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Lithium-ion batteries (LIBs) of high energy density and light-weight design, have found wide applications in electronic devices and systems. Degradation mechanisms that caused by lithiation is a main challenging problem for LIBs with high…
We investigate multi-modal material identification for special nuclear material (SNM) configurations using a combination of X-ray radiography, high-resolution {\gamma}-ray spectroscopy, and neutron multiplicity measurements. We consider a…
The nanoparticle size and distribution information in the SEM images of silicon crystals are generally counted by manual methods. The realization of automatic machine recognition is significant in materials science. This paper proposed a…
All materials are made from atoms arranged either in repeating (crystalline) or in random (amorphous) structures. Diffraction measurements probe average distances between atoms and/or planes of atoms. A transmission electron microscope in…
Nanogranular material systems are promising for a variety of applications in research and development. Their physical properties are often determined by grain sizes, shapes, mutual distances and by the chemistry of the embedding matrix With…
Lithium-ion batteries (LIBs) are becoming an important energy storage solution to achieve carbon neutrality, but it remains challenging to characterise their internal states for the assurance of performance, durability and safety. This work…
The routine and unique determination of minor phases in microstructures is critical to materials science. In metallurgy alone, applications include alloy and process development and the understanding of degradation in service. We develop a…
Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial…
Significant developments in the field of additive manufacturing (AM) allowed the fabrication of complex microarchitectured components with varying porosity across different scales. However, due to the high complexity of this process, the…
Artificial intelligence applications in autonomous driving, medical diagnostics, and financial systems increasingly demand machine learning models that can provide robust uncertainty quantification, interpretability, and noise resilience.…
Scanning transmission electron microscopy (STEM) has been extensively used for imaging complex materials down to atomic resolution. The most commonly employed STEM modality, annular dark-field imaging, produces easily-interpretable…
Scanning Electron Microscopy (SEM) is indispensable in modern materials science, enabling high-resolution imaging across a wide range of structural, chemical, and functional investigations. However, SEM imaging remains constrained by…
Integrated silicon photonic devices, which manipulate light to transmit and process information on a silicon-on-insulator chip, are highly sensitive to structural variations. Minor deviations during nanofabrication-the precise process of…
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…
A growing need exists for efficient and accurate methods for detecting defects in semiconductor materials and devices. These defects can have a detrimental impact on the efficiency of the manufacturing process, because they cause critical…
Automated and semi-automated techniques in biomedical electron microscopy (EM) enable the acquisition of large datasets at a high rate. Segmentation methods are therefore essential to analyze and interpret these large volumes of data, which…
Reconfigurable photonic devices are rapidly emerging as a cornerstone of next generation optical technologies, with wide ranging applications in quantum simulation, neuromorphic computing, and large-scale photonic processors. A central…
Traditional supervised medical image segmentation models require large amounts of labeled data for training; however, obtaining such large-scale labeled datasets in the real world is extremely challenging. Recent semi-supervised…
Precision in identifying nanometer-scale device-killer defects is crucial in both semiconductor research and development as well as in production processes. The effectiveness of existing ML-based approaches in this context is largely…
The development of modern metal deposition techniques like Focused Ion/Electron Beam Induced Deposition FIBID/FEBID relies heavily on the availability of metal-organic precursors of particular properties. To create a new precursor,…