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Subangstrom resolution has long been limited to aberration-corrected electron microscopy, where it is a powerful tool for understanding the atomic structure and properties of matter. Here, we demonstrate electron ptychography in an uncorrected scanning transmission electron microscope (STEM) with deep subangstrom spatial resolution down to 0.44 angstroms, exceeding the conventional resolution of aberration-corrected tools and rivaling their highest ptychographic resolutions​. Our approach, which we demonstrate on twisted two-dimensional materials in a widely available commercial microscope, far surpasses prior ptychographic resolutions (1 to 5 angstroms) of uncorrected STEMs. We further show how geometric aberrations can create optimized, structured beams for dose-efficient electron ptychography. Our results demonstrate that expensive aberration correctors are no longer required for deep subangstrom resolution.

 

The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research through autonomous data collection and processing. A significant challenge lies in developing ML models that rapidly generalize to large data sets under varying experimental conditions. We address this by employing a cycle generative adversarial network (CycleGAN) with a reciprocal space discriminator, which augments simulated data with realistic spatial frequency information. This allows the CycleGAN to generate images nearly indistinguishable from real data and provide labels for ML applications. We showcase our approach by training a fully convolutional network (FCN) to identify single atom defects in a 4.5 million atom data set, collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our method produces adaptable FCNs that can adjust to dynamically changing experimental variables with minimal intervention, marking a crucial step towards fully autonomous harnessing of microscopy big data.

 

Computational methods have gained importance and popularity in both academia and industry for materials research and development in recent years. Since 2014, our team at University of Illinois at Urbana-Champaign has consistently worked on reforming our Materials Science and Engineering curriculum by incorporating computational modules into all mandatory undergraduate courses. The outbreak of the COVID-19 pandemic disrupted education as on-campus resources and activities became highly restricted. Here we seek to investigate the impact of the university moving online in Spring 2020 and resuming in-person instructions in Fall 2021 on the effectiveness of our computational curricular reform from the students' perspective. We track and compare feedback from students in a representative course MSE 182 for their computational learning experience before, during and after the pandemic lockdown from 2019 to 2021. Besides, we survey all undergraduate students, for their online learning experiences during the pandemic. We find that online learning enhances the students' belief in the importance and benefits of computation in materials science and engineering, while making them less comfortable and confident to acquire skills that are relatively difficult. In addition, early computational learners are likely to experience more difficulties with online learning compared to students at late stages of their undergraduate education, regardless of the computational workload. Multiple reasons are found to limit the students' online computational learning, such as insufficient support from instructors and TAs, limited chances of peer communication and harder access to computational resources. Therefore, it is advised to guarantee more resources to students with novice computational skills regarding such limiting reasons in the future when online learning is applied.