Predicting melting point of ionic liquids using QSPR approach: Literature review and new models

Prediction and Interpretability of Melting Points of Ionic Liquids Using Graph Neural Networks

Ionic liquids (ILs) have wide and promising applications in fields such as chemical engineering, energy, and the environment. However, the melting points (MPs) of ILs are one of the most crucial properties affecting their applications. The MPs of ILs are affected by various factors, and tuning these in a laboratory is time-consuming and costly. Therefore, an accurate and efficient method is required to predict the desired MPs in the design of novel targeted ILs. In this study, three descriptor-based machine learning (DBML) models and eight graph neural network (GNN) models were proposed to predict the MPs of ILs. Fingerprints and molecular graphs were used to represent molecules for the DBML and GNNs, respectively. The GNN models demonstrated performance superior to that of the DBML models. Among all of the examined models, the graph convolutional model exhibited the best performance with high accuracy (root-mean-squared error = 37.06, mean absolute error = 28.79, and correlation coefficient = 0.76). Benefiting from molecular graph representation, we built a GNN-based interpretable model to reveal the atomistic contribution to the MPs of ILs using a data-driven procedure. According to our interpretable model, amino groups, S+, N+, and P+ would increase the MPs of ILs, while the negatively charged halogen atoms, S-, and N- would decrease the MPs of ILs. The results of this study provide new insight into the rapid screening and synthesis of targeted ILs with appropriate MPs.

Unexpected Energy Applications of Ionic Liquids

Ionic liquids and their various analogues are without doubt the scientific sensation of the last few decades, paving the way to a more sustainable society. Their versatile suite of properties, originating from an almost inconceivably large number of possible cation and anion combinations, allows tuning of the structure to serve a desired purpose. Ionic liquids hence offer a myriad of useful applications from solvents to catalysts, through to lubricants, gas absorbers, and azeotrope breakers. The purpose of this review is to explore the more unexpected of these applications, particularly in the energy space. It guides the reader through the application of ionic liquids and their analogues as i) phase change materials for thermal energy storage, ii) organic ionic plastic crystals, which have been studied as battery electrolytes and in gas separation, iii) key components in the nitrogen reduction reaction for sustainable ammonia generation, iv) as electrolytes in aluminum-ion batteries, and v) in other emerging technologies. It is concluded that there is tremendous scope for further optimizing and tuning of the ionic liquid in its task, subject to sustainability imperatives in line with current global priorities, assisted by artificial intelligence.

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.

COSMO-RS guided screening of ionic liquids for the separation of fluorinated greenhouse gases R-410A: Delving into anion, cation effects, and hydrogen bond dynamics

Reclamation of high-GWP near-azeotropic refrigerant R-410A (50 wt% R-32 (difluoromethane) + 50 wt% R-125 (pentafluoroethane)) can be an effective way to mitigate the greenhouse effect and achieve a circular economy. Efficient ionic liquids (ILs) as extractants needed to be found for the extractive distillation (ED) separation process of R-410A. Given the numerous combinations of cations and anions in ILs, the discovery of an efficient IL via experimental methods proves to be an exceedingly complex task. In this work, the solubilities of R-32, and R-125 in 840 conventional ILs (comprised of 20 cations and 42 anions) were analyzed based on infinite dilution activity coefficient. The absorption mechanisms of R-32 and R-125 in ILs were elucidated by analyzing excess enthalpy (HE), excess Gibbs free energy (GE)) and surface charge density distribution through COSMO-RS (Conductor-like Screening Model for Real Solvents). Results revealed that HE and GE of the mixture formed by R-125 and most ILs surpassed those of R-32, resulting in higher solubility of R-32 in most ILs compared to R-125. Structural changes of anions and cations had a greater effect on the solubility of R-125 in ILs. It is found for the first time that the existence of a strong hydrogen bond donor region in cations/anions generated intense repulsion with the hydrogen atom in R-125. Furthermore, a large area of weak polarity on the surface of cations/anions was difficult to form an effective charge shield with fluorine atoms in R-125, thus inhibiting the dissolution of R-125. Finally based on the identified interaction sites, combined with melting point and viscosity, some novel functional ILs with high selectivity for R-32 + R-125 were designed and determined for actual separation process. These findings significantly enrich the understanding of the solubility mechanism and provide theoretical guidance for designing new ILs for R-410A reclamation.

