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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.
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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].
The identity and purity was confirmed by analysis of spectroscopic and mass spectrometric data and by comparing the melting point with literature values [20].
The glass transition temperature (T g ) and melting point (T m ) values were taken from the second round of heating.
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