fig3

Physics infused machine learning force fields for 2D materials monolayers

Figure 3. Comparison of the predicted atomistic force and the DFT counterpart of monolayer (A) GeSe and (B) PbTe. The testing dataset contains ~0.86 million and ~1.5 million points for GeSe and PbTe, respectively. We calculate the RMSE, MAE, and R2 of the ML model for the testing data, and only 10% of the data are shown in the plots. DFT: Density functional theory; MAE: mean absolute error; ML: machine learning; RMSE: root mean square errors.

Journal of Materials Informatics
ISSN 2770-372X (Online)
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