Compositional optimization of hard-magnetic phases with machine-learning models

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build kernel-based ML models to predict optimal chemical compositions for new permanent magnets, which are key components in many green-energy technologies. The magnetic-property data used for training and testing the ML models are obtained from a combinatorial high-throughput screening based on density-functional theory calculations. Our straightforward choice of describing the different configurations enables the subsequent use of the ML models for compositional optimization and thereby the prediction of promising substitutes of state-of-the-art magnetic materials like Nd$_2$Fe$_{14}$B with similar intrinsic hard-magnetic properties but a lower amount of critical rare-earth elements.Comment: 12 pages, 6 figure

Elevational species shifts in a warmer climate are overestimated when based on weather station data

Strong topographic variation interacting with low stature alpine vegetation creates a multitude of micro-habitats poorly represented by common 2m above the ground meteorological measurements (weather station data). However, the extent to which the actual habitat temperatures in alpine landscapes deviate from meteorological data at different spatial scales has rarely been quantified. In this study, we assessed thermal surface and soil conditions across topographically rich alpine landscapes by thermal imagery and miniature data loggers from regional (2-km2) to plot (1-m2) scale. The data were used to quantify the effects of spatial sampling resolution on current micro-habitat distributions and habitat loss due to climate warming scenarios. Soil temperatures showed substantial variation among slopes (2-3K) dependent on slope exposure, within slopes (3-4K) due to micro-topography and within 1-m2 plots (1K) as a result of plant cover effects. A reduction of spatial sampling resolution from 1 × 1m to 100 × 100m leads to an underestimation of current habitat diversity by 25% and predicts a six-times higher habitat loss in a 2-K warming scenario. Our results demonstrate that weather station data are unable to reflect the complex thermal patterns of aerodynamically decoupled alpine vegetation at the investigated scales. Thus, the use of interpolated weather station data to describe alpine life conditions without considering the micro-topographically induced thermal mosaic might lead to misinterpretation and inaccurate predictio

Prison

Předmětem mé diplomové práce je návrh novostavby věznice. Budova je navržena jako věznice s dozorem Objekt je zasazen do rovinatého terénu na vybraném pozemku ve městě Jindřichův Hradec. Budova má tři nadzemní podlaží a jedno podzemní podlaží. Objekt je navržen z monolitického železobetonového stěnového systému. Konstrukci střechy tvoří jednoplášťová plochá střecha. Hlavní vstup do věznice je orientován na západ. Výkresová dokumentace potřebná k realizaci projektu je zpracována včetně šesti konstrukčních detailů. Výkresová část byla zpracována v počítačovém programu ArchiCAD.The topic of my final thesis is a design of a new building prison. The building is designed as prison with supervision. The building is situated on a flat terrain on the selected piece of land in a city of Jindřichův Hradec. The building has three above-gound floors and one underground floor. The building is designed of a cast-in-place reinforced concrete wall system. The roof is made as warm flat roof. The main entrance is west-facing. The project documentation which is needed for a realization of the new building is worked up with six structural details. Drawing part was processed in a computer program ArchiCAD.

Non-perturbative renormalization on the lattice

Strongly-interacting theories lie at the heart of elementary particle physics. Their distinct behaviour shapes our world sui generis. We are interested in lattice simulations of supersymmetric models, but every discretization of space-time inevitably breaks supersymmetry and allows renormalization of relevant susy-breaking operators. To understand the role of such operators, we study renormalization group trajectories of the nonlinear O(N) Sigma model (NLSM). Similar to quantum gravity, it is believed to adhere to the asymptotic safety scenario. By combining the demon method with blockspin transformations, we compute the global flow diagram. In two dimensions, we reproduce asymptotic freedom and in three dimensions, asymptotic safety is demonstrated. Essential for these results is the application of a novel optimization scheme to treat truncation errors. We proceed with a lattice simulation of the supersymmetric nonlinear O(3) Sigma model. Using an original discretization that requires to fine tune only a single operator, we argue that the continuum limit successfully leads to the correct continuum physics. Unfortunately, for large lattices, a sign problem challenges the applicability of Monte Carlo methods. Consequently, the last chapter of this thesis is spent on an assessment of the fermion-bag method. We find that sign fluctuations are thereby significantly reduced for the susy NLSM. The proposed discretization finally promises a direct confirmation of supersymmetry restoration in the continuum limit. For a complementary analysis, we study the one-flavor Gross-Neveu model which has a complex phase problem. However, phase fluctuations for Wilson fermions are very small and no conclusion can be drawn regarding the potency of the fermion-bag approach for this model

Engineering Quantum States, Nonlinear Measurements, and Anomalous Diffusion by Imaging

We show that well-separated quantum superposition states, measurements of strongly nonlinear observables, and quantum dynamics driven by anomalous diffusion can all be achieved for single atoms or molecules by imaging spontaneous photons that they emit via resonance florescence. To generate anomalous diffusion we introduce continuous measurements driven by L\'evy processes, and prove a number of results regarding their properties. In particular we present strong evidence that the only stable L\'evy density that can realize a strictly continuous measurement is the Gaussian.Comment: revtex4-1, 17 pages, 7 eps figure

Interplay of charge-transfer and Mott-Hubbard physics approached by an efficient combination of self-interaction correction and dynamical mean-field theory

Late transition-metal oxides with small charge-transfer energy $\Delta$ raise issues for state-of-the-art correlated electronic structure schemes such as the combination of density functional theory (DFT) with dynamical mean-field theory (DMFT). The accentuated role of the oxygen valence orbitals in these compounds asks for an enhanced description of ligand-based correlations. Utilizing the rocksalt-like NiO as an example, we present an advancement of charge self-consistent DFT+DMFT by including self-interaction correction (SIC) applied to oxygen. This introduces explicit onsite O correlations as well as an improved treatment of intersite $p-d$ correlations. Due to the efficient SIC incorporation in a pseudopotential form, the DFT+sicDMFT framework is an advanced but still versatile method to address the interplay of charge-transfer and Mott-Hubbard physics. We revisit the spectral features of stoichiometric NiO and reveal the qualitative sufficiency of local DMFT self-energies in describing spectral peak structures usually associated with explicit nonlocal processes. For Li$_x$Ni$_{1-x}$O, prominent in-gap states are verified by the present theoretical study.Comment: 8 pages, 6 figure