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• Computational modeling of molecular separation using adsorption process. • Machine learning modeling to evaluate the adsorption capability of boron nitride. • Decision tree, Multilayer Perceptron, and Linear Regression are developed. • The model's validation is performed via comparing with measured data. A computational modeling framework was developed to evaluate the adsorption behavior of boron nitride (BN) for separation of a solute from solution. Adsorption of an organic dye onto the surface of different porous BNs at two different temperatures was computed using different machine learning models. The amount of dye concentration changes in the solution based on the time parameter for different materials has been modeled and validated in this research. An attempt was made to construct the kinetics of adsorption using the developed models rather than the conventional kinetic models. A decision tree (DT), Multilayer Perceptron (MLP), and Linear Regression boosted with Adaboost (ADA-LR) are selected models that are optimized by their hyper-parameters using a grid-search. Models were developed separately for low and high temperatures for three different materials with different chemistry and structures. Using the MSE parameter, DT has an error of 1.44E-02 for low temperatures and 1.48E-02 for high temperatures. It is 1.01E-03 for low and 7.44E-03 for high temperatures using MLP method of simulation. However, in terms of MSE, the best model is ADA-LR, with 4.60E-03 for low temperature and 1.81E-03 for high temperature. With other metrics also, ADA-LR is the best model, so it is selected as the main model of this research. The R2-score of the final model is 0.940 for low and 0.973 for high temperature values. [ABSTRACT FROM AUTHOR]

• Advanced molecular-level modeling of drug delivery system. • Study on encapsulation Boron Nitride cluster with alkaline metals. • Comparisons of pure and functionalized BN for drug delivery. • Metformin was considered as the model drug. Finding new drug-delivery materials has attracted considerable interest from many researchers in recent years. Herein, systematic investigation of the metformin interaction with the surface of pristine B 12 N 12 and group I metals (Li, Na, and K) encapsulation nanoclusters was carried out at B3LYP/631++ (d, p) level of theory based on the DFT calculations. MF molecule has two nucleophilic sites, NH and NH 2 groups. The trapping of Li, Na, and K atoms affected the HOMO–LUMO gaps of the considered configurations and the electronic properties of considered complexes. Besides, it is noticed that presence of alkali metals remarkably increased the absorption energies of MF-B 12 N 12 to −2.14, −2.24, −2.30, and −2.38 for B 12 N 12 , Li@B 12 N 12 , Na@B 12 N 12 , and K@B 12 N 12 respectively. In addition, DOS plots show a decrease in E gap by the addition of alkali metals and therefore an increase in reactivity of the considered configurations, which is confirmed by the decrease in total hardness. [ABSTRACT FROM AUTHOR]