Fig. 5: Machine learning model based on SRO parameters for energy prediction. | npj Computational Materials

Fig. 5: Machine learning model based on SRO parameters for energy prediction.

From: Competing sublattice short-range orders and gap state engineering in multicomponent transition-metal dichalcogenide

Fig. 5: Machine learning model based on SRO parameters for energy prediction.

a Pearson correlation coefficients between SRO parameters used as descriptors. b Performance of Random Forest model with SRO features up to first, second and third nearest neighbor shells. c Random Forest algorithm predictions in comparison with DFT-calculated energies for the test set in the model training. d Energy prediction using machine learning model based on Random Forest (RF) for the structures sampled in the DFT-MC simulation at 2800 K.

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