Defect detection in atomic-resolution images via unsupervised learning with translational invariance

Crystallographic defects can now be routinely imaged at atomic resolution with aberration-corrected scanning transmission electron microscopy (STEM) at high speed, with the potential for vast volumes of data to be acquired in relatively short times or through autonomous experiments that can continue over very long periods. Automatic detection and classification of defects in the STEM images are needed in order to handle the data in an efficient way. However, like many other tasks related to object detection and identification in artificial intelligence, it is challenging to detect and identify defects from STEM images. Furthermore, it is difficult to deal with crystal structures that have many atoms and low symmetries. Previous methods used for defect detection and classification were based on supervised learning, which requires human-labeled data. In this work, we develop an approach for defect detection with unsupervised machine learning based on a one-class support vector machine (OCSVM). We introduce two schemes of image segmentation and data preprocessing, both of which involve taking the Patterson function of each segment as inputs. We demonstrate that this method can be applied to various defects, such as point and line defects in 2D materials and twin boundaries in 3D nanocrystals.

Laves phase crystal analysis (LaCA): Atomistic identification of lattice defects in C14 and C15 topologically close-packed phases

The identification of defects in crystal structures is crucial for the analysis of atomistic simulations. Many methods to characterize defects that are based on the classification of local atomic arrangement are available for simple crystalline structures. However, there is currently no method to identify both, the crystal structures and internal defects of topologically close-packed (TCP) phases such as Laves phases. We propose a new method, Laves phase crystal analysis (LaCA), to characterize the atomic arrangement in Laves crystals by interweaving existing structural analysis algorithms. The new method can identify the polytypes C14 and C15 of Laves phases, typical crystallographic defects in these phases, and common deformation mechanisms such as synchroshear and non-basal dislocations. Defects in the C36 Laves phase are detectable through deviations from the periodic arrangement of the C14 and C15 structures that make up this phase. LaCA is robust and extendable to other TCP phases. Graphic abstract

Twin-assisted cracking degradation in sodium layered oxide cathodes

Twinning defects often present in crystalline materials when are subject to mechanical stimuli and are mostly affecting their physicochemical properties. The twinning formation and twin-related degradation upon cycling in sodium layered oxides (SLOs) are poorly understood. Combining atomic-resolution imaging, spectroscopy and first principles calculations, we reveal that growth twinning is unexpectedly common in the SLO materials and the twin boundaries show distinct structural and chemical characters from those identified in lithium layered oxides. A unique O-P-O twinning plane was identified in the O3 type SLO materials. We discover that twin-assisted Na diffusion cause large volume variations and trigger intragranular fracture during electrochemical cycling. The present findings not only establish a robust correlation between growth twinning and intragranular cracking in SLOs, but also offer general implications for the development of high-performing intercalation electrode materials by regulating crystallographic defects.

Recent Developments in Lead and Lead-Free Halide Perovskite Nanostructures towards Photocatalytic CO2 Reduction

Perovskite materials have been widely considered as emerging photocatalysts for CO2 reduction due to their extraordinary physicochemical and optical properties. Perovskites offer a wide range of benefits compared to conventional semiconductors, including tunable bandgap, high surface energy, high charge carrier lifetime, and flexible crystal structure, making them ideal for high-performance photocatalytic CO2 reduction. Notably, defect-induced perovskites, for example, crystallographic defects in perovskites, have given excellent opportunities to tune perovskites’ catalytic properties. Recently, lead (Pb) halide perovskite and their composites or heterojunction with other semiconductors, metal nanoparticles (NPs), metal complexes, graphene, and metal-organic frameworks (MOFs) have been well established for CO2 conversion. Besides, various halide perovskites have come under focus to avoid the toxicity of lead-based materials. Therefore, we reviewed the recent progress made by Pb and Pb-free halide perovskites in photo-assisted CO2 reduction into useful chemicals. We also discussed the importance of various factors like change in solvent, structure defects, and compositions in the fabrication of halide perovskites to efficiently convert CO2 into value-added products.