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Advancements in nanotechnology require the development of nanofabrication methods for a wide range of materials, length scales, and elemental distributions. Today’s nanofabrication methods are typically missing at least one demanded characteristic. Hence, a general method enabling versatile nanofabrication remains elusive. Here, we show that, when revealing and using the underlying mechanisms of thermomechanical nanomolding, a highly versatile nanofabrication toolbox is the result. Specifically, we reveal interface diffusion and dislocation slip as the controlling mechanisms and use their transition to control, combine, and predict the ability to fabricate general materials, material combinations, and length scales. Designing specific elemental distributions is based on the relative diffusivities, the transition temperature, and the distribution of the materials in the feedstock. The mechanistic origins of thermomechanical nanomolding and their homologous temperature-dependent transition suggest a versatile toolbox capable of combining many materials in nanostructures and potentially producing any material in moldable shapes on the nanoscale. 

Abstract The viscosity and its temperature dependence, the fragility, are key properties of a liquid. A low fragility is believed to promote the formation of metallic glasses. Yet, the fragility remains poorly understood, since experimental data of its compositional dependence are scarce. Here, we introduce the film inflation method (FIM), which measures the fragility of metallic glass forming liquids across wide ranges of composition and glass-forming ability. We determine the fragility for 170 alloys ranging over 25 at.% in Mg–Cu–Y. Within this alloy system, large fragility variations are observed. Contrary to the general understanding, a low fragility does not correlate with high glass-forming ability here. We introduce crystallization complexity as an additional contribution, which can potentially become significant when modeling glass forming ability over many orders of magnitude. 

How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.