Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures

Abstract

The solubility of organic molecules is crucial in organic synthesis and industrial chemistry, it is important in the design of many phase separation and purification units, and it controls the migration of many species into the environment. Solubility limits force chemists and engineers to change the solvent or adjust the temperature of their process, often by trial and error. Here we present a fast and convenient computational method for estimating the solubility of any neutral organic molecule in water and any organic solvent at any temperature. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solubility at 298K. We provide free open-source and online tools for the prediction of solubility limits and a curated data collection (SolProp) that includes more than 5,000 experimental solubility values for validation of the model. The model predictions are accurate for aqueous systems and for a huge range of organic solvents up to 550K or higher. Methods to further improve solubility predictions by providing experimental data on the solute of interest in any solvent at any temperature, or on the solute’s sublimation enthalpy, are also presented.

Solution Processing of Cross-Linked Porous Organic Polymers

“…The latest theories and mechanisms for predicting and controlling solution dispersibility should be developed from the endeavors of theoretical chemists. 54 (2) New concepts and mechanisms for solution processing CPOPs need to be developed. For instance, is it possible to develop an unprecedented mechanism to obtain the solution-like nanosheet sol systems beyond the ionization methods?…”

Concluding remarks: Faraday Discussion on unimolecular reactions

“…The predicted solvent effects are accurate enough ($0.5 kcal mol À1 errors, comparable to errors in careful calculations or measurements of gas phase E a 's) to give some hope that this may be the right approach. We have had excellent success predicting solvation of molecules, [21][22][23][24] even close to the critical point of the solvent, 21 so it is perhaps not surprising that we can also compute the solvation energy of transition states. Another interesting environmental effect is radiation.…”

Prediction of Organic Compound Aqueous Solubility Using Interpretable Machine Learning- A Comparison Study of Descriptor-Based and Topological Models

“…Our database is a curated collection of the aqueous solubilities of organic compounds from three literature-based large databases, including Vermeire's (11804 datapoints) [17], Boobier's (901 datapoints) [1], and Delaney's (1145 datapoints) [12]. This dataset was prepared by omitting the non-unique measures and noisy data, consisting of more than one solubility measure for a single molecule, yielding a total of 8,438 unique data points.…”