Atomic-resolution imaging of magnetism via ptychographic phase retrieval

Atomic-scale characterization of spin textures in solids is essential for understanding and tuning properties of magnetic materials and devices. While high-energy electrons are employed for atomic-scale imaging of materials, they are insensitive to the spin textures. In general, the magnetic contribution to the phase of high-energy electron wave is 1000 times weaker than the electrostatic potential. Via accurate phase retrieval through electron ptychography, here we show that the magnetic phase can be separated from the electrostatic one, opening the door to atomic-resolution characterization of spin textures in magnetic materials and spintronic devices.Comment: 20 pages, 9 figure

Information limit of 15 pm achieved with bright-field ptychography

It is generally assumed that a high spatial resolution of a microscope requires a large numerical aperture of the imaging lens or detector. In this study, the information limit of 15 pm is achieved in transmission electron microscopy using only the bright-field disk (small numerical aperture) via multislice ptychography. The results indicate that high-frequency information has been encoded in the electrons scattered to low angles due to the multiple scattering of electrons in the objects, making it possible to break the diffraction limit of imaging via bright-field ptychography.Comment: 10 pages, 4 figure

Sub-nanometer-scale mapping of crystal orientation and depth-dependent structure of dislocation cores in SrTiO3

Accurate measurement of defect structures is hindered by complex atomic configuration and/or crystal tilt. Here, the authors realize sub-nanometer mapping of crystal tilt and deep-subangstrom resolution and depth-dependent imaging of dislocations

Deep-Learning-Based Drug–Target Interaction Prediction

Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug–drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs

Enhanced electric resistivity and dielectric energy storage by vacancy defect complex

The presence of uncontrolled defects is a longstanding challenge for achieving high electric resistivity and high energy storage density in dielectric capacitors. In this study, opposite to conventional strategies to suppress de- fects, a new approach, i.e. , constructing defects with deeper energy levels, is demonstrated to address the inferior resistivity of BiFeO 3 -based dielectric films. Deep-level vacancy complexes with high charge carrier activation energies are realized via deliberate incorporation of oxygen vacancies and bismuth vacancies in low-oxygen- pressure deposited films. This method dramatically increases the resistivity by ∼4 orders of magnitude and the breakdown strength by ∼150%, leading to a ∼460% enhancement of energy density (from 14 to 79 J cm − 3 ), as well as improved efficiency and performance reliability. This work reveals the significance of rational design and precise control of defects for high-performance dielectric energy storage. The deep-level vacancy complex approach is generalizable to wide ranges of dielectric systems and functional applications.National Research Foundation (NRF)Submitted/Accepted versionThis work was supported by the Natural Science Foundation of China (NSFC) via the Basic Science Center Project grant 51788104, NSFC Grants 51532003, 51790490, 52072209 and 1729201. X.R.W. acknowledges supports from the Singapore National Research Foundation (NRF) under the Competitive Research Programs (CRP Grant No. NRF-CRP21–2018–0003). J.L.MD. would like to thank the Royal Academy of Engineering grant CIET 1819_24