Three-dimensional imaging of vortex structure in a ferroelectric nanoparticle driven by an electric field.

Topological defects of spontaneous polarization are extensively studied as templates for unique physical phenomena and in the design of reconfigurable electronic devices. Experimental investigations of the complex topologies of polarization have been limited to surface phenomena, which has restricted the probing of the dynamic volumetric domain morphology in operando. Here, we utilize Bragg coherent diffractive imaging of a single BaTiO3 nanoparticle in a composite polymer/ferroelectric capacitor to study the behavior of a three-dimensional vortex formed due to competing interactions involving ferroelectric domains. Our investigation of the structural phase transitions under the influence of an external electric field shows a mobile vortex core exhibiting a reversible hysteretic transformation path. We also study the toroidal moment of the vortex under the action of the field. Our results open avenues for the study of the structure and evolution of polar vortices and other topological structures in operando in functional materials under cross field configurations.Imaging of topological states of matter such as vortex configurations has generally been limited to 2D surface effects. Here Karpov et al. study the volumetric structure and dynamics of a vortex core mediated by electric-field induced structural phase transition in a ferroelectric BaTiO3 nanoparticle.

Classification of ABO3 perovskite solids: a machine learning study.

We explored the use of machine learning methods for classifying whether a particular ABO3 chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, the A and B ionic radii relative to the radius of O, and the bond valence distances between the A and B ions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2-3 percentage points over using any one pair. We also included the Mendeleev numbers of the A and B atoms to this set of feature pairs. Doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.

Effect of interfacial octahedral behavior in ultrathin manganite films.

We investigate structural coupling of the MnO6 octahedra across a film/substrate interface and the resultant changes of the physical properties of ultrathin La2/3Sr1/3MnO3 (LSMO) films. In order to isolate the effect of interfacial MnO6 octahedral behavior from that of epitaxial strain, LSMO films are grown on substrates with different symmetry and similar lattice parameters. Ultrathin LSMO films show an increased magnetization and electrical conductivity on cubic (LaAlO3)0.3(Sr2AlTaO6)0.7 (LSAT) compared to those grown on orthorhombic NdGaO3 (NGO) substrates, an effect that subsides as the thickness of the films is increased. This study demonstrates that interfacial structural coupling can play a critical role in the functional properties of oxide heterostructures.