Vertical hetero-structures made from stacked monolayers of transition metal dichalcogenides (TMDC) are promising candidates for next-generation optoelectronic and thermoelectric devices. Identification of optimal layered materials for these applications requires the calculation of several physical properties, including electronic band structure and thermal transport coefficients. However, exhaustive screening of the material structure space using ab initio calculations is currently outside the bounds of existing computational resources. Furthermore, the functional form of how the physical properties relate to the structure is unknown, making gradient-based optimization unsuitable. Here, we present a model based on the Bayesian optimization technique to optimize layered TMDC hetero-structures, performing a minimal number of structure calculations. We use the electronic band gap and thermoelectric figure of merit as representative physical properties for optimization. The electronic band structure calculations were performed within the Materials Project framework, while thermoelectric properties were computed with BoltzTraP. With high probability, the Bayesian optimization process is able to discover the optimal hetero-structure after evaluation of only ∼20% of all possible 3-layered structures. In addition, we have used a Gaussian regression model to predict not only the band gap but also the valence band maximum and conduction band minimum energies as a function of the momentum.

High-resolution (or “cross-correlation”) electron backscatter diffraction analysis (HR-EBSD) utilizes cross-correlation techniques to determine relative orientation and distortion of an experimental electron backscatter diffraction pattern with respect to a reference pattern. The integrity of absolute strain and tetragonality measurements of a standard Si/SiGe material have previously been analyzed using reference patterns produced by kinematical simulation. Although the results were promising, the noise levels were significantly higher for kinematically produced patterns, compared with real patterns taken from the Si region of the sample. This paper applies HR-EBSD techniques to analyze lattice distortion in an Si/SiGe sample, using recently developed dynamically simulated patterns. The results are compared with those from experimental and kinematically simulated patterns. Dynamical patterns provide significantly more precision than kinematical patterns. Dynamical patterns also provide better estimates of tetragonality at low levels of distortion relative to the reference pattern; kinematical patterns can perform better at large values of relative tetragonality due to the ability to rapidly generate patterns relating to a distorted lattice. A library of dynamically generated patterns with different lattice parameters might be used to achieve a similar advantage. The convergence of the cross-correlation approach is also assessed for the different reference pattern types.

High-quality services for people with psychosis are essential. However, in this debate David Castle questions whether separate early intervention services are the best option and argues instead for an integrated approach. Swaran Singh responds, robustly defending the value of early intervention services.

Risk for depression is expressed across multiple levels of analysis. For example, parental depression and cognitive vulnerability are known markers of depression risk, but no study has examined their interactive effects on children's cortisol reactivity, a likely mediator of early depression risk. We examined relations across these different levels of vulnerability using cross-sectional and longitudinal methods in two community samples of children. Children were assessed for cognitive vulnerability using self-reports (Study 1; n = 244) and tasks tapping memory and attentional bias (Study 2; n = 205), and their parents were assessed for depression history using structured clinical interviews. In both samples, children participated in standardized stress tasks and cortisol reactivity was assessed. Cross-sectionally and longitudinally, parental depression history and child cognitive vulnerability interacted to predict children's cortisol reactivity; associations between parent depression and elevated child cortisol activity were found when children also showed elevated depressotypic attributions as well as attentional and memory biases. Findings indicate that models of children's emerging depression risk may benefit from the examination of the interactive effects of multiple sources of vulnerability across levels of analysis.

The solid state provides a richly varied fabric for intertwining chemical bonding, electronic structure, and magnetism. The discovery of superconductivity in iron pnictides and chalcogenides has revealed new aspects of this interplay, especially involving magnetism and superconductivity. Moreover, it has challenged prior thinking about high-temperature superconductivity by providing a set of materials that differ in many crucial aspects from the previously known cuprate superconductors. Here we review some of what is known about the superconductivity and its interplay with magnetism, chemistry, and electronic structure in Fe-based superconductors.

Photodeposited physical relief structures have been formed via the UV-irradiation of solutions of the heteroleptic titanium alkoxide, (OPy)2Ti(TAP)2. Raman studies of the films produced confirm the photoinitiation of hydrolysis and condensation reactions in the molecular structure, leading to the development of an insoluble solid phase. Using a shadow mask technique, these films were deposited directly from solution with microscale patterning and their structure and properties were studied as a function of both irradiation conditions (UV-fluence and intensity) as well as precursor solution chemistry (water content). Variations in nanostructure are visible in scanning electron micrographs, with a higher solution water content producing a more condensed, i.e. less porous, film.