Nanoscale Investigation of Local Thermal Expansion at SrTiO3 Grain Boundaries by Electron Energy Loss Spectroscopy.

The presence of grain boundaries (GBs) has a great impact on the coefficient of thermal expansion (CTE) of polycrystals. However, direct measurement of local expansion of GBs remains challenging for conventional methods due to the lack of spatial resolution. In this work, we utilized the valence electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM) to directly measure the CTE of Σ5 and 45°GBs of SrTiO3 at a temperature range between 373 and 973 K. A CTE that was about 3 times larger was observed in Σ5 GB along the direction normal to GB plane, while only a 1.4 time enhancement was found in the 45° GB. Our result provides direct evidence that GBs contribute to the enhancement of CTE in polycrystals. Also, this work has revealed how thermodynamic properties are varied in different GB structures and demonstrated the potential of EELS for probing local thermal properties with nanometer-scale resolution.

Current-Driven Motion of Domain Boundaries between Skyrmion Lattice and Helical Magnetic Structure.

To utilize magnetic skyrmions, nanoscale vortex-like magnetic structures, experimental elucidation of their dynamics against current application in various circumstances such as in confined structure and mixture of different magnetic phases is indispensable. Here, we investigate the current-induced dynamics of the coexistence state of magnetic skyrmions and helical magnetic structure in a thin plate of B20-type helimagnet FeGe in terms of in situ real-space observation using Lorentz transmission electron microscopy. Current pulses with various heights and widths were applied, and the change of the magnetic domain distribution was analyzed using a machine-learning technique. The observed average driving direction of the two-magnetic-state domain boundary is opposite to the applied electric current, indicating ferromagnetic s-d exchange coupling in the spin-transfer torque mechanism. The evaluated driving distance tends to increase with increasing the pulse duration time, current density (>1 × 109 A/m2), and sample temperature, providing valuable information about hitherto unknown current-induced dynamics of the skyrmion-lattice ensemble.

Real-Space Observation of Short-Period Cubic Lattice of Skyrmions in MnGe.

Three-dimensional forms of skyrmion aggregate, such as a cubic lattice of skyrmions, are anticipated to exist, yet their direct observations remain elusive. Here, we report real-space observations of spin configurations of the skyrmion-antiskyrmion cubic-lattice in MnGe with a very short period (∼3 nm) and hence endowed with the largest skyrmion number density. The skyrmion lattices parallel to the {100} atomic lattices are directly observed using high-resolution Lorentz transmission electron microscopes, simultaneously with underlying atomic-lattice fringes.

Unraveling the Stability of Layered Intercalation Compounds through First-Principles Calculations: Establishing a Linear Free Energy Relationship with Aqueous Ions.

Layered intercalation compounds, where atoms or molecules (intercalants) are inserted into layered materials (hosts), hold great potential for diverse applications. However, the lack of a systematic understanding of stable host-intercalant combinations poses challenges in materials design due to the vast combinatorial space. In this study, we performed first-principles calculations on 9024 compounds, unveiling a novel linear regression equation based on the principle of hard and soft acids and bases. This equation, incorporating the intercalant ion formation energy and ionic radius, quantitatively reveals the stability factors. Additionally, employing machine learning, we predicted regression coefficients from host properties, offering a comprehensive understanding and a predictive model for estimating the intercalation energy. Our work provides valuable insights into the energetics of layered intercalation compounds, facilitating targeted materials design.

Prediction of the Ground-State Electronic Structure from Core-Loss Spectra of Organic Molecules by Machine Learning.

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states at the excited state and is a powerful analytical technique to investigate local atomic and electronic structures of materials. However, various molecular properties governed by the ground-state electronic structure of the occupied orbital cannot be directly obtained from the core-loss spectra. Here, we constructed a machine learning model to predict the ground-state carbon s- and p-orbital PDOS in both occupied and unoccupied states from the C K-edge spectra. We also attempted an extrapolation prediction of the PDOS of larger molecules using a model trained by smaller molecules and found that the extrapolation prediction performance can be improved by excluding tiny molecules. Besides, we found that using smoothing preprocess and training by specific noise data can improve the PDOS prediction for noise-contained spectra, which pave a way for the application of the prediction model to the experimental data.

Simulated carbon K edge spectral database of organic molecules.

Here we provide a database of simulated carbon K (C-K) edge core loss spectra of 117,340 symmetrically unique sites in 22,155 molecules with no more than eight non-hydrogen atoms (C, O, N, and F). Our database contains C-K edge spectra of each carbon site and those of molecules along with their excitation energies. Our database is useful for analyzing experimental spectrum and conducting spectrum informatics on organic materials.

Automatic determination of the spectrum-structure relationship by tree structure-based unsupervised and supervised learning.

Spectroscopy is widely used for the analysis of chemical, vibrational, and bonding information. Interpretations of the spectral features have been performed by comparing the objective spectra with reference spectra from experiments or simulations. However, the interpretation process by humans is not always straightforward, especially for spectra obtained from unknown or new materials. In the present study, we developed a method using machine learning techniques to obtain human-like interpretation automatically. We combined unsupervised and supervised learning methods; then applied it to the spectrum database which includes more than 400 spectra of water and organic molecules containing various ligands and chemical bonds. The proposed method has successfully found the correlations between the spectral features and descriptors of the atoms, bonds, and ligands. We demonstrated that the proposed method enabled the automatic determination of reasonable spectrum-structure relationships such as between π* resonance in C-K edges and multiple bonds. The proposed method enables the automatic determination of physically and chemically reasonable spectrum-structure relationships without arbitrariness in data-driven manner, which is considerably difficult only with simulation or conventional machine leaning techniques. Such relationships are useful for understanding what structural parameters cause changes in the spectrum, providing a way for the better interpretation of spatial distributed or time evolutionary data. Furthermore, although the present work focused on the ELNES/XANES spectrum from small organic molecules, the proposed method can be readily extended to other spectral data. It is expected to contribute to a better understanding of the spectrum-structure relationship in various spectroscopy applications.

