Fig. 4: Scaling law observed in the Flory–Huggins χ parameter prediction task. | npj Computational Materials

Fig. 4: Scaling law observed in the Flory–Huggins χ parameter prediction task.

From: Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions

Fig. 4

a Scaling behavior when increasing the simulation dataset size. The horizontal axis represents the number of polymer--solvent pairs used as the simulation dataset, and the vertical axis shows the average MAE of 100 independent trials with 90% confidence interval calculated via bootstrapping. The dashed line is the estimated power-law with the estimated equation given at the bottom left, and the horizontal red line indicates the average MAE for direct learning without pretraining. b Scaling behaviors across different sizes of experimental data, and c scaling to increase the experimental dataset for different simulation dataset sizes. Each line shows the average MAE over 100 trials.

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