Predicting water solubility in ionic liquids using machine learning towards design of hydro-philic/phobic ionic liquids

Ionic Liquids in Metal, Photo-, Electro-, and (Bio) Catalysis

Ionic liquids (ILs) have unique physicochemical properties that make them advantageous for catalysis, such as low vapor pressure, non-flammability, high thermal and chemical stabilities, and the ability to enhance the activity and stability of (bio)catalysts. ILs can improve the efficiency, selectivity, and sustainability of bio(transformations) by acting as activators of enzymes, selectively dissolving substrates and products, and reducing toxicity. They can also be recycled and reused multiple times without losing their effectiveness. ILs based on imidazolium cation are preferred for structural organization aspects, with a semiorganized layer surrounding the catalyst. ILs act as a container, providing a confined space that allows modulation of electronic and geometric effects, miscibility of reactants and products, and residence time of species. ILs can stabilize ionic and radical species and control the catalytic activity of dynamic processes. Supported IL phase (SILP) derivatives and polymeric ILs (PILs) are good options for molecular engineering of greener catalytic processes. The major factors governing metal, photo-, electro-, and biocatalysts in ILs are discussed in detail based on the vast literature available over the past two and a half decades. Catalytic reactions, ranging from hydrogenation and cross-coupling to oxidations, promoted by homogeneous and heterogeneous catalysts in both single and multiphase conditions, are extensively reviewed and discussed considering the knowledge accumulated until now.

Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications

The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.

Mapping the frontier orbital energies of imidazolium-based cations using machine learning

The knowledge of the frontier orbital, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), energies is vital for studying chemical and electrochemical stability of compounds, their corrosion inhibition potential, reactivity, etc. Density functional theory (DFT) calculations provide a direct route to estimate these energies either in the gas-phase or condensed phase. However, the application of DFT methods becomes computationally intensive when hundreds of thousands of compounds are to be screened. Such is the case when all the isomers for the 1-alkyl-3-alkylimidazolium cation [CnCmim]+ (n = 1-10, m = 1-10) are considered. Enumerating the isomer space of [CnCmim]+ yields close to 386 000 cation structures. Calculating frontier orbital energies for each would be computationally very expensive and time-consuming using DFT. In this article, we develop a machine learning model based on the extreme gradient boosting method using a small subset of the isomer space and predict the HOMO and LUMO energies. Using the model, the HOMO energies are predicted with a mean absolute error (MAE) of 0.4 eV and the LUMO energies are predicted with a MAE of 0.2 eV. Inferences are also drawn on the type of the descriptors deemed important for the HOMO and LUMO energy estimates. Application of the machine learning model results in a drastic reduction in computational time required for such calculations.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (K_d). Almost all quantitative structure-property relationship (QSPR) models that describe K_d apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (K_PE-w), PE-seawater (K_PE-sw), PVC-water (K_PVC-w) and PP-seawater (K_PP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting K_d more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with K_PE-w, K_PE-sw, K_PVC-w and K_PP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (R_adj²: 0.907-0.999), robustness (Q_BOOT²: 0.900-0.937) and predictability (R_ext²: 0.889-0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Solvent Screening for Solubility Enhancement of Theophylline in Neat, Binary and Ternary NADES Solvents: New Measurements and Ensemble Machine Learning

Theophylline, a typical representative of active pharmaceutical ingredients, was selected to study the characteristics of experimental and theoretical solubility measured at 25 °C in a broad range of solvents, including neat, binary mixtures and ternary natural deep eutectics (NADES) prepared with choline chloride, polyols and water. There was a strong synergistic effect of organic solvents mixed with water, and among the experimentally studied binary systems, the one containing DMSO with water in unimolar proportions was found to be the most effective in theophylline dissolution. Likewise, for NADES, the addition of water (0.2 molar fraction) resulted in increased solubility compared to pure eutectics, with the highest solubilisation potential offered by the composition of choline chloride with glycerol. The ensemble of Statistica Automated Neural Networks (SANNs) developed using intermolecular interactions in pure systems has been found to be a very accurate model for solubility computations. This machine learning protocol was also applied as an extensive screening for potential solvents with higher solubility of theophylline. Such solvents were identified in all three subgroups, including neat solvents, binary mixtures and ternary NADES systems. Some methodological considerations of SANNs applications for future modelling were also provided. Although the developed protocol is focused exclusively on theophylline solubility, it also has general importance and can be used for the development of predictive models adequate for solvent screening of other compounds in a variety of systems. Formulation of such a model offers rational guidance for the selection of proper candidates as solubilisers in the designed solvents screening.