production control technology.