Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures - JACS 2022 144 10785-10797

Aug 29, 2026

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Solubility of organic molecules is central to organic synthesis, industrial chemistry, and the design of phase separation and purification units, while also governing species migration into the environment. To select feasible solvents and temperatures, trial-and-error is often constrained by unknown solid solubility limits. This work presents a fast computational method to estimate solid neutral organic solubility across water and many organic solvents over broad temperature ranges, combining thermodynamic equations with machine learning for solvation free energy, enthalpy, Abraham parameters, and aqueous solid solubility at 298 K. Open tools and the SolProp dataset enable validation with 5000+ experimental values, yielding accurate predictions up to 550 K or higher, with strategies for further improvement using cross-solvent data or sublimation enthalpy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

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Abstract

Introduction

| | | | | | | | --- | --- | --- | --- | --- | --- | | [pubs.acs.org/JACS](pubs.acs.org/JACS) Article
Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures
Florence H. Vermeire, Yunsie Chung, and William H. Green *
Cite This: J. Am. Chem. Soc. 2022, 144, 10785−10797

Read Online | | | | | | | ACCESS | Metrics & More | | Article Recommendations | | *sı Supporting Information | 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. To decide which solvents and temperatures can be used in the design of new processes, trial and error is often used, as the choice is restricted by unknown solid solubility limits. Here, we present a fast and convenient computational method for estimating the solubility of solid neutral organic molecules in water and many organic solvents for a broad range of temperatures. The model is developed by combining fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K. We provide free open-source and online tools for the prediction of solid solubility limits and a curated data collection (SolProp) that includes more than 5000 experimental solid 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 550 K or higher. Methods to further improve solid solubility predictions by providing experimental data on the solute of interest in another solvent, or on the solute’s sublimation enthalpy, are also presented. ■ INTRODUCTION The solubility of solids in organic solvents plays a huge role in various chemical systems such as organic chemistry, environmental chemistry, organo(photo)catalysis, and (petro) -chemical industry. Solid−solution equilibria are, for example, used in the design of ﬂow batteries, puriﬁcation units, extraction units, and liquid-phase chemical processes. Speciﬁc to the pharmaceutical industry, the solubility of active pharmaceutical ingredients (API) in a variety of organic solvents is an important property in the development of new drugs. Many curated databases (e.g., the AqSol database 1) are available for the aqueous solid solubility of APIs as this property is of interest in the initial screening process of potential drugs. Further in the drug development chain during lab-scale synthesis, puriﬁcation, crystallization, and scale-up from batch to continuous processes, information on the solubility of the APIs in organic solvents other than water is required. Many methods exist for the prediction of the solubility of solids in speciﬁc organic solvents. A recently published review by Kuentz et al.2 summarizes recent advances in solubility predictions. The main limitation of existing methods is that they require physical properties of the solute, such as the fusion known in the enthalpy and/or melting temperature (e.g., the wellNRTL method3) or empirical solute parameters (e.g., relationships developed by Abraham et al.4). Other methods for the direct prediction of solid solubility are always limited to one or a few solvents. For example, the aqueous solid solubility is important in many applications, including the initial screening of potential APIs, and thus there are enough data available to train deep neural networks for the prediction of this property. Some examples of recent successful datadriven predictors for the aqueous solid solubility are AquaSol developed by Lusci et al.,5 the models of Cui et al.6 and by Boobier et al.,7 and SolTranNet developed by Francoeur and Koes.8 Despite the tremendous progress made in machine learning for molecular and material science,9 for the direct prediction of

FAQs

Why are predicting solid solubility limits important in process design?

Solid solubility limits restrict which solvents and temperatures can be used, affecting phase separation, purification, and migration of species into the environment. Using unknown limits forces reliance on trial-and-error.

What computational approach does the document propose for solubility prediction?

It combines fundamental thermodynamic equations with machine learning models for solvation free energy, solvation enthalpy, Abraham solute parameters, and aqueous solid solubility at 298 K.

What resources are provided to support model validation and use?

The paper provides free open-source and online tools, along with a curated open dataset (SolProp) containing more than 5000 experimental solid solubility values for validation.

Aug 29, 2026

Uploaded by Aurelia