Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
Low-temperature scanning tunneling spectroscopy is a key method to probe electronic and magnetic properties down to the atomic scale, but suffers from extreme vibrational sensitivity. This makes it challenging to employ closed-cycle cooling with its required pulse-type vibrational excitations, albeit this is mandatory to avoid helium losses for counteracting the continuously raising helium prices. Here, we describe a compact ultra-high vacuum scanning tunneling microscope (STM) system with an integrated primary pulse tube cooler (PTC) for closed-cycle operation.
Many-body interactions in metal-organic frameworks (MOFs) are fundamental for emergent quantum physics. Unlike their solution counterpart, magnetization at surfaces in low-dimensional analogues is strongly influenced by magnetic anisotropy (MA) induced by the substrate and still not well understood. Here, on-surface coordination chemistry is used to synthesize on Ag(111) and superconducting Pb(111) an iron-based spin chain by using pyrene-4,5,9,10-tetraone (PTO) precursors as ligands.
Protein kinases are important drug targets, yet specific inhibitors have been developed for only a fraction of the more than 500 human kinases. A major challenge in designing inhibitors for highly related kinases is selectivity. Unlike their non-covalent counterparts, covalent inhibitors offer the advantage of selectively targeting structurally similar kinases by modifying specific protein side chains, particularly non-conserved cysteines.
How emerging adulthood, a period particularly susceptible to the onset of mental disorders, responds to acute aerobic exercise effects on mood and brain remains an open question. A challenge in addressing this question is the scarcity of open data derived from scientific intervention paradigms. We recruited 83 healthy volunteers aged 17 to 24, who were randomly assigned to either an exercise group or a control group.
Domain walls are quasi-one-dimensional topological defects in ferroic materials, which can harbor emergent functionalities. In the case of ferroelectric domain wall (FEDW) devices, an exciting frontier has emerged: memristor-based information storage and processing approaches. Memristor solid-state FEDW devices presented thus far, however predominantly utilize a complex network of domain walls to achieve the desired regulation of density and charge state.
