Using CycleGANs to Generate Realistic STEM Images for Machine Learning

The rise of automation and machine learning (ML) in electron microscopy has the potential to revolutionize materials research by enabling the autonomous collection and processing of vast amounts of atomic resolution data. However, a major challenge is developing ML models that can reliably and rapidly generalize to large data sets with varying experimental conditions. To overcome this challenge, we develop a cycle generative adversarial network (CycleGAN) that introduces a novel reciprocal space discriminator to augment simulated data with realistic, complex spatial frequency information learned from experimental data. This enables the CycleGAN to generate nearly indistinguishable images from real experimental data, while also providing labels for further ML applications. We demonstrate the effectiveness of this approach by training a fully convolutional network (FCN) to identify single atom defects in a large data set of 4.5 million atoms, which we collected using automated acquisition in an aberration-corrected scanning transmission electron microscope (STEM). Our approach yields highly adaptable FCNs that can adjust to dynamically changing experimental variables, such as lens aberrations, noise, and local contamination, with minimal manual intervention. This represents a significant step towards building fully autonomous approaches for harnessing microscopy big data.

Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System

Industry 4.0 targets the conversion of the traditional industries into intelligent ones through technological revolution. This revolution is only possible through innovation, optimization, interconnection, and rapid decision-making capability. Numerical models are believed to be the key components of Industry 4.0, facilitating quick decision-making through simulations instead of costly experiments. However, numerical investigation of precise, high-fidelity models for optimization or decision-making is usually time-consuming and computationally expensive. In such instances, data-driven surrogate models are excellent substitutes for fast computational analysis and the probabilistic prediction of the output parameter for new input parameters. The emergence of Internet of Things (IoT) and Machine Learning (ML) has made the concept of surrogate modeling even more viable. However, these surrogate models contain intrinsic uncertainties, originate from modeling defects, or both. These uncertainties, if not quantified and minimized, can produce a skewed result. Therefore, proper implementation of uncertainty quantification techniques is crucial during optimization, cost reduction, or safety enhancement processes analysis. This chapter begins with a brief overview of the concept of surrogate modeling, transfer learning, IoT and digital twins. After that, a detailed overview of uncertainties, uncertainty quantification frameworks, and specifics of uncertainty quantification methodologies for a surrogate model linked to a digital twin is presented. Finally, the use of uncertainty quantification approaches in the nuclear industry has been addressed.