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure-property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure-property models are discussed.

The Curious Case of 1-Ethylpyridinium Triflate: Ionic Liquid Exhibiting the Mpemba Effect

Here, we report the results of qualitative and quantitative investigations of the first-order phase transition in the ionic liquid 1-ethylpyridinium triflate exhibiting a high variability of temperature ranges, within which the freezing and melting occur. By two methods, the direct fast quenching/annealing and the slow temperature-controlled differential scanning calorimeter, it is revealed that despite the almost constant absolute enthalpies of phase transition, the freezing occurs faster with the larger temperature contrast (cooling rate) between the initially hotter sample and the colder surrounding. This feature is a clear exhibition of the Mpemba effect. The regularity in the change of the melting point is analyzed as well.

Machine Learning-Boosted Design of Ionic Liquids for CO2 Absorption and Experimental Verification

Efficient CO₂ capture is indispensable for achieving a carbon-neutral society while maintaining a high quality of life. Since the discovery that ionic liquids (ILs; room-temperature molten salts) can absorb CO₂, various solvents composed of molecular ions have been studied. However, it is challenging to observe the properties of each isolated ion component to control the function of ILs as they are mixtures of ions. Finding the optimal cation-anion combination for the CO₂ absorbent from their enormous chemical space had been impossible in a practical sense. This study applied electronic structure informatics to explore ILs with high CO₂ solubility from 402,114 IL candidates. The feature variables were determined by a set of cheap quantum chemistry calculations for isolated small-ion fragments, and the importance of molecular geometries and electronic states governing molecular interactions was identified via the wrapper method. As a result, it was clearly shown that the electronic states of ionic species must have essential roles in the CO₂ physisorption capacity of ILs. Considering synthetic easiness for the candidates narrowed by the machine learning model, trihexyl(tetradecyl)phosphonium perfluorooctanesulfonate was synthesized. Using a magnetic suspension balance, it was experimentally confirmed that this IL has higher CO₂ solubility than trihexyl(tetradecyl)phosphonium bis(trifluoromethanesulfonyl)amide, which is the previous best IL for CO₂ absorption.

Development of a gliclazide ionic liquid and its mesoporous silica particles: an effective formulation strategy to improve oral absorption properties

Ionic liquid (IL) technology provides a useful platform to enhance the oral absorption of therapeutic agents. In the present work, gliclazide (GLI), a second-generation sulfonylurea drug was transformed into an IL with tetrabutylphosphonium. The physicochemical properties of this IL were systematically characterized by DSC, TGA, FT-IR, NMR, and HPLC. For the further preparation development, a solution stability test was conducted. GLI-based IL could improve the solution stability in a neutral environment. To assess oral potential, the solubility characteristics including equilibrium solubility, 24 h kinetic saturation solubilities and supersaturation profiles were first explored. Significant enhancement of solubilities, supersaturation ratio and duration of supersaturation was found for the synthesized IL. Computational methodology was utilized to better understand the improved solubility results. From the simulated results, [TBP][GLI] showed a longer time period when the distance between cation and anion was far above the baseline and a higher deviation degree, indicating less stable ion pairs of [TBP][GLI] in an aqueous environment and it being easy for the cation and anion to tear apart and form interactions with water molecules. The prepared [TBP][GLI] exhibited intestinal transportation ability and safety as evidenced by the in vitro gastrointestinal tract artificial membrane permeability assays (GIT-PAMPA) and cytotoxicity experiments with Caco-2 cells. A mesoporous carrier, AEROPERL® 300 Pharma, was chosen to load the IL and then encapsuled into enteric capsules. The prepared oral capsules containing GLI-based IL loaded mesoporous silica particles released fast and could realize 100% release within 60 min.