Direct imaging of atomistic grain boundary migration.

Grain boundary (GB) migration plays an important role in modifying the microstructures and the related properties of polycrystalline materials, and is governed by the atomistic mechanism by which the atoms are displaced from one grain to another. Although such an atomistic mechanism has been intensively investigated, it is still experimentally unclear as to how the GB migration proceeds at the atomic scale. With the aid of high-energy electron-beam irradiation in atomic-resolution scanning transmission electron microscopy, we controllably triggered the GB migration in α-Al2O3 and directly visualized the atomistic GB migration as a stop motion movie. It was revealed that the GB migration proceeds by the cooperative shuffling of atoms on GB ledges along specific routes, passing through several different stable and metastable GB structures with low energies. We demonstrated that GB migration could be facilitated by the GB structural transformations between these low-energy structures.

Accurate prediction of grain boundary structures and energetics in CdTe: a machine-learning potential approach.

To accurately predict grain boundary (GB) atomic structures and their energetics in CdTe, the present study constructs an artificial-neural-network (ANN) interatomic potential. To cover a wide range of atomic environments, large amounts of density functional theory (DFT) data are used as a training dataset including point defects, surfaces and GBs. Structural relaxation combined with the trained ANN potential is applied to symmetric tilt and twist GBs, many of which are not included in the training dataset. The relative stability of the relaxed structures and their GB energies are then evaluated with the DFT level. The ANN potential is found to accurately predict low-energy structures and their energetics with reasonable accuracy with respect to DFT results, while conventional empirical potentials critically fail to find low-energy structures. The present study also provides a way to further improve the transferability of the ANN potential to more complicated GBs, using only low-Σ GBs as training datasets. Such improvement will offer a way to accurately predict atomic structures of general GBs within practical computational cost.

Photoindentation: A New Route to Understanding Dislocation Behavior in Light.

It was recently found that extremely large plasticity is exhibited in bulk compression of single-crystal ZnS in complete darkness. Such effects are believed to be caused by the interactions between dislocations and photoexcited electrons and/or holes. However, methods for evaluating dislocation behavior in such semiconductors with small dimensions under a particular light condition had not been well established. Here, we propose the "photoindentation" technique to solve this issue by combining nanoscale indentation tests with a fully controlled lighting system. The quantitative data analyses based on this photoindentation approach successfully demonstrate that the first pop-in stress indicating dislocation nucleation near the surface of ZnS clearly increases by light irradiation. Additionally, the room-temperature indentation creep tests show a drastic reduction of the dislocation mobility under light. Our approach demonstrates great potential in understanding the light effects on dislocation nucleation and mobility at the nanoscale, as most advanced technology-related semiconductors are limited in dimensions.

Crystal and Electronic Structures of MoSi2-Type CrGe2 Synthesized under High Pressure.

Chromium germanides, namely, Nowotny chimney-ladder-phase CrGe1.77 and MoSi2-type CrGe2, were synthesized above 15 GPa or more via laser heating using a diamond anvil cell (LHDAC). MoSi2-type CrGe2, which is the most Ge-rich compound in the Cr-Ge system, crystallizes in the tetragonal structure with a space group of I4/mmm (no. 139) and lattice parameters of a = 3.24919(6) Å and c = 8.0523(3) Å and is isostructural with MoSi2. MoSi2-type CrGe2 has a deep pseudogap caused by the splitting of 3d orbitals with Cr, as evidenced by ab initio calculation. In this article, we have succeeded in synthesizing a binary compound between transition-metal and metalloid elements for the first time at high pressures above 10 GPa using the LHDAC. This pathway opens the possibility to explore more compounds in this system and may provide new insights into the fundamental interaction between these two elements.

Direct Measurement of Electronic Band Structures at Oxide Grain Boundaries.

Grain boundaries (GBs) modulate the macroscopic properties in polycrystalline materials because they have different atomic and electronic structures from the bulk. Despite the progress on the understanding of GB atomic structures, knowledge of the localized electronic band structures is still lacking. Here, we experimentally characterized the atomic structures and the band gaps of four typical GBs in α-Al2O3 by scanning transmission electron microscopy and valence electron energy-loss spectroscopy (EELS). It was found that the band gaps of the GBs are narrowed by 0.5-2.1 eV compared with that of 8.8 eV in the bulk. By combing core-loss EELS with first-principles calculations, we elucidated that the band gap reductions directly correlate with the decrease of the coordination numbers of Al and O ions at the GBs. These results provide in-depth understanding between the local atomic and electronic band structures for GBs and demonstrate a novel electronic-structure analysis for crystalline defects.

Multicrystalline Informatics Applied to Multicrystalline Silicon for Unraveling the Microscopic Root Cause of Dislocation Generation.

A comprehensive analysis of optical and photoluminescence images obtained from practical multicrystalline silicon wafers is conducted, utilizing various machine learning models for dislocation cluster region extraction, grain segmentation, and crystal orientation prediction. As a result, a realistic 3D model that includes the generation point of dislocation clusters is built. Finite element stress analysis on the 3D model coupled with crystal growth simulation reveals inhomogeneous and complex stress distribution and that dislocation clusters are frequently formed along the slip plane with the highest shear stress among twelve equivalents, concentrated along bending grain boundaries (GBs). Multiscale analysis of the extracted GBs near the generation point of dislocation clusters combined with ab initio calculations has shown that the dislocation generation due to the concentration of shear stress is caused by the nanofacet formation associated with GB bending. This mechanism cannot be captured by the Haasen-Alexander-Sumino model. Thus, this research method reveals the existence of a dislocation generation mechanism unique to the multicrystalline structure. Multicrystalline informatics linking experimental, theoretical, computational, and data science on multicrystalline materials at multiple scales is expected to contribute to the advancement of materials science by unraveling complex phenomena in various multicrystalline materials.

Quantitative prediction of grain boundary thermal conductivities from local atomic environments.

Quantifying the dependence of thermal conductivity on grain boundary (GB) structure is critical for controlling nanoscale thermal transport in many technologically important materials. A major obstacle to determining such a relationship is the lack of a robust and physically intuitive structure descriptor capable of distinguishing between disparate GB structures. We demonstrate that a microscopic structure metric, the local distortion factor, correlates well with atomically decomposed thermal conductivities obtained from perturbed molecular dynamics for a wide variety of MgO GBs. Based on this correlation, a model for accurately predicting thermal conductivity of GBs is constructed using machine learning techniques. The model reveals that small distortions to local atomic environments are sufficient to reduce overall thermal conductivity dramatically. The method developed should enable more precise design of next-generation thermal materials as it allows GB structures exhibiting the desired thermal transport behaviour to be identified with small computational overhead.

Atomically ordered solute segregation behaviour in an oxide grain boundary.

Grain boundary segregation is a critical issue in materials science because it determines the properties of individual grain boundaries and thus governs the macroscopic properties of materials. Recent progress in electron microscopy has greatly improved our understanding of grain boundary segregation phenomena down to atomistic dimensions, but solute segregation is still extremely challenging to experimentally identify at the atomic scale. Here, we report direct observations of atomic-scale yttrium solute segregation behaviours in an yttria-stabilized-zirconia grain boundary using atomic-resolution energy-dispersive X-ray spectroscopy analysis. We found that yttrium solute atoms preferentially segregate to specific atomic sites at the core of the grain boundary, forming a unique chemically-ordered structure across the grain boundary.