Flexoelectric and Piezoelectric Coupling in a Bended MoS2 Monolayer

Ab-initiotransport model to study the thermoelectric performance of MoS2, MoSe2, and WS2monolayers by using Boltzmann transport equation

The potential for thermoelectric applications of two-dimensional materials is quite promising. Usingab-initiocalculations, we have investigated the electronic band structure, phonon band structure, electronic density of states, and phonon density of states of monolayers MoS2, MoSe2, and WS2. In order to compute the thermoelectric properties of monolayers MoS2, MoSe2, and WS2, we used theab-initiomodel suggested by Faghaniniaet al(2015Phys. Rev.B91235123). Within this model, by using inputs from density functional theory and considering all relevant elastic and inelastic scattering mechanisms, we have calculated the thermoelectric properties of monolayers MoS2, MoSe2, and WS2over various ranges of temperature (T) and carrier concentration (n). The obtained results of Seebeck coefficients (S) and figure of merit (ZT) atT= 300 K for bothn/p-types of monolayers MoS2, MoSe2, and WS2are in good agreement with the findings obtained by other models using the Boltzmann transport equation within a constant relaxation time framework.

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

Despite its prevalence in experiments, the influence of complex strain on material properties remains understudied due to the lack of effective simulation methods. Here, the effects of bending, rippling, and bubbling on the ferroelectric domains are investigated in an In2Se3 monolayer by density functional theory and deep learning molecular dynamics simulations. Since the ferroelectric switching barrier can be increased (decreased) by tensile (compressive) strain, automatic polarization reversal occurs in α-In2Se3 with a strain gradient when it is subjected to bending, rippling, or bubbling deformations to create localized ferroelectric domains with varying sizes. The switching dynamics depends on the magnitude of curvature and temperature, following an Arrhenius-style relationship. This study not only provides a promising solution for cross-scale studies using deep learning but also reveals the potential to manipulate local polarization in ferroelectric materials through strain engineering.