Modeling high-entropy transition-metal alloys with alchemical compression

Alloys composed of several elements in roughly equimolar composition, often referred to as high-entropy alloys, have long been of interest for their thermodynamics and peculiar mechanical properties, and more recently for their potential application in catalysis. They are a considerable challenge to traditional atomistic modeling, and also to data-driven potentials that for the most part have memory footprint, computational effort and data requirements which scale poorly with the number of elements included. We apply a recently proposed scheme to compress chemical information in a lower-dimensional space, which reduces dramatically the cost of the model with negligible loss of accuracy, to build a potential that can describe 25 d-block transition metals. The model shows semi-quantitative accuracy for prototypical alloys, and is remarkably stable when extrapolating to structures outside its training set. We use this framework to study element segregation in a computational experiment that simulates an equimolar alloy of all 25 elements, mimicking the seminal experiments by Cantor et al., and use our observations on the short-range order relations between the elements to define a data-driven set of Hume-Rothery rules that can serve as guidance for alloy design. We conclude with a study of three prototypical alloys, CoCrFeMnNi, CoCrFeMoNi and IrPdPtRhRu, determining their stability and the short-range order behavior of their constituents

Surface segregation in high-entropy alloys from alchemical machine learning

High-entropy alloys (HEAs), containing several metallic elements in near-equimolar proportions, have long been of interest for their unique mechanical properties. More recently, they have emerged as a promising platform for the development of novel heterogeneous catalysts, because of the large design space, and the synergistic effects between their components. In this work we use a machine-learning potential that can model simultaneously up to 25 transition metals to study the tendency of different elements to segregate at the surface of a HEA. We use as a starting point a potential that was previously developed using exclusively crystalline bulk phases, and show that, thanks to the physically-inspired functional form of the model, adding a much smaller number of defective configurations makes it capable of describing surface phenomena. We then present several computational studies of surface segregation, including both a simulation of a 25-element alloy, that provides a rough estimate of the relative surface propensity of the various elements, and targeted studies of CoCrFeMnNi and IrFeCoNiCu, which provide further validation of the model, and insights to guide the modeling and design of alloys for heterogeneous catalysis

Proximity Effect in Crystalline Framework Materials: Stacking‐Induced Functionality in MOFs and COFs

Metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) consist of molecular building blocks being stitched together by strong bonds. They are well known for their porosity, large surface area, and related properties. The electronic properties of most MOFs and COFs are the superposition of those of their constituting building blocks. If crystalline, however, solid‐state phenomena can be observed, such as electrical conductivity, substantial dispersion of electronic bands, broadened absorption bands, formation of excimer states, mobile charge carriers, and indirect band gaps. These effects emerge often by the proximity effect caused by van der Waals interactions between stacked aromatic building blocks. Herein, it is shown how functionality is imposed by this proximity effect, that is, by stacking aromatic molecules in such a way that extraordinary properties emerge in MOFs and COFs. After discussing the proximity effect in graphene‐related materials, its importance for layered COFs and MOFs is shown. For MOFs with well‐defined structure, the stacks of aromatic building blocks can be controlled via varying MOF topology, lattice constant, and by attaching steric control units. Finally, an overview of theoretical methods to predict and analyze these effects is given, before the layer‐by‐layer growth technique for well‐ordered surface‐mounted MOFs is summarized

Additive Polynomials for Finite Groups of Lie Type

This paper provides a realization of all classical and most exceptional finite groups of Lie type as Galois groups over function fields over F_q and derives explicit additive polynomials for the extensions. Our unified approach is based on results of Matzat which give bounds for Galois groups of Frobenius modules and uses the structure and representation theory of the corresponding connected linear algebraic groups.Comment: 59 pages; v2: added reference, slightly restructured section 6.1, few small rewordings; v3: completed realization of Steinberg's triality groups (thanks to P. Mueller for solving the remaining open question); clarified argument how to use Thm. 3.

"Warum sind die grossen Kinder so borniert?" Remediatisierte Jugend in gegenwärtigen Musikvideos

Der über Jahrzehnte etablierte Zusammenhang von Pop und Jugend ist zuletzt brüchig geworden und einem vielschichtigen Diskurs über Altersphasen im Pop gewichen. Diese Reflexionen zeigen sich auch zu-nehmend in Musikvideos. Im Zentrum unseres Aufsatzes stehen Musikvideos, in denen erwachsene Künstler*innen über die Lebensphase der Jugend erzählen. Mit dem Modus der Rückschau geht einher, dass diese Narrative mit der (Re-) Mediatisierung (Bolter und Grusin) von Jugend arbeiten: Als Bezugspunkte dienen klassische Coming-of-Age-Filme sowie Heim-Videos und Familienarchive, die teilweise in naher Verwandtschaft zum Musikdokumentarfilm Sequenzen persönlicher Archive mit zeitgeschichtlichem Material verweben. Am Bei-spiel neuerer Musikvideos von Tocotronic, Men I Trust, Molly Nilsson, Botschaft und den Goldenen Zitronen diskutieren wir, inwiefern über die spezifische Medialität und Ästhetik dieser Videos Jugend als fundamental mediatisierter, nostalgischer und häufig generationell codierter Sehnsuchtsort imaginiert wird

Segmentation and classification of total hip endoprosthesis in x-ray images

Motivation: Aim of this project is the automatic classification of total hip endoprosthesis (THEP) components in 2D Xray images. Revision surgeries of total hip arthroplasty (THA) are common procedures in orthopedics and trauma surgery. Currently, around 400.000 procedures per year are performed in the United States (US) alone. To achieve the best possible result, preoperative planning is crucial. Especially if parts of the current THEP system are to be retained. Methods: First, a ground truth based on 76 X-ray images was created: We used an image processing pipeline consisting of a segmentation step performed by a convolutional neural network and a classification step performed by a support vector machine (SVM). In total, 11 classes (5 pans and 6 shafts) shall be classified. Results: The ground truth generated was of good quality even though the initial segmentation was performed by technicians. The best segmentation results were achieved using a U-net architecture. For classification, SVM architectures performed much better than additional neural networks. Conclusions: The overall image processing pipeline performed well, but the ground truth needs to be extended to include a broader variability of implant types and more examples per training class

Proximity Effect in Crystalline Framework Materials: Stacking-Induced Functionality in MOFs and COFs

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) consist of molecular building blocks being stitched together by strong bonds. They are well known for their porosity, large surface area, and related properties. The electronic properties of most MOFs and COFs are the superposition of those of their constituting building blocks. If crystalline, however, solid-state phenomena can be observed, such as electrical conductivity, substantial dispersion of electronic bands, broadened absorption bands, formation of excimer states, mobile charge carriers, and indirect band gaps. These effects emerge often by the proximity effect caused by the van-der-Waals interactions between stacked aromatic building blocks. This Progress Report shows how functionality is imposed by this proximity effect, that is, by stacking aromatic molecules in such a way that extraordinary electronic and optoelectronic properties emerge in MOFs and COFs. After discussing the proximity effect in graphene-related materials, its importance for layered COFs and MOFs is shown. For MOFs with well-defined structure, the stacks of aromatic building blocks can be controlled via varying MOF topology, lattice constant, and by attaching steric control units. Finally, an overview of theoretical methods to predict and analyze these effects is given, before the layer-by-layer growth technique for well-ordered surface-mounted MOFs is summarized.</div