Detecting pathogenic bacteria and their phenotypes including microbial resistance is crucial for preventing infection, ensuring food safety, and promoting environmental protection. Raman spectroscopy offers rapid, seamless, and label-free identification, rendering it superior to gold-standard detection techniques such as culture-based assays and polymerase chain reactions. However, its practical adoption is hindered by issues related to weak signals, complex spectra, limited datasets, and a lack of adaptability for detection and characterization of bacterial pathogens. This review focuses on addressing these issues with recent Raman spectroscopy breakthroughs enabled by machine learning (ML), particularly deep learning methods. Given the regulatory requirements, consumer demand for safe food products, and growing awareness of risks with environmental pathogens, this study emphasizes addressing pathogen detection in clinical, food safety, and environmental settings. Here, we highlight the use of convolutional neural networks for analyzing complex clinical data and surface enhanced Raman spectroscopy for sensitizing early and rapid detection of pathogens and analyzing food safety and potential environmental risks. Deep learning methods can tackle issues with the lack of adequate Raman datasets and adaptability across diverse bacterial samples. We highlight pending issues and future research directions needed for accelerating real-world impacts of ML-enabled Raman diagnostics for rapid and accurate diagnosis and surveillance of pathogens across critical fields.

Characterizing defects in 2D materials, such as cracks in chemical vapor deposited (CVD)-grown hexagonal boron nitride (hBN), is essential for evaluating material quality and reliability. Traditional characterization methods are often time-consuming and subjective and can be hindered by the limited optical contrast of hBN. To address this, we utilized a YOLOv8n deep learning model for automated crack detection in transferred CVD-grown hBN films, using MATLAB’s Image Labeler and Supervisely for meticulous annotation and training. The model demonstrates promising crack-detection capabilities, accurately identifying cracks of varying sizes and complexities, with loss curve analysis revealing progressive learning. However, a trade-off between precision and recall highlights the need for further refinement, particularly in distinguishing fine cracks from multilayer hBN regions. This study demonstrates the potential of ML-based approaches to streamline 2D material characterization and accelerate their integration into advanced devices.

Strain and twist offer geometric control over lattice reconstruction and electronic symmetry in two‐dimensional (2D) materials. In contrast to chemical doping or compositional alloying, these deformations act on the material response by varying the deformation state and stacking registry. This review explores how the emerging fields of straintronics and twistronics are transforming from isolated demonstrations towards controllable design strategies for electronic, optoelectronic, magnetic, and quantum 2D material systems. Here, we organize a review of recent advances in controlled fabrication, spatially resolved characterization, multiscale modeling, and machine‐learning‐assisted analysis of twist‐ and strain‐induced effects. The examples include carbon‐based nanosheets, transition‐metal dichalcogenides (TMDs), magnetic layers, and layered halide perovskites. Throughout this overview, we emphasize the link from imposed geometric input to local lattice reconstruction and, ultimately, to the measured material or device response. Finally, we identify the main barriers to translating strain‐ and twist‐controlled 2D materials into electronic and optoelectronic devices.