Phase-field modeling of domain evolution in ferroelectric materials in the presence of defects

Fedeli, Patrick; Kamlah, Marc; Frangi, Attilio

doi:10.1088/1361-665x/aafff8

Cited by 26 publications

(13 citation statements)

References 77 publications

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“…In contrast to the above-mentioned simulations, in this work we considered self-consistently a time-evolution of a self-organized domain structure coupled with the migration of mobile oxygen vacancies in an acceptor-doped tetragonal barium titanate without introduction of artificial excess charges. Among other effects, the formation of charged 180°-domain walls was established which might be due to the account of the electrostriction and flexoelectricity in contrast to the previous studies [33,78,79,80]. The development of the system may be divided in the following subsequent stages:…”

Section: Discussionmentioning

confidence: 87%

Hierarchy of domain reconstruction processes due to charged defect migration in acceptor doped ferroelectrics

2020

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UkraineEvolution of a stripe array of polarization domains triggered by the oxygen vacancy migration in an acceptor doped ferroelectric is investigated in a self-consistent manner. A comprehensive model based on the Landau-Ginzburg-Devonshire approach includes semiconductor features due to the presence of electrons and holes, and effects of electrostriction and flexoelectricity especially significant near the free surface and domain walls. A domain array spontaneously formed in the absence of an external field is shown to undergo a reconstruction in the course of the gradual oxygen vacancy migration driven by the depolarization fields. The charge defect accumulation near the free ferroelectric surface causes a series of phenomena: (i) symmetry breaking between the positive and negative cdomains, (ii) appearance of an effective dipole layer at the free surface followed by the formation of a surface electrostatic potential, (iii) tilting and recharging of the domain walls, especially pronounced at higher acceptor concentrations. An internal bias field determined by the gain in the free energy of the structure exhibits dependences of its amplitude on time and dopant concentration well comparable with available experimental results on aging in BaTiO 3 . † Corresponding

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Section: Discussionmentioning

confidence: 87%

Hierarchy of domain reconstruction processes due to charged defect migration in acceptor doped ferroelectrics

2020

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“…For example, in Ref. 33 other shapes of extended defects were investigated and found to have similar effect on E c .…”

Section: Resultsmentioning

confidence: 99%

Role of depolarization in the polarization reversal in ferroelectrics

2019

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Atomistic first-principles-based simulations are used to investigate polarization reversal in ferroelectrics in both the intrinsic and extrinsic regimes in order to determine the origin of nearly an order of magnitude difference in the coercive field predicted theoretically and observed in experiments. We find that the residual depolarizing field that is routinely ignored from considerations is responsible for the drastic reduction of the coercive field. The depolarizing field stabilizes a polydomain phase which allows for counterintuitive cooperation with the applied field to achieve polarization reversal in an energy efficient way. Contrary to the common belief that low coercive field necessitates polarization reversal via domains formation and propagation, we predict that the same fields could be achieved if the residual depolarizing field is taken into account. An efficient way to incorporate such depolarizing field in any type of atomistic simulations is proposed, which is expected to resolve the long-standing issue of overestimation of fields in simulations of ferroelectrics.

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“…For example, the prescribed voltage and mechanical displacements, such as the diameter and position of the tip, could be entered directly into the latent space between the encoder and the decoder. Finally, other extensions should be made to address the complexity of ferroelectric simulation, for example, the introduction of crystal and grain orientation, 31 charged defects, 31 and varying dielectric and elastic coefficients 25 are examples of potential improvements.…”

Section: ■ Conclusionmentioning

confidence: 99%

Machine Learning Surrogate Model for Acceleration of Ferroelectric Phase-Field Modeling

Alhada-Lahbabi

Deleruyelle

Gautier

2023

ACS Appl. Electron. Mater.

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Phase-field modeling is a powerful technique for predicting domain structure evolution and electromechanical properties of ferroelectric materials. However, it remains computationally very expensive, thus demanding high computing resources and restraining its use for exploring large systems. Some machine learning approaches have already been proposed to accelerate general phase-field simulations. Here, we present a specifically neural-network-trained model for ferroelectric phase-field modeling, including supervised and nonsupervised learning from Landau energy. A surrogate model predicts the microstructural polarization field evolution determining the electrostatic and mechanical equilibrium at each time step. The model produces accurate and stable rollout predictions, up to hundreds of frames even when starting from the beginning of the simulation. With a relative error contained below 5% compared to a high-fidelity phase field, we show that our model can be used instead of real solvers to conduct full simulations. While being at least 685 times faster than the classical phase-field computation, our approach opens a path to explore ferroelectric materials at a larger scale with fewer computing resources.

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Phase-field modeling of domain evolution in ferroelectric materials in the presence of defects

Cited by 26 publications

References 77 publications

Hierarchy of domain reconstruction processes due to charged defect migration in acceptor doped ferroelectrics

Hierarchy of domain reconstruction processes due to charged defect migration in acceptor doped ferroelectrics

Role of depolarization in the polarization reversal in ferroelectrics

Machine Learning Surrogate Model for Acceleration of Ferroelectric Phase-Field Modeling

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