机器学习势在铁电材料研究中的应用

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机器学习势在铁电材料研究中的应用

刘仕, 黄佳玮, 武静

Application of Machine Learning Force Fields for Modeling Ferroelectric Materials

LIU Shi, HUANG Jiawei, WU Jing

图10 铁电相HfO₂的极化反转路径^[128]

Fig.10 Polarization reversal paths in the ferroelectric phase HfO₂^[128]
(a) vibrational modes of

$X_{2}^{-}$

crystals in a single cell of Pca2₁, with outwardly and inwardly shifted oxygen atoms indicated by purple and brown-orange spheres, respectively, and polarization along the z-axis
(b) Pca2₁ has alternating arrangements of O^np and O^p, with gray shaded regions marking the polar regions
(c) schematic representation of the shift in (SI) and shift across (SA) polarization reversal paths driven by an external electric field (ε). In the SA path, the O^np ion

$X_{2}^{-}$

sign is reversed (indicated in grey during the transition). The SI and SA paths can start from the same structure, represented by P vectors in opposite directions (green arrows) to ensure compatibility with classical electrodynamics
(d) calculated results of the minimum energy paths for different polarization reversal paths, lines indicate the results of the DFT, and scatters indicate the results of the deep neural network-based force field prediction (λ represents the polariza-tion state (polar-nonpolar-polar))
(e) polarization reversal energy barrier versus electric field strength (|ε |)