Universal image segmentation for optical identification of 2D materials

Machine Learning-Enabled Nanoscale Phase Prediction in Engineered Poly(Vinylidene Fluoride)

Engineered poly(vinylidene fluoride) (PVDF) with its diverse crystalline phases plays a crucial role in determining the performance of devices in piezo-, pyro-, ferro- and tribo-electric applications, indicating the importance of distinct phase-detection in defining the structure-property relation. However, traditional characterization techniques struggle to effectively distinguish these phases, thereby failing to offer complete information. In this study, multimodal data-driven techniques have been employed for distinguishing different phases with a machine learning (ML) approach. This developed multimode model has been trained from empirical to theoretical data and demonstrates a classification accuracy of >94%, 15% more noise resilience, and 11% more accuracy from unimodality. Thus, from conception to validation, an alternative approach is provided to autonomously distinguish the different PVDF phases and eschew repetitive experiments that saved resources, thus accelerating the process of materials selection in various applications.

Semantic segmentation in crystal growth process using fake micrograph machine learning

Microscopic evaluation is one of the most effective methods in materials research. High-quality images are essential to analyze microscopic images using artificial intelligence. To overcome this challenge, we propose the machine learning of "fake micrographs" in this study. To verify the effectiveness of this method, we chose to analyze the optical microscopic images of the crystal growth process of a Ge thin film, which is a material in which it is difficult to obtain a contrast between the crystal and amorphous states. By learning the automatically generated fake micrographs that mimic the crystal growth process, the machine learning model can now identify the low-resolution real micrographs as crystalline or amorphous. Comparing the three types of machine learning models, it was found that ResUNet ++ exhibited high accuracy, exceeding 90%. The technology developed in this study for the automatic and rapid analysis of low-resolution images is widely helpful in material research.

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.

Identification and Structural Characterization of Twisted Atomically Thin Bilayer Materials by Deep Learning

Two-dimensional materials are expected to play an important role in next-generation electronics and optoelectronic devices. Recently, twisted bilayer graphene and transition metal dichalcogenides have attracted significant attention due to their unique physical properties and potential applications. In this study, we describe the use of optical microscopy to collect the color space of chemical vapor deposition (CVD) of molybdenum disulfide (MoS2) and the application of a semantic segmentation convolutional neural network (CNN) to accurately and rapidly identify thicknesses of MoS2 flakes. A second CNN model is trained to provide precise predictions on the twist angle of CVD-grown bilayer flakes. This model harnessed a data set comprising over 10,000 synthetic images, encompassing geometries spanning from hexagonal to triangular shapes. Subsequent validation of the deep learning predictions on twist angles was executed through the second harmonic generation and Raman spectroscopy. Our results introduce a scalable methodology for automated inspection of twisted atomically thin CVD-grown bilayers.

A Universal Approach to Determine the Atomic Layer Numbers in Two-Dimensional Materials Using Dark-Field Optical Contrast

Two-dimensional (2D) materials possess unique properties primarily due to the quantum confinement effect, which highly depends on their thicknesses. Identifying the number of atomic layers in these materials is a crucial, yet challenging step. However, the commonly used optical reflection method offers only very low contrast. Here, we develop an approach that shows unprecedented sensitivity by analyzing the brightness of dark-field optical images. The brightness of the 2D material edges has a linear dependence on the number of atomic layers. The findings are modeled by Rayleigh scattering, and the results agree well with the experiments. The relative contrast of single-layer graphene can reach 70% under white-light incident conditions. Furthermore, different 2D materials were successfully tested. By adjusting the exposure conditions, we can identify the number of atomic layers ranging from 1 to over 100. Finally, this approach can be applied to various substrates, even transparent ones, making it highly versatile.

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.

A simple and robust machine learning assisted process flow for the layer number identification of TMDs using optical contrast spectroscopy

Layered transition metal dichalcogenides (TMDs) like tungsten disulphide (WS₂) possess a large direct electronic band gap (∼2 eV) in the monolayer limit, making them ideal candidates for opto-electronic applications. The size and nature of the bandgap is strongly dependent on the number of layers. However, different TMDs require different experimental tools under specific conditions to accurately determine the number of layers. Here, we identify the number of layers of WS₂exfoliated on top of SiO₂/Si wafer from optical images using the variation of optical contrast with thickness. Optical contrast is a universal feature that can be easily extracted from digital images. But fine variations in the optical images due to different capturing conditions often lead to inaccurate layer number determination. In this paper, we have implemented a simple Machine Learning assisted image processing workflow that uses image segmentation to eliminate this difficulty. The workflow developed for WS₂is also demonstrated on MoS₂, graphene and h-BN, showing its applicability across various classes of 2D materials. A graphical user interface is provided to enhance the adoption of this technique in the 2D materials research community.

Universal image segmentation for optical identification of 2D materials

Machine learning methods are changing the way data is analyzed. One of the most powerful and widespread applications of these techniques is in image segmentation wherein disparate objects of a digital image are partitioned and classified. Here we present an image segmentation program incorporating a series of unsupervised clustering algorithms for the automatic thickness identification of two-dimensional materials from digital optical microscopy images. The program identifies mono- and few-layer flakes of a variety of materials on both opaque and transparent substrates with a pixel accuracy of roughly 95%. Contrasting with previous attempts, application generality is achieved through preservation and analysis of all three digital color channels and Gaussian mixture model fits to arbitrarily shaped data clusters. Our results provide a facile implementation of data clustering for the universal, automatic identification of two-dimensional materials exfoliated onto any substrate.