Machine learning interatomic potential for molecular dynamics simulation of the ferroelectric <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>KNb</mml:mi><mml:msub><mml:mi mathvariant="normal">O</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math> perovskite

نویسندگان

چکیده

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials recently demonstrated to be an excellent candidate sustainable development. Molecular dynamics is a versatile theoretical calculation approach the investigation of dynamical properties ferroelectric perovskites. However, molecular simulation has limited simple systems, since conventional construction interatomic potential rather difficult and inefficient. In present study, we construct machine-learning KNbO3 (as representative system (K,Na)NbO3) by using deep neural network model. Including first-principles data into training dataset ensures quantum-mechanics accuracy potential. The based on shows good agreement with calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic phonon dispersion. addition, exhibits satisfactory performance domain wall temperature-dependent phase transition. machine learning could potentially transferred other consequently benefits study ferroelectrics.

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ژورنال

عنوان ژورنال: Physical review

سال: 2023

ISSN: ['0556-2813', '1538-4497', '1089-490X']

DOI: https://doi.org/10.1103/physrevb.107.014101