Fig. 4: Efficiency of model fine-tuning. | npj Computational Materials

Fig. 4: Efficiency of model fine-tuning.

From: A pre-trained deep potential model for sulfide solid electrolytes with broad coverage and high accuracy

Fig. 4

a, b Energy and force predictions for the L2B2S5 (mp-29410) system before and after fine-tuning the model using 20 data frames. The comparison highlights the sample efficiency of fine-tuning versus training from scratch. c, d Mean absolute errors (MAEs) of energy and force predictions as a function of the number of training frames for the L2B2S5 system. For reference, the learning curve of the pre-trained DPA-2-MP universal force field fine-tuned using DPA-SSE dataset is also shown.

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