An optical linewidth study of a chromoprotein-C-phycocyanin in a low-temperature glass

The temperature dependence of spectral holes burnt into a phycocyanin-doped ethylene glycol/water glass is investigated in the temperature range between 1.5 and 15 K. The data are well described by a power law with an exponent of 1.16 ± 0.1. Chromoproteins thus behave very much the same as glasses doped with small impurity molecules

Magnetic field enhanced structural instability in EuTiO_{3}

EuTiO_{3} undergoes a structural phase transition from cubic to tetragonal at T_S = 282 K which is not accompanied by any long range magnetic order. However, it is related to the oxygen ocathedra rotation driven by a zone boundary acoustic mode softening. Here we show that this displacive second order structural phase transition can be shifted to higher temperatures by the application of an external magnetic field (increased by 4 K for mu_{0}H = 9 T). This observed field dependence is in agreement with theoretical predictions based on a coupled spin-anharmonic-phonon interaction model.Comment: 4 pages, 4 figure

Dem Leben der Meerforelle auf der Spur

Einzigartiges Forschungsprojekt an der Lippingau: Geomar-Wissenschaftler pflanzen Fischen Überwachungs-Chips ei

Lattice and polarizability mediated spin activity in EuTiO_3

EuTiO_3 is shown to exhibit novel strong spin-charge-lattice coupling deep in the paramagnetic phase. Its existence is evidenced by an, until now, unknown response of the paramagnetic susceptibility at temperatures exceeding the structural phase transition temperature T_S = 282K. The "extra" features in the susceptibility follow the rotational soft zone boundary mode temperature dependence above and below T_S. The theoretical modeling consistently reproduces this behavior and provides reasoning for the stabilization of the soft optic mode other than quantum fluctuations.Comment: 8 pages, 4 figure

Hybrid paramagnon phonon modes at elevated temperatures in EuTiO3

EuTiO3 (ETO) has recently experienced an enormous revival of interest because of its possible multiferroic properties which are currently in the focus of research. Unfortunately ETO is an unlikely candidate for enlarged multifunctionality since the mode softening - typical for ferroelectrics - remains incomplete, and the antiferromagnetic properties appear at 5.5K only. However, a strong coupling between lattice and Eu spins exists and leads to the appearance of a magnon-phonon-hybrid mode at elevated temperatures as evidenced by electron paramagnetic resonance (EPR), muon spin rotation ({\mu}SR) experiments and model predictions based on a coupled spin-polarizability Hamiltonian. This novel finding supports the notion of strong magneto-dielectric (MD) effects being realized in ETO and opens new strategies in material design and technological applications.Comment: 9 pages, 4 figure

Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples directly, vast computational effort is invested for simulating these system in small steps, e.g., using Molecular Dynamics. Combining deep learning and statistical mechanics, we here develop Boltzmann Generators, that are shown to generate statistically independent samples of equilibrium states of representative condensed matter systems and complex polymers. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free energy differences, and discovery of new system states are demonstrated, providing a new statistical mechanics tool that performs orders of magnitude faster than standard simulation methods

Boltzmann Generators: Sampling Equilibrium States of Many-Body Systems with Deep Learning

Computing equilibrium states in condensed-matter many-body systems, such as solvated proteins, is a long-standing challenge. Lacking methods for generating statistically independent equilibrium samples directly, vast computational effort is invested for simulating these system in small steps, e.g., using Molecular Dynamics. Combining deep learning and statistical mechanics, we here develop Boltzmann Generators, that are shown to generate statistically independent samples of equilibrium states of representative condensed matter systems and complex polymers. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free energy differences, and discovery of new system states are demonstrated, providing a new statistical mechanics tool that performs orders of magnitude faster than standard simulation methods