We report detailed low temperature magnetotransport and magnetization measurements in MnSi under pressures up to $\sim12\,{\rm kbar}$. Tracking the role of sample quality, pressure transmitter, and field and temperature history allows us to link the emergence of a giant topological Hall resistivity $\sim50\,{\rm n\Omega cm}$ to the skyrmion lattice phase at ambient pressure. We show that the remarkably large size of the topological Hall resistivity in the zero-temperature limit must be generic. We discuss various mechanisms which can lead to the much smaller signal at elevated temperatures observed at ambient pressure

Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibria as well as transport processes. Although experimental data on thousands of binary mixtures are available, prediction methods are needed to calculate the activity coefficients in many relevant mixtures that have not been explored to-date. In this report, we propose a probabilistic matrix factorization model for predicting the activity coefficients in arbitrary binary mixtures. Although no physical descriptors for the considered components were used, our method outperforms the state-of-the-art method that has been refined over three decades while requiring much less training effort. This opens perspectives to novel methods for predicting physico-chemical properties of binary mixtures with the potential to revolutionize modeling and simulation in chemical engineering. Activity Coefficients at Infinite Dilution   Solutes SolventsThis document is the unedited authors' version of a submitted work that was subsequently accepted for publication in TheIn this work, we describe a novel application of Machine Learning (ML) to the field of physical chemistry and thermodynamics: the prediction of physico-chemical properties of binary liquid mixtures by matrix completion. We focus on the prediction of a single property: the so-called activity coefficient, which is a measure of the non-ideality of a liquid mixture and of enormous relevance in practice. The interesting aspect of our approach is that no expert knowledge about the components that make up the mixture was used: all we needed was an incomplete, sparse data set of binary mixtures and their measured activity coefficients that our method was able to successfully complete. We show that this simple approach outperforms an established procedure that has been the state of the art for several decades.ML approaches to chemical and engineering sciences date back more than 50 years ago, but the genuine exploitation of the potential of ML in these fields has only recently begun 1 . An overview of recent advances in chemical and material sciences has, e.g., been given by Ramprasad et al. 2 and Butler et al. 3 ML has already been used to predict physico-chemical properties of mixtures, including activity coefficients 4-10 . Most of these approaches are basically quantitative structureproperty relationships (QSPR) methods 11 that use physical descriptors of the components as input data to characterize the considered mixtures and relate them to the property of interest by an ML algorithm, e.g., a neural network. However, the scope of these approaches is in general rather small.Binary mixtures are of fundamental importance in chemical engineering. The properties of mixtures can in general not be described based on properties of the pure components alone. If, however, the respective properties of the binary constituent 'sub-mixtures' of a multi-component mixture are known, the properties of the multi...

Propagation character of spin wave was investigated for chiral magnets FeGe and Co-Zn-Mn alloys, which can host magnetic skyrmions near room temperature. On the basis of the frequency shift between counter-propagating spin waves, the magnitude and sign of Dzyaloshinskii-Moriya (DM) interaction were directly evaluated. The obtained magnetic parameters quantitatively account for the size and helicity of skyrmions as well as their materials variation, proving that the DM interaction plays a decisive role in the skyrmion formation in this class of room-temperature chiral magnets. The propagating spin-wave spectroscopy can thus be an efficient tool to study DM interaction in bulk single-phase compounds. Our results also demonstrate a function of spin-wave diode based on chiral crystal structures at room temperature. 1 Recently, the concept of magnetic skyrmions, i.e. vortex-like swirling spin texture with topologically stable particle nature, has attracted much attention as potential information carriers for novel magnetic information storage and processing devices [1-8]. The skyrmion and associated helical spin texture can be stabilized by several distinctive mechanisms, such as Dzyaloshinskii-Moriya interaction (DMI) [1, 8], frustrated exchange interactions [9, 10] or the competition between magnetic dipole-dipole interaction and magnetic anisotropy [11, 12]. So far, the experimental observation of skyrmions has mainly been reported for a series of noncentrosymmetric ferromagnets, where the sizable contribution of DMI is expected [2, 4, 13-15]. However, the full understanding for DMI in the metallic system is often more difficult than the case for the insulating system, and recent theories suggest its relevance to quantum Berry phase and band anti-crossing that causes the complicated E F (Fermi energy)-dependence of DMI [16-18]. To unambiguously elucidate the microscopic origin of skyrmion formation for each compound, the direct quantitative evaluation of relevant magnetic parameters, in particular the magnitude and sign of DMI, is important. To directly evaluate DMI, one promising approach is the analysis of spin wave dispersion in the ferromagnetic state. It is generally symmetric (i.e. even function) with respect to the wave number k, but can become asymmetric only under the existence of k-linear term originating from DMI that causes the energy shift between ±k [19]. The direct observation of DMI based on this idea has recently been reported for the interface of bilayer films by employing several surface-sensitive methods such as Brillouin light scattering [20, 21] and spin-polarized electron energy loss spectroscopy [22]. For bulk single-phase compounds, on the other hand, the direct quantitative evaluation of DMI has rarely been reported. Only recently, the alternative method based on neutron inelastic scattering technique [23] and propagating spin-wave spectroscopy (PSWS) [24-28] has been proposed. Among a series of single-phase compounds hosting magnetic skyrmions, the most promising ones for potential appli...

The lateral-line system is a unique mechanosensory facility of aquatic animals that enables them not only to localize prey, predator, obstacles, and conspecifics, but also to recognize hydrodynamic objects. Here we present an explicit model explaining how aquatic animals such as fish can distinguish differently shaped submerged moving objects. Our model is based on the hydrodynamic multipole expansion and uses the unambiguous set of multipole components to identify the corresponding object. Furthermore, we show that within the natural range of one fish length the velocity field contains far more information than that due to a dipole. Finally, the model we present is easy to implement both neuronally and technically, and agrees well with available neuronal, physiological, and behavioral data on the lateral-line system.

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach `distills' the physical method's predictions into a prior model and combines it with...

Predictive models of thermodynamic properties of mixtures are paramount in chemical engineering and chemistry. Classical thermodynamic models are successful in generalizing over (continuous) conditions like temperature and concentration. On the...

We present a generic way to hybridize physical and data-driven methods for predicting physicochemical properties. The approach ‘distills’ the physical method's predictions into a prior model and combines it with sparse experimental data using Bayesian inference. We apply the new approach to predict activity coefficients at infinite dilution and obtain significant improvements compared to the physical and data-driven baselines and established ensemble methods from the machine learning literature.

The presence of (approximate) conservation laws can prohibit the fast relaxation of interacting many-particle quantum systems. We investigate this physics by studying the center-of-mass oscillations of two species of fermionic ultracold atoms in a harmonic trap. If their trap frequencies are equal, a dynamical symmetry (spectrum generating algebra), closely related to Kohn's theorem, prohibits the relaxation of center-of-mass oscillations. A small detuning δω of the trap frequencies for the two species breaks the dynamical symmetry and ultimately leads to a damping of dipole oscillations driven by inter-species interactions. Using memory-matrix methods, we calculate the relaxation as a function of frequency difference, particle number, temperature and strength of interspecies interactions. When interactions dominate, there is almost perfect drag between the two species and the dynamical symmetry is approximately restored. The drag can either arise from Hartree potentials or from friction. In the latter case (hydrodynamic limit), the center-of-mass oscillations decay with a tiny rate, 1/τ ∝ (δω) 2 /Γ, where Γ is a single particle scattering rate.