fig8

PhyMLP: an automated strategy for machine-learning potential construction via data fusion and adaptive point-sampling

Figure 8. Validation of the machine-learned tungsten potential for extended defects. (A) Formation energies of 1/2<111> and <100> interstitial dislocation loops as a function of size; (B) Surface energies for ten different crystallographic planes compared with DFT references; Generalized stacking fault energy curves for (C) 1/2<111>110 and (D) 1/2<111>112 slip systems. DFT: Density functional theory; SIAs: self-interstitial atoms.

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