Ferroelectric Domain and Switching Dynamics in Curved In₂Se₃: First-Principles and Deep Learning Molecular Dynamics...

Area of Science:

• Materials Science
• Condensed Matter Physics
• Computational Materials Science

Background:

• Complex strain effects on material properties are understudied due to limited simulation methods.
• Ferroelectric materials exhibit domain structures sensitive to mechanical stress.

Purpose of the Study:

• To investigate the impact of bending, rippling, and bubbling on ferroelectric domains in In2Se3 monolayers.
• To explore the potential of strain engineering for manipulating ferroelectric polarization.

Main Methods:

• Density Functional Theory (DFT) simulations.
• Deep learning molecular dynamics (DLMD) simulations.
• Analysis of strain gradients and ferroelectric switching barriers.

Main Results:

• Complex strain induces automatic polarization reversal in In2Se3 monolayers, creating localized ferroelectric domains.
• Switching dynamics are dependent on curvature magnitude and temperature, following an Arrhenius-style relationship.
• Strain gradients effectively control the size and behavior of ferroelectric domains.

Conclusions:

• Deep learning offers a promising approach for cross-scale simulations of complex strain effects.
• Strain engineering provides a viable method for manipulating local polarization in ferroelectric materials.
• This work advances the understanding of mechanical control over ferroelectric properties.