arXiv subjects

Yuan-Jie Sun

Publications and source records attributed to Yuan-Jie Sun.

3 recordsLinked to original sources

arXiv2026

Polar nanoregions and reentrant-like ferroelectric behavior in SrTiO$_3$

Recent real-space imaging in quantum paraelectric SrTiO$_3$ [Nature 656, 54 (2026)] reveals that local polar textures do not continuously grow upon cooling, but reach a maximum intensity at intermediate temperatures around 60-65 K and weaken again toward the quantum paraelectric ground state. Such a reentrant-like weakening of local polar textures challenges the conventional paradigm in which ordering tendencies generally continue to strengthen as temperature decreases. Here we employ a self-consistent phase-field theory showing that this anomalous behavior naturally arises from the coupling between intrinsic polar and antiferrodistortiv (AFD) fluctuations. We demonstrate that the flexoelectric-like coupling strongly hybridizes the polar and AFD modes. As the uncoupled polar and AFD modes cross near 52 K, their hybridization is maximized, driving the lower hybridized branch to develop a minimum on a finite-wave-vector shell. This finite-$q$ softening triggers a Brazovskii-type instability, strongly enhancing polarization correlations and producing nanoscale polar textures. Away from the crossing temperature, the two modes become increasingly detuned, weakening their hybridization and the associated finite-$q$ softening. These results reaveal the origin of the formation of polar nanoregions in SrTiO$_3$, naturally explaining the unexpected confinement to an intermediate-temperature window and providing a mechanism beyond the quantum-fluctuation-based interpretation suggested by experiment. Furthermore, we predict an unconventional reentrant-like sequence in weakly strained SrTiO$_3$, evolving from ferroelectric to paraelectric, polar-nanoregion, and eventually paraelectric regimes upon heating from zero temperature.

cond-mat.mtrl-sci↗

arXiv2026

Mesoscale Domain Evolution Mechanism during Alternating Current (AC) Poling of Relaxor Ferroelectrics

Ferroelectric domain variants that are energetically equivalent are expected to remain preserved during polarization reversal under a symmetry-preserving electric field. However, recent experiments on relaxor-ferroelectric crystals have revealed irreversible elimination of inclined domain walls during AC poling, while the underlying mesoscale mechanism remains unclear. Here, we investigate the domain-wall motion during AC poling of rhombohedral Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$--PbTiO$_3$ single crystals containing both 71$^\circ$ and 109$^\circ$ domain walls within a quasi-two-dimensional laminated geometry using phase-field simulations. The simulations reveal that the domain-wall behavior during polarization reversal depends on the spacing ratio between the 71$^\circ$ and 109$^\circ$ domain walls. Closely spaced 71$^\circ$ domain walls undergo irreversible elimination, whereas more widely separated walls are preserved, while the 109$^\circ$ domain walls remain intact. A threshold ratio for domain-wall elimination is identified and found to depend on the mechanical boundary conditions. By tracking the domain-wall trajectories during the switching process, we attribute this behavior to unsynchronized motion of neighboring 71$^\circ$ domain walls arising from long-range elastic interactions when the walls become strongly coupled. This collective motion breaks the symmetry between energetically equivalent domain variants and leads to irreversible domain-wall elimination during polarization reversal. These findings provide mechanistic insight into collective domain-wall evolution during polarization reversal and suggest that proximity-driven symmetry breaking may provide a mesoscale mechanism for domain engineering in ferroelectric materials with high domain-wall densities.

cond-mat.mtrl-sci↗

arXiv2021

A General Machine Learning-based Approach for Inverse Design of One-dimensional Photonic Crystals Toward Targeted Visible Light Reflection Spectrum

Data-driven methods have increasingly been applied to the development of optical systems as inexpensive and effective inverse design approaches. Optical properties (e.g., band-gap properties) of photonic crystals (PCs) are closely associated with characteristics of their light reflection spectra. Finding optimal PC constructions (within a pre-specified parameter space) that generate reflection spectra closest to a targeted spectrum is thus an interesting and meaningful inverse design problem, although relevant studies are still limited. Here we report a generally effective machine learning-based inverse design approach for one-dimensional photonic crystals (1DPCs), focusing on visible light spectra which are of high practical relevance. For a given class of 1DPC system, a deep neural network (DNN) in a unified structure is first trained over data from sizeable forward calculations (from layer thicknesses to spectrum). An iterative optimization scheme is then developed based on a coherent integration of DNN backward predictions (from spectrum to layer thicknesses), forward calculations, and Monte Carlo moves. We employ this new approach to four representative 1DPC systems including periodic structures with two-, three-, and four-layer repeating units and a heterostructure. The approach successfully converges to solutions of optimal 1DPC constructions for various targeted spectra regardless of their exact achievability. As two demonstrating examples, inverse designs toward a specially constructed "rectangle-shaped" green-light or red-light reflection spectrum are presented and discussed in detail. Remarkably, the results show that the approach can efficiently find out optimal layer thicknesses even when they are far outside the range covered by the original training data of DNN.

physics.optics↗