With additional user information on the solid solubility of the solute in one organic solvent at room temperature, the accuracy of the method can be improved. The main limitation of the model is that the predictions are limited to neutral solvents in the liquid phase and neutral solutes in the solid phase. The proposed method could be extended to the solubility of ions in organic solvents, for example for applications in nonaquous redox flow batteries. Many correlations and machine learning models are combined to calculate the solid solubility in organic solvents at different temperatures. An overview of the different models, and the inputs and outputs to the method are given in Figure . We use thermodynamics to relate the solid solubility of solutes in organic solvents to properties for which plenty of experimental data are available, i.e. (i) the aqueous solid solubility log(S aq ), (ii) the gas-solution solvation free energy ∆G solv , and (iii) the gas-solution solvation enthalpy ∆H solv . Besides the solid solubility, the model and online tool also provide values for other thermodynamic properties that are used in the calculation of the solid solubility, for example thermodynamic properties related to the solubility of gases (solvation free energy and enthalpy, gas-solution equilibrium coefficients), related to the partitioning of solutes between solutions (dry partitioning coefficient), and related to solid state phase transitioning (sublimation enthalpy and heat capacity at 298K). All datasets that are constructed as part of this work are made publicly available in the SolProp data collection.