We present RASPA3, a molecular simulation code for computing adsorption and diffusion in nanoporous materials and thermodynamic and transport properties of fluids. It implements force field based classical Monte Carlo/molecular dynamics in various ensembles. In this article, we introduce the new additions and changes compared to RASPA2. RASPA3 is rewritten from the ground up in C++23 with speed and code readability in mind. Transition-matrix Monte Carlo is added to compute the density of states and free energies. The Monte Carlo code for rigid molecules is based on quaternions, and the atomic positions needed in the energy evaluation are recreated from the center of mass position and quaternion orientation. The expanded ensemble methodology for fractional molecules, with a scaling parameter λ between 0 and 1, now also keeps track of analytic expressions of dU/dλ, allowing independent verification of the chemical potential using thermodynamic integration. The source code is freely available under the MIT license on GitHub. Using this code, we compare four Monte Carlo (MC) insertion/deletion techniques: unbiased Metropolis MC, Configurational-Bias Monte Carlo (CBMC), Continuous Fractional Component MC (CFCMC), and CB/CFCMC. We compare particle distribution shapes, acceptance ratios, accuracy and speed of isotherm computation, enthalpies of adsorption, and chemical potentials, over a wide range of loadings and systems, for the grand canonical ensemble and for the Gibbs ensemble.

We present the RUPTURA code (https://github.com/iraspa/ruptura) as a free and open-source software package (MIT license) for (1) the simulation of gas adsorption breakthrough curves, (2) mixture prediction using methods like the Ideal Adsorption Solution Theory (IAST), segregated-IAST and explicit isotherm models, and (3) fitting of isotherm models on computed or measured adsorption isotherm data. The combination with the RASPA software enables computation of breakthrough curves directly from adsorption simulations in the grand-canonical ensemble. RUPTURA and RASPA have similar input styles. IAST is implemented near machine precision but we also provide several explicit mixture prediction methods that are non-iterative and potentially faster than IAST. The code supports a wide variety of isotherm models like Langmuir, Anti-Langmuir, BET, Henry, Freundlich, Sips, Langmuir-Freundlich, Redlich-Peterson, Toth, Unilan, O'Brian & Myers, Asymptotic Temkin, and Bingel & Walton. The isotherm model parameters can easily be obtained by the fitting module. Breakthrough plots and animations of the column properties are automatically generated. In addition to highlighting the code, we also review all the developed techniques from literature for mixture prediction, breakthrough simulations, and isotherm model fitting, and provide a tutorial discussing the workflows.

Ideal Adsorbed Solution Theory (IAST) is a common method for modelling mixture adsorption isotherms based on pure component isotherms. When the adsorbent has distinct adsorption sites, the segregated version of IAST (SIAST) provides improved adsorbed loadings compared to IAST. We have adopted the concept of SIAST and applied it to an explicit isotherm model which takes into account the different sizes of the adsorbates: the so called Segregated Explicit Isotherm (SEI). The purpose of SEI is to have an explicit adsorption model that can consider both size-effects of the co-adsorbed molecules and surface heterogeneities. In sharp contrast to IAST and SIAST, no iterative scheme is required in case of SEI, which leads to much faster simulations. A comparative study has been performed to analyse the adsorption isotherms calculated using these three methods. The adsorbed loadings predicted by SEI and SIAST are in excellent agreement with the Grand-Canonical Monte Carlo (GCMC) simulation data. The loadings estimated by IAST show considerable deviations from the GCMC data at high pressures. Breakthrough curve modelling is used to compare the effects of these three models at dynamic conditions. The explicit model (SEI) leads to the fastest simulation run time, followed by SIAST.