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| 1 | Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS_(2)显示文摘Recent advances in scanning transmission electron microscopy(STEM)allow the real-time visualization of solid-state transformations in materials,including those induced by an electron beam and temperature,with atomic resolution.However,despite the ever-expanding capabilities for high-resolution data acquisition,the inferred information about kinetics and thermodynamics of the process,and single defect dynamics and interactions is minimal.This is due to the inherent limitations of manual ex situ analysis of the collected volumes of data.To circumvent this problem,we developed a deep-learning framework for dynamic STEM imaging that is trained to find the lattice defects and apply it for mapping solid state reactions and transformations in layered WS_(2).The trained deep-learning model allows extracting thousands of lattice defects from raw STEM data in a matter of seconds,which are then classified into different categories using unsupervised clustering methods.We further expanded our framework to extract parameters of diffusion for sulfur vacancies and analyzed transition probabilities associated with switching between different configurations of defect complexes consisting of Mo dopant and sulfur vacancy,providing insight into pointdefect dynamics and reactions.This approach is universal and its application to beam-induced reactions allows mapping chemical transformation pathways in solids at the atomic level. | Artem Maksov Ondrej Dyck Kai Wang Kai Xiao David B.Geohegan Bobby G.Sumpter Rama K.Vasudevan Stephen Jesse Sergei V.Kalinin Maxim Ziatdinov | 2019 | npj Computational Materials2019，，1： | 11 |
| 2 | Identification of crystal symmetry from noisy diffraction patterns by a shape analysis and deep learning显示文摘The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis.Recent studies have shown that deep learning(DL)methods can effectively reveal the correlations between X-ray or electron-beam diffraction patterns and crystal symmetry.Despite their promise,most of these studies have been limited to identifying relatively few classes into which a target material may be grouped.On the other hand,the DL-based identification of crystal symmetry suffers from a drastic drop in accuracy for problems involving classification into tens or hundreds of symmetry classes(e.g.,up to 230 space groups),severely limiting its practical usage. | Leslie Ching Ow Tiong Jeongrae Kim Sang Soo Han Donghun Kim | 2020 | npj Computational Materials2020，，1： | 2 |
| 3 | Defect detection in atomic-resolution images via unsupervised learning with translational invariance显示文摘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. | Yueming Guo Sergei V.Kalinin Hui Cai Kai Xiao Sergiy Krylyuk Albert V.Davydov Qianying Guo Andrew R.Lupini | 2021 | npj Computational Materials2021，，1： | 2 |
| 4 | Machine learning in materials design:Algorithm and application显示文摘Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning(ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm:(i) data acquisition →(ii) feature engineering →(iii) algorithm →(iv) ML model →(v) model evaluation →(vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’:(i) structure and composition →(ii) property →(iii) synthesis →(iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications. | 宋志龙 陈曦雯 孟繁斌 程观剑 王陈 孙中体 尹万健 | 2020 | Chinese Physics B2020，29，11： | 1 |
| 5 | Automatic identification of crystal structures and interfaces via artificial-intelligence-based electron microscopy显示文摘Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images. | Andreas Leitherer Byung Chul Yeo Christian H.Liebscher Luca M.Ghiringhelli | 2023 | npj Computational Materials2023，，1： | 0 |
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