Publication Type:Journal Article
Source:Nature Communications, Nature Publishing Group, Volume 8, Number 1 (2017)
Keywords:Article, chemical structure, computer analysis, computer vision, crystal structure, data set, electrical property, electricity, electricity generation, electron microscopy, Experimental study, flexoelectricity, identification method, inorganic compound, Lead titanate, Machine learning, Methodology, modeling, molecular model, organolead compound, Polarization, quantitative analysis, Strontium, strontium titanate, Titanium dioxide, unclassified drug, vortex, vortex motion
Flexoelectricity refers to electric polarization generated by heterogeneous mechanical strains, namely strain gradients, in materials of arbitrary crystal symmetries. Despite more than 50 years of work on this effect, an accurate identification of its coupling strength remains an experimental challenge for most materials, which impedes its wide recognition. Here, we show the presence of flexoelectricity in the recently discovered polar vortices in PbTiO3/SrTiO3 superlattices based on a combination of machine-learning analysis of the atomic-scale electron microscopy imaging data and phenomenological phase-field modeling. By scrutinizing the influence of flexocoupling on the global vortex structure, we match theory and experiment using computer vision methodologies to determine the flexoelectric coefficients for PbTiO3 and SrTiO3. Our findings highlight the inherent, nontrivial role of flexoelectricity in the generation of emergent complex polarization morphologies and demonstrate a viable approach to delineating this effect, conducive to the deeper exploration of both topics. © 2017 The Author(s).
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