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| 1 | A machine perspective of atomic defects in scanning transmission electron microscopy显示文摘Enabled by the advances in aberration-corrected scanning transmission electron microscopy(STEM),atomic-resolution real space imaging of materials has allowed a direct structure-property investigation.Traditional ways of quantitative data analysis suffer from low yield and poor accuracy.New ideas in the field of computer vision and machine learning have provided more momentum to harness the wealth of big data and sophisticated information in STEM data analytics,which has transformed STEM from a localized characterization technique to a macroscopic tool with intelligence.In this review article,we discuss the prime significance of defect topology and density in two-dimensional(2D)materials,which have proved to be a powerful means to tune a wide range of properties.Subsequently,we systematically review advanced data analysis methods that have demonstrated promising prospects in analyzing STEM data,particularly for identifying structural defects,with high throughput and veracity.A unified framework for atomic structure identification is also summarized. | Jiadong Dan Xiaoxu Zhao Stephen J.Pennycook | 2019 | InfoMat2019，1，3： | 5 |
| 2 | Emergent properties at oxide interfaces controlled by ferroelectric polarization显示文摘Ferroelectric materials are characterized by the spontaneous polarization switchable by the applied fields,which can act as a“gate”to control various properties of ferroelectric/insulator interfaces.Here we review the recent studies on the modulation of oxide hetero-/homo-interfaces by ferroelectric polarization.We discuss the potential applications of recently developed four-dimensional scanning transmission electron microscopy and how it can provide insights into the fundamental understanding of ferroelectric polarization-induced phenomena and stimulate future computational studies.Finally,we give the outlook for the potentials,the challenges,and the opportunities for the contribution of materials computation to future progress in the area. | Fan Ye Yi Zhang Christopher Addiego Mingjie Xu Huaixun Huyan Xiaobing Ren Xiaoqing Pan | 2021 | npj Computational Materials2021，，1： | 0 |
| 3 | Finding the semantic similarity in single-particle diffraction images using self-supervised contrastive projection learning显示文摘Single-shot coherent diffraction imaging of isolated nanosized particles has seen remarkable success in recent years,yielding in-situ measurements with ultra-high spatial and temporal resolution.The progress of high-repetition-rate sources for intense X-ray pulses has further enabled recording datasets containing millions of diffraction images,which are needed for the structure determination of specimens with greater structural variety and dynamic experiments.The size of the datasets,however,represents a monumental problem for their analysis.Here,we present an automatized approach for finding semantic similarities in coherent diffraction images without relying on human expert labeling.By introducing the concept of projection learning,we extend self-supervised contrastive learning to the context of coherent diffraction imaging and achieve a dimensionality reduction producing semantically meaningful embeddings that align with physical intuition.The method yields substantial improvements compared to previous approaches,paving the way toward real-time and large-scale analysis of coherent diffraction experiments at X-ray free-electron lasers. | Julian Zimmermann Fabien Beguet Daniel Guthruf Bruno Langbehn Daniela Rupp | 2023 | npj Computational Materials2023，，1： | 0 |
| 4 | Differential programming enabled functional imaging with Lorentz transmission electron microscopy显示文摘Lorentz transmission electron microscopy is an advanced characterization technique that enables the simultaneous imaging of both the microstructure and functional properties of materials.Information such as magnetization and electric potentials is carried by the phase of the electron wave,and is lost during image acquisition.Various methods have been proposed to retrieve the phase of the electron wavefunction using intensities of the acquired images,most of which work only in the small defocus limit.Imaging at strong defoci not only carries more quantitative phase information,but is essential to the study of weak magnetic and electrostatic fields at the nanoscale.In this work we develop a method based on differentiable programming to solve the inverse problem of phase retrieval.We show that our method maintains a high spatial resolution and robustness against noise even at the upper defocus limit of the microscope.More importantly,our proposed method can go beyond recovering just the phase information.We demonstrate this by retrieving the electron-optical parameters of the contrast transfer function alongside the electron exit wavefunction. | Tao Zhou Mathew Cherukara Charudatta Phatak | 2021 | npj Computational Materials2021，，1： | 0 |
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