Fig. 3 | npj Computational Materials

Fig. 3

From: Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

Fig. 3

Performance of forward prediction models. a, b Five-fold cross-validation of trained linear models for glass transition temperature (Tg) and melting temperature (Tm). All predicted values in the five validation sets are plotted against observed values, denoted by blue dots (red for the training). The mean absolute error (MAE), root mean square error (RMSE) and correlation coefficient (R) are shown in each plot. c, d Validation results for the prediction model on λ that exhibited the best transferability (MAE = 0.0204 W/mK) out of 1000 pre-trained models on Tm. The prediction results of the best transferred model and a random forest model trained directly using the 28 data points for λ (MAE = 0.0327 W/mK) are shown in c, d respectively

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