Deep Learning-Assisted Quantification of Atomic Dopants and Defects in 2D Materials

Defect Density and Atomic Defect Recognition in the Middle Layer of a Trilayer MoS2 Stack

Molybdenum disulfide (MoS2) is one of the most intriguing two-dimensional materials, and moreover, its single atomic defects can significantly alter the properties. These defects can be both imaged and engineered using spherical and chromatic aberration-corrected high-resolution transmission electron microscopy (CC/CS-corrected HRTEM). In a few-layer stack, several atoms are vertically aligned in one atomic column. Therefore, it is challenging to determine the positions of missing atoms and the damage cross-section, particularly in the not directly accessible middle layers. In this study, we introduce a technique for extracting subtle intensity differences in CC/CS-corrected HRTEM images. By exploiting the crystal structure of the material, our method discerns chalcogen vacancies even in the middle layer of trilayer MoS2. We found that in trilayer MoS2 the middle layer's damage cross-section is about ten times lower than that in the monolayer. Our findings could be essential for the application of few-layer MoS2 in nanodevices.

Advances in Defect Engineering of Metal Oxides for Photocatalytic CO2 Reduction

Photocatalytic CO2 reduction technology, capable of converting low-density solar energy into high-density chemical energy, stands as a promising approach to alleviate the energy crisis and achieve carbon neutrality. Semiconductor metal oxides, characterized by their abundant reserves, good stability, and easily tunable structures, have found extensive applications in the field of photocatalysis. However, the wide bandgap inherent in metal oxides contributes to their poor efficiency in photocatalytic CO2 reduction. Defect engineering presents an effective strategy to address these challenges. This paper reviews the research progress in defect engineering to enhance the photocatalytic CO2 reduction performance of metal oxides, summarizing defect classifications, preparation methods, and characterization techniques. The focus is on defect engineering, represented by vacancies and doping, for improving the performance of metal oxide photocatalysts. This includes advancements in expanding the photoresponse range, enhancing photogenerated charge separation, and promoting CO2 molecule activation. Finally, the paper provides a summary of the current issues and challenges faced by defect engineering, along with a prospective outlook on the future development of photocatalytic CO2 reduction technology.

When Machine Learning Meets 2D Materials: A Review

The availability of an ever-expanding portfolio of 2D materials with rich internal degrees of freedom (spin, excitonic, valley, sublattice, and layer pseudospin) together with the unique ability to tailor heterostructures made layer by layer in a precisely chosen stacking sequence and relative crystallographic alignments, offers an unprecedented platform for realizing materials by design. However, the breadth of multi-dimensional parameter space and massive data sets involved is emblematic of complex, resource-intensive experimentation, which not only challenges the current state of the art but also renders exhaustive sampling untenable. To this end, machine learning, a very powerful data-driven approach and subset of artificial intelligence, is a potential game-changer, enabling a cheaper - yet more efficient - alternative to traditional computational strategies. It is also a new paradigm for autonomous experimentation for accelerated discovery and machine-assisted design of functional 2D materials and heterostructures. Here, the study reviews the recent progress and challenges of such endeavors, and highlight various emerging opportunities in this frontier research area.

Deep learning analysis on transmission electron microscope imaging of atomic defects in two-dimensional materials

Defects are prevalent in two-dimensional (2D) materials due to thermal equilibrium and processing kinetics. The presence of various defect types can affect material properties significantly. With the development of the advanced transmission electron microscopy (TEM), the property-related structures could be investigated in multiple dimensions. It produces TEM datasets containing a large amount of information. Traditional data analysis is influenced by the subjectivity of researchers, and manual analysis is inefficient and imprecise. Recent developments in deep learning provide robust methods for the quantitative identification of defects in 2D materials efficiently and precisely. Taking advantage of big data, it breaks the limitations of TEM as a local characterization tool, making TEM an intelligent macroscopic analysis method. In this review, the recent developments in the TEM data analysis of defects in 2D materials using deep learning technology are summarized. Initially, an in-depth examination of the distinctions between TEM and natural images is presented. Subsequently, a comprehensive exploration of TEM data analysis ensues, encompassing denoising, point defects, line defects, planar defects, quantitative analysis, and applications. Furthermore, an exhaustive assessment of the significant obstacles encountered in the accurate identification of distinct structures is also provided.

Se-Vacancy Healing with Substitutional Oxygen in WSe2 for High-Mobility p-Type Field-Effect Transistors

Transition-metal dichalcogenides possess high carrier mobility and can be scaled to sub-nanometer dimensions, making them viable alternative to Si electronics. WSe₂ is capable of hole and electron carrier transport, making it a key component in CMOS logic circuits. However, since the p-type electrical performance of the WSe₂-field effect transistor (FET) is still limited, various approaches are being investigated to circumvent this issue. Here, we formed a heterostructural multilayer WSe₂ channel and solution-processed aluminum-doped zinc oxide (AZO) for compositional modification of WSe₂ to obtain a device with excellent electrical properties. Supplying oxygen anions from AZO to the WSe₂ channel eliminated subgap states through Se-deficiency healing, resulting in improved transport capacity. Se vacancies are known to cause mobility degradation due to scattering, which is mitigated through ionic compensation. Consequently, the hole mobility can reach high values, with a maximum of approximately 100 cm²/V s. Further, the transport behavior of the oxygen-doped WSe₂-FET is systematically analyzed using density functional theory simulations and photoexcited charge collection spectroscopy measurements.

