Fig. shows one of several different non-linear many-parameter fitting forms that have been used to correlate molecules or reactions with their properties. The form shown in Fig. is used in Chemprop , a very popular open-source "ML for Chemistry" software package (more than 100,000 downloads per month in early 2024). The Chemprop-type approach is very effective if one has a large amount of data (e.g. on >1,000 molecules or reactions). If one has a smaller data set, e.g. data on about 100 molecules, other correlation methods with fewer adjustable parameters may be more satisfactory, e.g. the Reaction Mechanism Generator software package uses trees to classify reactions . A key advantage of having a large dataset is that one can reserve a large "test set" as well as a "validation set" and still have a large amount of data remaining to train the estimator. The validation dataset is used to detect when the model is starting to overfit the training data, and so when one should stop the training procedure. The reserved test set then provides a realistic assessment of the accuracy of the estimates, albeit on molecules or reactions similar to those in the test set. Helpful tips are available on how best to do this fitting procedure and estimate the uncertainties in the predictions, with warnings about common pitfalls. Some properties depend on more than one molecule, e.g. solvation energy of a solute (Molecule 1) in a solvent (Molecule 2); that situation can be modeled as shown in Fig. . Slightly more complicated variations are required for reactions to maintain atom-mapping through the transition state or for properties where Molecule 1 and Molecule 2 play the same roles, so (Molecule 1, Molecule 2) should give exactly the same property value as (Molecule 2, Molecule 1). For predicting the properties of mixtures, one must also input the mole fractions of each molecule in the mixture.