Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

Effects of Ionic Liquids on the Nucleofugality of Dimethyl Sulfide

The nucleofugality of dimethyl sulfide was measured in solvent mixtures containing ionic liquids. The first-order rate constants of the solvolysis of sulfonium salts were determined in mixtures containing different proportions of 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide in ethanol, representing the first report on the solvolysis of a charged species in an ionic liquid. Temperature-dependent kinetic studies allowed determination of activation parameters and rationalization of observed solvent effects in different ionic liquid mixtures. From the solvolysis data, the nucleofugality of dimethyl sulfide in different proportions of this ionic liquid in ethanol was determined. Further, the nucleofugality of dimethyl sulfide was determined in mixtures containing high proportions of each of seven other ionic liquids in ethanol. These data allowed quantification of the effects of varying both the amount of ionic liquid present and on changing the components of the ionic liquid on the nucleofugality of dimethyl sulfide. The ionic liquid mixtures were shown to affect the nucleofugality of this nucleofuge in a different manner to the previously studied monatomic charged nucleofuges, owing to different microscopic interactions in solution. This work highlighted the necessity of considering electrofuges with an appropriate range of electrofugality values along with the importance of the nucleofuge-specific sensitivity parameter.

Effects of Ionic Liquids on the Nucleofugality of Bromide

The nucleofugality of bromide was measured in solvent mixtures containing ionic liquids. The solvolysis rate constants of the bromides of well-defined electrofuges were determined in mixtures containing different proportions of 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide in ethanol. Temperature-dependent kinetic studies allowed an explanation of the observed solvent effects in different mixtures in terms of interactions in solution. Using the solvolysis data, the nucleofugality of bromide in these systems was determined. Likewise, nucleofugality data for bromide were determined in mixtures containing high proportions of seven further ionic liquids. These data allowed quantification of the effects of both varying the amount of ionic liquid and the nature of ionic liquid components on the nucleofugality of bromide. Importantly, ionic liquid mixtures were shown to affect the nucleofugality in a manner similar to chloride, providing a method for predicting the effects of ionic liquids on other electrofuges. Further, the ionic liquids were shown to move the transition state earlier along the reaction coordinate, meaning that there is less charge development in the transition state.

Spontaneously Blinking Rhodamine Dyes for Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) has found extensive applications in various fields of biology and chemistry. As a vital component of SMLM, fluorophores play an essential role in obtaining super-resolution fluorescence images. Recent research on spontaneously blinking fluorophores has greatly simplified the experimental setups and extended the imaging duration of SMLM. To support this crucial development, this review provides a comprehensive overview of the development of spontaneously blinking rhodamines from 2014 to 2023, as well as the key mechanistic aspects of intramolecular spirocyclization reactions. We hope that by offering insightful design guidelines, this review will contribute to accelerating the advancement of super-resolution imaging technologies.

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure-property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH3, H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH3, H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy ( Fuel 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions.

The effects of ionic liquids on the ethanolysis of a chloroacenaphthene. Evaluation of the effectiveness of nucleofugality data to predict reaction outcome

The reaction of a chlorobenzene in mixtures containing ethanol and eight different ionic liquids was investigated in order to understand the effects of varying proportions and constituent ions of an ionic liquid on the rate constant of the process. The results were found to be generally consistent with previously studied reactions of the same type, with small proportions of an ionic liquid resulting in a rate constant increase compared to ethanol and large proportions causing a rate constant decrease. Temperature dependent kinetic studies were used to interpret the changes in reaction outcome, particularly noting an entropic cost on moving to high proportions of ionic liquid, consistent with organisation of solvent around the transition state. While attempts to use empirical solvent parameters to correlate outcome with the ionic liquid used were unsuccessful, use of recently acquired nucleofugality data for chloride and estimations for the electrofuge allowed for excellent prediction of the effects of ionic liquids, with rate constants quantitatively predicted in systems containing both different proportions of ionic liquid (mean absolute error (MAE) log(k₁) = 0.11) and different ionic liquids (MAE log(k₁) = 0.33). Importantly, this demonstrates the ready application of these quantitative reactivity parameters.

Fast Predictions of Reaction Barrier Heights: Toward Coupled-Cluster Accuracy

Quantitative estimates of reaction barriers are essential for developing kinetic mechanisms and predicting reaction outcomes. However, the lack of experimental data and the steep scaling of accurate quantum calculations often hinder the ability to obtain reliable kinetic values. Here, we train a directed message passing neural network on nearly 24,000 diverse gas-phase reactions calculated at CCSD(T)-F12a/cc-pVDZ-F12//ωB97X-D3/def2-TZVP. Our model uses 75% fewer parameters than previous studies, an improved reaction representation, and proper data splits to accurately estimate performance on unseen reactions. Using information from only the reactant and product, our model quickly predicts barrier heights with a testing MAE of 2.6 kcal mol^-1 relative to the coupled-cluster data, making it more accurate than a good density functional theory calculation. Furthermore, our results show that future modeling efforts to estimate reaction properties would significantly benefit from fine-tuning calibration using a transfer learning technique. We anticipate this model will accelerate and improve kinetic predictions for small molecule chemistry.

Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures

Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.

Progress towards machine learning reaction rate constants

Quantum and classical reaction rate constant calculations come at the cost of exploring potential energy surfaces. Due to the "curse of dimensionality", their evaluation quickly becomes unfeasible as the system size grows. Machine learning algorithms can accelerate the calculation of reaction rate constants by predicting them using low cost input features. In this perspective, we briefly introduce supervised machine learning algorithms in the context of reaction rate constant prediction. We discuss existing and recently created kinetic datasets and input feature representations as well as the use and design of machine learning algorithms to predict reaction rate constants or quantities required for their computation. Amongst these, we first describe the use of machine learning to predict activation, reaction, solvation and dissociation energies. We then look at the use of machine learning to predict reactive force field parameters, reaction rate constants as well as to help accelerate the search for minimum energy paths. Lastly, we provide an outlook on areas which have yet to be explored so as to improve and evaluate the use of machine learning algorithms for chemical reaction rate constants.

Effects of Ionic Liquids on the Nucleofugality of Chloride

The nucleofugality of chloride has been measured in solvent mixtures containing ionic liquids for the first time, allowing reactivity in these solvents to be put in context with molecular solvents. Using well-described electrofuges, solvolysis rate constants were determined in mixtures containing different proportions of ethanol and the ionic liquid 1-butyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide; the different solvent effects observed as the mixture changed could be explained using interactions of the ionic liquid with species along the reaction coordinate, determined using temperature dependent kinetic studies. The solvolysis data allowed determination of the nucleofugality of chloride in these mixtures, which varied with the proportion of salt in the reaction mixture, demonstrating quantitatively the importance of the amount of ionic liquid in the reaction mixture in determining reaction outcome. Nucleofugality data for chloride were determined in seven further ionic liquids, with the reactivity shown to vary over more than an order of magnitude. This outcome illustrates that the components of the ionic liquid are critical in determining reaction outcome. Overall, this work quantitatively extends the understanding of solvent effects in ionic liquids and demonstrates the potential for such information to be used to rationally select an ionic liquid to control reaction outcome.

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

Rapid relaxation NMR measurements to predict rate coefficients in ionic liquid mixtures. An examination of reaction outcome changes in a homologous series of ionic liquids

A series of ionic liquids based on the 1-alkyl-3-methylimidazolium cations were examined as components of the solvent mixture for a bimolecular substitution process. The effects on both the rate coefficient of the process and the NMR spin-spin relaxation of the solvent components of changing either the alkyl chain length or the amount of ionic liquid in the reaction mixture were determined. At a constant mole fraction, a shorter alkyl chain length resulted in a greater rate coefficient enhancement and a longer relaxation time, with the opposite effects for a longer alkyl chain length. For a given ionic liquid, increasing the proportion of salt in the reaction mixture resulted in a greater rate coefficient and a shorter relaxation time. The microscopic origins of the rate coefficient enhancement were determined and a step change found in the activation parameters on increasing the alkyl chain length from hexyl to octyl, suggesting notable structuring in solution. Across a range of ionic liquids and solvent compositions, the relaxation time from NMR measurements was shown to relate to the reaction rate coefficient. The approach of using fast and simple NMR relaxation measurements to predict reaction outcomes was exemplified using a morpholinium-based ionic liquid.

Targeted modifications in ionic liquids - from understanding to design

Ionic liquids are extremely versatile and continue to find new applications in academia as well as industry. This versatility is rooted in the manifold of possible ion types, ion combinations, and ion variations. However, to fully exploit this versatility, it is imperative to understand how the properties of ionic liquids arise from their constituents. In this work, we discuss targeted modifications as a powerful tool to provide understanding and to enable design. A 'targeted modification' is a deliberate change in the structure of an ionic liquid. This includes chemical changes in an experiment as well as changes to the parameterisation in a computer simulation. In any case, such a change must be purposeful to isolate what is of interest, studying, as far as is possible, only one concept at a time. The concepts can then be used as design elements. However, it is often found that several design elements interact with each other - sometimes synergistically, and other times antagonistically. Targeted modifications are a systematic way of navigating these overlaps. We hope this paper shows that understanding ionic liquids requires experimentalists and theoreticians to join forces and provides a tool to tackle the difficult transition from understanding to design.