AtomVision: A Machine Vision Library for Atomistic Images

Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling microscopy and scanning transmission electron microscopy) data sets and apply a variety of machine learning techniques. To demonstrate the applicability of this library, we (1) establish an atomistic image data set of about 10 000 materials with large structural and chemical diversity, (2) develop and compare convolutional and atomistic line graph neural network models to classify the Bravais lattices, (3) demonstrate the application of fully convolutional neural networks using U-Net architecture to pixelwise classify atom versus background, (4) use a generative adversarial network for super resolution, (5) curate an image data set on the basis of natural language processing using an open-access arXiv data set, and (6) integrate the computational framework with experimental microscopy images for Rh, Fe₃O₄, and SnS systems. The AtomVision library is available at https://github.com/usnistgov/atomvision.

Deep learning based atomic defect detection framework for two-dimensional materials

Defects to popular two-dimensional (2D) transition metal dichalcogenides (TMDs) seriously lower the efficiency of field-effect transistor (FET) and depress the development of 2D materials. These atomic defects are mainly identified and researched by scanning tunneling microscope (STM) because it can provide precise measurement without harming the samples. The long analysis time of STM for locating defects in images has been solved by combining feature detection with convolutional neural networks (CNN). However, the low signal-noise ratio, insufficient data, and a large amount of TMDs members make the automatic defect detection system hard to be applied. In this study, we propose a deep learning-based atomic defect detection framework (DL-ADD) to efficiently detect atomic defects in molybdenum disulfide (MoS₂) and generalize the model for defect detection in other TMD materials. We design DL-ADD with data augmentation, color preprocessing, noise filtering, and a detection model to improve detection quality. The DL-ADD provides precise detection in MoS₂ (F2-scores is 0.86 on average) and good generality to WS₂ (F2-scores is 0.89 on average).

Deep-Learning Pipeline for Statistical Quantification of Amorphous Two-Dimensional Materials

Aberration-corrected transmission electron microscopy enables imaging of two-dimensional (2D) materials with atomic resolution. However, dissecting the short-range-ordered structures in radiation-sensitive and amorphous 2D materials remains a significant challenge due to low atomic contrast and laborious manual evaluation. Here, we imaged carbon-based 2D materials with strong contrast, which is enabled by chromatic and spherical aberration correction at a low acceleration voltage. By constructing a deep-learning pipeline, atomic registry in amorphous 2D materials can be precisely determined, providing access to a full spectrum of quantitative data sets, including bond length/angle distribution, pair distribution function, and real-space polygon mapping. Accurate segmentation of micropores and surface contamination, together with robustness against background inhomogeneity, guaranteed the quantification validity in complex experimental images. The automated image analysis provides quantitative metrics with high efficiency and throughput, which may shed light on the structural understanding of short-range-ordered structures. In addition, the convolutional neural network can be readily generalized to crystalline materials, allowing for automatic defect identification and strain mapping.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Defect chemistry of electrocatalysts for CO2 reduction

Electrocatalytic CO2 reduction is a promising strategy for converting the greenhouse gas CO2 into high value-added products and achieving carbon neutrality. The rational design of electrocatalysts for CO2 reduction is of great significance. Defect chemistry is an important category for enhancing the intrinsic catalytic performance of electrocatalysts. Defect engineering breaks the catalytic inertia inherent in perfect structures by imparting unique electronic structures and physicochemical properties to electrocatalysts, thereby improving catalytic activity. Recently, various defective nanomaterials have been studied and show great potential in electrocatalytic CO2 reduction. There is an urgent need to gain insight into the effect of defects on catalytic performance. Here, we summarized the recent research advances on the design of various types of defects, including carbon-based materials (intrinsic defects, heteroatom doping and single-metal-atom sites) and metal compounds (vacancies, grain boundaries, and lattice defects). The major challenges and prospects of defect chemistry in electrocatalytic CO2 reduction are also proposed. This review is expected to be instructive in the development of defect engineering for CO2 reduction catalysts.

Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam position without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step toward augmenting electron microscopy with machine learning methods.

STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2

Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS₂ from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.

Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf0.5 Zr0.5 O2 Thin Films

By controlling the configuration of polymorphic phases in high-k Hf_0.5 Zr_0.5 O₂ thin films, new functionalities such as persistent ferroelectricity at an extremely small scale can be exploited. To bolster the technological progress and fundamental understanding of phase stabilization (or transition) and switching behavior in the research area, efficient and reliable mapping of the crystal symmetry encompassing the whole scale of thin films is an urgent requisite. Atomic-scale observation with electron microscopy can provide decisive information for discriminating structures with similar symmetries. However, it often demands multiple/multiscale analysis for cross-validation with other techniques, such as X-ray diffraction, due to the limited range of observation. Herein, an efficient and automated methodology for large-scale mapping of the crystal symmetries in polycrystalline Hf_0.5 Zr_0.5 O₂ thin films is developed using scanning probe-based diffraction and a hybrid deep convolutional neural network at a 2 nm² resolution. The results for the doped hafnia films are fully proven to be compatible with atomic structures revealed by microscopy imaging, not requiring intensive human input for interpretation.