Dataset on structure and physical properties of stable diatomic systems based on van der Waals density functional method.

With the influence of progress in the materials informatics, development of fundamental database has been attracting growing interest. The bonding between atoms is essential component of all kinds of materials and govern their structure, stability, and properties. When we try to understand a material by breaking it down into microscopic components, bonding of diatomic system is the most fundamental. In the field of spectroscopy, diatomic molecular spectroscopy data has been studied well, and the diatomic molecular spectroscopy database [1] has been constructed recently. Concerning electronic structure, however, there is no easily accessible database of diatomic system. In order to develop a database of diatomic systems, it is important to consider adequate interaction. In addition to covalent bonding, van der Waals (vdW) interaction is also known to play an essential role especially in describing weak bonding systems such as noble gas dimers, atomic or molecular absorption, and layered materials. Thus, vdW interaction must be considered to develop database of diatomic systems so that it can be used for general purposes. One of its theoretical implementations is vdW density functional (vdW-DF) method [2], which has been developed within the framework of density functional theory 3 (DFT) and has been showing its effectiveness as general-purpose method. In this data article, we provide a vdW-DF-based calculation dataset focusing on diatomic systems. All diatomic systems containing atoms from H (Z = 1) to Ra (Z = 88) were considered, and stable structures and properties of more than 2,900 stable diatomic systems has been calculated correctly. This cyclopedic dataset of diatomic systems with consideration of vdW interaction can be useful building blocks for understanding, describing, and predicting interaction of atoms.

Controlled transformation of skyrmions and antiskyrmions in a non-centrosymmetric magnet.

Control of topological spin textures in magnetic systems may enable future spintronic applications. Magnetic field pulses can switch the vortex polarity1 or the winding number of magnetic bubbles2. Thermal energy can reverse the helicity of skyrmions3 and induce the transformation between meron and skyrmion by modifying the in-plane anisotropy4,5. Among the various topological spin textures, skyrmions6,7 and antiskyrmions8-10 are nanometric spin-whirling structures carrying integer topological charges (N) of -1 and +1 (refs. 7,11,12), respectively, and can be observed in real space8,13. They exhibit different dynamical properties under current flow14-18, for example, opposite signs for the topological Hall effect. Here we observe, in real space, transformations among antiskyrmions, non-topological (NT) bubbles and skyrmions (with N of +1, 0 and -1, respectively) and their lattices in a non-centrosymmetric Heusler magnet, Mn1.4Pt0.9Pd0.1Sn, with D2d symmetry. Lorentz transmission electron microscopy images under out-of-plane magnetic fields show a square lattice of square-shaped antiskyrmions near the Curie temperature and a triangular lattice of elliptically deformed skyrmions with opposite helicities at lower temperatures. The clockwise and counter-clockwise helicities of the skyrmions originate from Dzyaloshinskii-Moriya interactions with opposite signs along the [100] and [010] directions, respectively. A variation of the in-plane magnetic field induces a topological transformation from antiskyrmions to NT-bubbles and to skyrmions, which is accompanied by a change of the lattice geometry. We also demonstrate control of the helicity of skyrmions by variations of the in-plane magnetic field. These results showcase the control of the topological nature of spin configurations in complex magnetic systems.

Nanometric square skyrmion lattice in a centrosymmetric tetragonal magnet.

Magnetic skyrmions are topologically stable spin swirls with a particle-like character and are potentially suitable for the design of high-density information bits. Although most known skyrmion systems arise in non-centrosymmetric systems with a Dzyaloshinskii-Moriya interaction, centrosymmetric magnets with a triangular lattice can also give rise to skyrmion formation, with a geometrically frustrated lattice being considered essential in this case. Until now, it remains an open question if skyrmions can also exist in the absence of both geometrically frustrated lattice and inversion symmetry breaking. Here we discover a square skyrmion lattice state with 1.9 nm diameter skyrmions in the centrosymmetric tetragonal magnet GdRu2Si2 without a geometrically frustrated lattice by means of resonant X-ray scattering and Lorentz transmission electron microscopy experiments. A plausible origin of the observed skyrmion formation is four-spin interactions mediated by itinerant electrons in the presence of easy-axis anisotropy. Our results suggest that rare-earth intermetallics with highly symmetric crystal lattices may ubiquitously host nanometric skyrmions of exotic origins.

Current-induced dynamics of skyrmion strings.

Dynamics of string-like objects is an important issue in a broad range of physical systems, including vortex lines in superconductors, viscoelastic polymers, and superstrings in elementary particle physics. In noncentrosymmetric magnets, string forms of magnetic skyrmions are present as topological spin objects, and their current-induced dynamics has recently attracted intense interest. We show in the chiral magnet MnSi that the current-induced deformation dynamics of skyrmion strings results in transport response associated with the real-space Berry phase. Prominent nonlinear Hall signals emerge above the threshold current only in the skyrmion phase. We clarify the mechanism for these nonlinear Hall signals by adopting spin density wave picture to describe the moving skyrmion lattice; deformation of skyrmion strings occurs in an asymmetric manner due to the Dzyaloshinskii-Moriya interaction, which leads to the nonreciprocal nonlinear Hall response originating from an emergent electromagnetic field. This finding reveals the dynamical nature of string-like objects and consequent transport outcomes in noncentrosymmetric systems.