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The Cluster expansion (CE) is a powerful method for representing the energetics of alloys from a fit to first principles energies. However, many common fitting methods are computationally demanding and do not provide the guarantee that the system’s ground states are preserved. This paper demonstrates the use of an efficient implementation of a Bayesian algorithm for cluster expansion construction that ensures all the input structural energies are fitted perfectly while reducing computational cost. The method incorporates an active learning scheme that searches for new optimal structures to include in the fit. As performance tests, we calculate the phase diagram of the Fe-Ir system and study the short range order in an equimolar MoNbTaVW system. The new method has been integrated into the Alloy Theoretic Automated Toolkit (ATAT). 

Abstract Reducing the sample size can profoundly impact properties of bulk metallic glasses. Here, we systematically reduce the length scale of Au and Pt-based metallic glasses and study their vitrification behavior and atomic mobility. For this purpose, we exploit fast scanning calorimetry (FSC) allowing to study glassy dynamics in an exceptionally wide range of cooling rates and frequencies. We show that the mainαrelaxation process remains size independent and bulk-like. In contrast, we observe pronounced size dependent vitrification kinetics in micrometer-sized glasses, which is more evident for the smallest samples and at low cooling rates, resulting in more than 40 K decrease in fictive temperature,T_f, with respect to the bulk. We discuss the deep implications on how this outcome can be used to convey glasses to low energy states. 

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.