In scientific imaging, deep learning has become a pivotal tool for image analytics. However, handling large volumetric datasets, which often exceed the memory capacity of standard GPUs, require special attention when subjected to deep learning efforts. This paper introduces qlty, a toolkit designed to address these challenges through tensor management techniques. qlty offers robust methods for subsampling, cleaning, and stitching of large-scale spatial data, enabling effective training and inference even in resource-limited environments.

Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a method for identifying artifacts in experimental XRD images. The proposed method uses deep learning convolutional neural network architectures, such as tunable U-Nets to identify the artifacts. In particular, the predicted artifacts are evaluated against the corresponding ground truth (manually implemented) using the overall true positive rate or recall. The result demonstrates that the U-Nets can consistently produce great recall performance at 92.4% on the test dataset, which is not included in the training, with a 34% reduction in average false positives in comparison to the conventional method. The U-Nets also reduce the time required to identify and separate artifacts by more than 50%. Furthermore, the exclusion of the artifacts shows major changes in the integrated 1D XRD pattern, enhancing further analysis of the post-processing XRD data.

Advancements in x-ray free-electron lasers on producing ultrashort, ultrabright, and coherent x-ray pulses enable single-shot imaging of fragile nanostructures, such as superfluid helium droplets. This imaging technique gives unique access to the sizes and shapes of individual droplets. In the past, such droplet characteristics have only been indirectly inferred by ensemble averaging techniques. Here, we report on the size distributions of both pure and doped droplets collected from single-shot x-ray imaging and produced from the free-jet expansion of helium through a 5 μm diameter nozzle at 20 bars and nozzle temperatures ranging from 4.2 to 9 K. This work extends the measurement of large helium nanodroplets containing 109-1011 atoms, which are shown to follow an exponential size distribution. Additionally, we demonstrate that the size distributions of the doped droplets follow those of the pure droplets at the same stagnation condition but with smaller average sizes.

Revealing the positions of all the atoms in large macromolecules is powerful but only possible with neutron macromolecular crystallography (NMC). Neutrons provide a sensitive and gentle probe for the direct detection of protonation states at near-physiological temperatures and clean of artifacts caused by x rays or electrons. Currently, NMC use is restricted by the requirement for large crystal volumes even at state-of-the-art instruments such as the macromolecular neutron diffractometer at the Spallation Neutron Source. EWALD's design will break the crystal volume barrier and, thus, open the door for new types of experiments, the study of grand challenge systems, and the more routine use of NMC in biology. EWALD is a single crystal diffractometer capable of collecting data from macromolecular crystals on orders of magnitude smaller than what is currently feasible. The construction of EWALD at the Second Target Station will cause a revolution in NMC by enabling key discoveries in the biological, biomedical, and bioenergy sciences.

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself.

Infrared (IR) absorption spectromicroscopy is a powerful, non-invasive probe that provides access to spatio-chemical information at the micron scale. The physical basis of IR spectroscopy lies in the oscillations of dynamic dipole moments in chemical bonds, with resonant frequencies in the IR spectral region of 4,000-400 cm -1 wave numbers. The bending or stretching of chemical bonds between atoms with different electronegativities, such as O-H or C=O, will lead to intense absorption and thus provide a unique fingerprint of specific chemical groups within the sample. The presence - or absence - of specific spectral fingerprints provide the opportunity to locate and identify chemical processes throughout the sample and track these processes in time or as a function of external perturbation. We can perform these types of measurements using a Synchrotron Fourier Transform Infrared (SFTIR) spectromicroscopy setup, as it provides orders of magnitude more photons than traditional bench-top machines 1 . Even with an ultra-bright IR source as provided at the Advanced Light Source, acquisition times typically take multiple hours. These long acquisition times are in part caused by the size of the field of view, as compared to the probe size: users typically analyze a 70 um by 100 um sample using a regular grid with a spacing of 1 um. With an acquisition time of 4 seconds per pixel, we would need about 8 hours to measure the full sample. Given that access to instruments is scarce, compounded by the desire to characterize different samples, being able to speed up data acquisition is of paramount importance. Here we present a strategy that drastically increases the efficiency of SFTIR spectromicroscopy by coupling the data collection with Gaussian Process based surrogate model 2,3 . This approach models the full hyperspectral datasets across the entire field of view, including regions we haven’t measure d yet, by multivariate Normal distribution. By analyzing this distribution, we gain insight into which future measurement locations provide the greatest reduction in total uncertainty and can also predict various quality metrics of the surrogate model we can use to end an experiment. Preliminary experiments show we can increase the throughput of SFTIR experiments by a factor of ~20.

Helium nanodroplets are considered ideal model systems to explore quantum hydrodynamics in self-contained, isolated superfluids. However, exploring the dynamic properties of individual droplets is experimentally challenging. In this work, we used single-shot femtosecond x-ray coherent diffractive imaging to investigate the rotation of single, isolated superfluid helium-4 droplets containing ~10(8) to 10(11) atoms. The formation of quantum vortex lattices inside the droplets is confirmed by observing characteristic Bragg patterns from xenon clusters trapped in the vortex cores. The vortex densities are up to five orders of magnitude larger than those observed in bulk liquid helium. The droplets exhibit large centrifugal deformations but retain axially symmetric shapes at angular velocities well beyond the stability range of viscous classical droplets.

Small Angle X-ray Scattering (SAXS) is a standard technique for the determination of low-resolution shapes of macromolecules and their complexes, adding important insights in understanding biological function. We present a novel and computationally efficient method for shape reconstruction from SAXS data. High-quality molecular shapes are calculated in less than two minutes, providing real-time feedback during SAXS experiments.

Laccase from the fungus Coriolus hirsutus has been purified. Crystals of the enzyme suitable for X-ray structure analysis have been obtained under optimized crystallization conditions using polyethylene glycol as a precipitant. The crystals belong to space group P2(1)2(1)2(1), with unit-cell parameters a = 50.65, b = 74.01, c = 124.83 A, and contain 40% solvent and a single molecule of laccase in the asymmetric unit. X-ray data were collected to 1.85 A at the copper edge and the four copper sites have been located from the anomalous signal. The obtained SAD phases with subsequent density modification produced a promising initial electron-density map.