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The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science. A findable, accessible, interoperable and reusable (FAIR) data infrastructure is discussed to turn the large amount of research data generated by the field of materials science into knowledge and value.

Inverse problems in image processing, phase imaging, and computer vision often share the same structure of mapping input image(s) to output image(s) but are usually solved by different application-specific algorithms. Deep convolutional neural networks have shown great potential for highly variable tasks across many image-based domains, but sometimes can be challenging to train due to their internal non-linearity. We propose a novel, fast-converging neural network architecture capable of solving generic image(s)-to-image(s) inverse problems relevant to a diverse set of domains. We show this approach is useful in recovering wavefronts from direct intensity measurements, imaging objects from diffusely reflected images, and denoising scanning transmission electron microscopy images, just by using different training datasets. These successful applications demonstrate the proposed network to be an ideal candidate solving general inverse problems falling into the category of image(s)-to-image(s) translation.

Surprise is a key component of many learning experiences, and yet its precise computational role, and how it changes with age, remain debated. One major challenge is that surprise often occurs jointly with other variables, such as uncertainty and outcome probability. To assess how humans learn from surprising events, and whether aging affects this process, we studied choices while participants learned from bandits with either Gaussian or bi-modal outcome distributions, which decoupled outcome probability, uncertainty, and surprise. A total of 102 participants (51 older, aged 50–73; 51 younger, 19–30 years) chose between three bandits, one of which had a bimodal outcome distribution. Behavioral analyses showed that both age-groups learned the average of the bimodal bandit less well. A trial-by-trial analysis indicated that participants performed choice reversals immediately following large absolute prediction errors, consistent with heightened sensitivity to surprise. This effect was stronger in older adults. Computational models indicated that learning rates in younger as well as older adults were influenced by surprise, rather than uncertainty, but also suggested large interindividual variability in the process underlying learning in our task. Our work bridges between behavioral economics research that has focused on how outcomes with low probability affect choice in older adults, and reinforcement learning work that has investigated age differences in the effects of uncertainty and suggests that older adults overly adapt to surprising events, even when accounting for probability and uncertainty effects.

We present a method that lowers the dose required for an electron ptychographic reconstruction by adaptively scanning the specimen, thereby providing the required spatial information redundancy in the regions of highest importance. The proposed method is built upon a deep learning model that is trained by reinforcement learning, using prior knowledge of the specimen structure from training data sets. We show that using adaptive scanning for electron ptychography outperforms alternative low-dose ptychography experiments in terms of reconstruction resolution and quality.

The breaking of symmetry across an oxide heterostructure causes the electronic orbitals to be reconstructed at the interface into energy states that are different from their bulk counterparts1. The detailed nature of the orbital reconstruction critically affects the spatial confinement and the physical properties of the electrons occupying the interfacial orbitals2–4. Using an example of two-dimensional electron liquids forming at LaAlO3/SrTiO3 interfaces5,6 with different crystal symmetry, we show that the selective orbital occupation and spatial quantum confinement of electrons can be resolved with subnanometre resolution using inline electron holography. For the standard (001) interface, the charge density map obtained by inline electron holography shows that the two-dimensional electron liquid is confined to the interface with narrow spatial extension (~1.0 ± 0.3 nm in the half width). On the other hand, the two-dimensional electron liquid formed at the (111) interface shows a much broader spatial extension (~3.3 ± 0.3 nm) with the maximum density located ~2.4 nm away from the interface, in excellent agreement with density functional theory calculations.

Background Equine sarcoids (ES) are the most common cutaneous tumors in equids. Systemic treatment options are sparse. Subcutaneous (SC) injections of Viscum album extract (VAE) demonstrate efficacy as a systemic treatment directed against ES. Objectives/Aim To critically assess the therapeutic efficacy of orally administered VAE. Animals Forty‐five ES‐affected, privately owned, 3–12 year‐old horses. Methods A 3‐armed randomized placebo‐controlled, double‐blinded study was conducted in a double‐dummy design. Horses were subjected to oral administration and SC injections of either VAE or placebo (VAE oral/placebo SC, VAE SC/placebo oral, placebo oral/placebo SC) over a 7‐month treatment period. Primary endpoint was the change of baseline of a composite index of ES number and ES area after 14 months. Second endpoint was the clinical response. Results No statistically significant difference in the composite endpoint between the 3 study arms was found. The primary endpoint showed 4 (27%) horses in the VAE oral group with complete ES regression, 3 (21%) in the VAE SC injection group, and 2 (13%) in the placebo group. The clinical response revealed complete or partial regression in 6 horses of the oral VAE group (40%), 4 of the SC injection group (29%), and 4 of the placebo group (25%). Direct comparison of oral VAE and placebo showed an odds ratio, stratified for prognosis of 2.16 (95%‐CI: 0.45–10.42) and a P‐value of 0.336. Conclusion and Clinical Importance Oral administration of VAE is well tolerated. No statistically significant difference in the effectiveness of systemic VAE versus placebo against ES was found.

4D-STEM, in which the 2D diffraction plane is captured for each 2D scan position in the scanning transmission electron microscope (STEM) using a pixelated detector, is complementing, and increasingly replacing existing imaging approaches. However, at present the speed of those detectors, although having drastically improved in the recent years, is still 100 to 1,000 times slower than the current PMT technology operators are used to. Regrettably, this means environmental scanning-distortion often limits the overall performance of the recorded 4D data. Here, we present an extension of existing STEM distortion correction techniques for the treatment of 4D data series. Although applicable to 4D data in general, we use electron ptychography and electric-field mapping as model cases and demonstrate an improvement in spatial fidelity, signal-to-noise ratio (SNR), phase precision, and spatial resolution.

Growing an InxGa1−xN/GaN (InGaN/GaN) multi‐quantum well (MQW) heterostructure in nanowire (NW) form is expected to overcome limitations inherent in light‐emitting diodes (LEDs) based on the conventional planar heterostructure. The epitaxial strain induced in InGaN/GaN MQW heterostructure can be relaxed through the sidewalls of NW, which is beneficial to LEDs because a much larger misfit strain with higher indium concentration can be accommodated with reduced piezoelectric polarization fields. The strain relaxation, however, renders highly complex strain distribution within the NW heterostructure. Here the authors show that complementary strain mapping using scanning transmission electron microscopy and dark‐field inline holography can comprehend the strain distribution within the axial In0.3Ga0.7N/GaN MQW heterostructure embedded in GaN NW by providing the strain maps which can cover the entire NW and fine details near the sidewalls. With the quantitative evaluation by 3D finite element modelling, it is confirmed that the observed complex strain distribution is induced by the strain relaxation leading to the strain partitioning between InGaN quantum disk, GaN quantum well, and the surrounding epitaxial GaN shell. The authors further show that the strain maps provide the strain tensor components which are crucial for accurate assessment of the strain‐induced piezoelectric fields in NW LEDs. Comprehensive strain analysis by scanning transmission electron microscopy‐based geometrical phase analysis and dark‐field inline holography, in conjunction with 3D finite element modelling, demonstrates that strain relaxation through the sidewall induces the strain partitioning in the axial In0.3Ga0.7N/GaN multi‐quantum well heterostructure embedded in GaN nanowire. Furthermore, the piezoelectric polarization calculated using the measured in‐plane and out‐of‐plane strain indicates a significant piezoelectric field reduction compared to the planar heterostructure.