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Register a new userAtomic scale chemical fluctuation in LaSrVMoO6: A proposed halfmetallic antiferromagnet
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Half metallic antiferromagnets (HMAFM) have been proposed theoretically long ago but have not been realized experimentally yet. Recently, a double perovskite compound, LaSrVMoO6, has been claimed to be an almost real HMAFM system. Here, we report detailed experimental and theoretical studies on this compound. Our results reveal that the compound is neither a half metal nor an ordered antiferromagnet. Most importantly, an unusual chemical fluctuation is observed locally, which finally accounts for all the electronic and magnetic properties of this compound.
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Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces determine the strengthening capacities in high temperature. By means of high resolution scanning transmission electron microscopy (HRSTEM) and 3D atom probe (3DAP) tomography, Srinivasan et al. proposed a new point that in nickel base superalloys there exist two different interfacial widths across the {gamma}/{gamma} interface, one corresponding to an order-disorder transition, and the other to the composition transition. We argue about this conclusion in this comment.
The propagation of dislocations in random crystals is evidenced to be governed by atomic-scale avalanches whose the extension in space and the time intermittency characterizingly diverge at the critical threshold. Our work is the very first atomic-scale evidence that the paradigm of second order phase transitions applies to the depinning of elastic interfaces in random media.
Amorphous materials are coming within reach of realistic computer simulations, but new approaches are needed to fully understand their intricate atomic structures. Here, we show how machine-learning (ML)-based techniques can give new, quantitative chemical insight into the atomic-scale structure of amorphous silicon (a-Si). Based on a similarity function (kernel), we define a structural metric that unifies the description of nearest- and next-nearest-neighbor environments in the amorphous state. We apply this to an ensemble of a-Si networks, generated in melt-quench simulations with an ML-based interatomic potential, in which we tailor the degree of ordering by varying the quench rates down to $10^{10}$ K/s (leading to a structural model that is lower in energy than the established WWW network). We then show how machine-learned atomic energies permit a chemical interpretation, associating coordination defects in a-Si with distinct energetic stability regions. The approach is straightforward and inexpensive to apply to arbitrary structural models, and it is therefore expected to have more general significance for developing a quantitative understanding of the amorphous state.
The predictability of a certain effect or phenomenon is often equated with the knowledge of relevant physical laws, typically understood as a functional or numerically derived relationship between the observations and known states of the system. Correspondingly, observations inconsistent with prior knowledge can be used to derive new knowledge on the nature of the system or indicate the presence of yet unknown mechanisms. Here we explore the applicability of Gaussian Processes (GP) to establish predictability and uncertainty of local behaviors from multimodal observations, providing an alternative to this classical paradigm. Using atomic-resolution Scanning Transmission Electron Microscopy (STEM) of multiferroic Sm-doped BiFeO3 across a broad composition range, we directly visualize the atomic structure and structural, physical, and chemical order parameter fields for the material. GP regression is used to establish the predictability of the local polarization field from different groups of parameters, including the adjacent polarization values and several combinations of physical and chemical descriptors, including lattice parameters, column intensities, etc. We observe that certain elements of microstructure including charged and uncharged domain walls and interfaces with the substrate are best predicted with specific combinations of descriptors, and this predictability and their associated uncertainties are consistent across the composition series. The associated generative physical mechanisms are discussed. We argue that predictability and uncertainty in observational data offers a new pathway to probe the physics of condensed matter systems from multimodal local observations.
Imaging individual vacancies in solids and revealing their interactions with solute atoms remains one of the frontiers in microscopy and microanalysis. Here we study a creep-deformed binary Ni-2 at.% Ta alloy. Atom probe tomography reveals a random distribution of Ta. Field ion microscopy, with contrast interpretation supported by density-functional theory and time-of-flight mass spectrometry, evidences a positive correlation of tantalum with vacancies. Our results support solute-vacancy binding, which explains improvement in creep resistance of Ta-containing Ni-based superalloys and helps guide future material design strategies.
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