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The final models were evaluated by training on the entire training data of 2746 molecules, and predicting the melting point value of the external test set.
Thus the surface effects in small samples could reduce the melting point value.
The observed melting point value (133 135°C) was also comparable with the reported melting point value of β-sitosterol (i.e. 135 136°C) [ 27].
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Cyclic PCL crystals (both crystallized from solution and from the melt) exhibited higher melting point values (Tm) than analogous linear PCL lamellae.
We use the Karthikeyan dataset [32] of melting point values as described in Nigsch et al. [33].
The effect was attributed to the fact that the density of points in the training set is less at the extremes of the range of melting point values.
For comparison with the WAAC results, we predicted the melting point values for the external test data using an RF model built on the training data.
Each model is built on the training set using whatever features and parameter values have been selected, and then used to predict the melting point values for the internal test set.
We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values.
However, looking at the test set predictions in the second column of Figures 3 and 6 it is clear, particularly for the RF model, that a systematic error occurs at the extremes of the melting point values in the dataset: low values are systematically overpredicted, while high values are underpredicted.
All melting point values are uncorrected and taken by open capillary method on a heating block Instrument.
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