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We present a theoretical study of the potential of Principal Component Analysis to analyse magnetic diffuse neutron scattering data on quantum materials. To address this question, we simulate the scattering function $S\left(\mathbf{q}\right)$ for a model describing a cluster magnet with anisotropic spin-spin interactions under different conditions of applied field and temperature. We find high dimensionality reduction and that the algorithm can be trained with surprisingly small numbers of simulated observations. Subsequently, observations can be projected onto the reduced-dimensionality space defined by the learnt principal components. Constant-field temperature scans corresponds to trajectories in this space which show characteristic bifurcations at the critical fields corresponding to ground-state phase boundaries. Such plots allow the ground-state phase diagram to be accurately determined from finite-temperature measurements., 12 pages, 9 figures; author's accepted version with improved discussions and additional appendices

In recent years, Artificial Intelligence techniques have proved to be very successful when applied to problems in physical sciences. Here we apply an unsupervised Machine Learning (ML) algorithm called Principal Component Analysis (PCA) as a tool to analyse the data from muon spectroscopy experiments. Specifically, we apply the ML technique to detect phase transitions in various materials. The measured quantity in muon spectroscopy is an asymmetry function, which may hold information about the distribution of the intrinsic magnetic field in combination with the dynamics of the sample. Sharp changes of shape of asymmetry functions - measured at different temperatures - might indicate a phase transition. Existing methods of processing the muon spectroscopy data are based on regression analysis, but choosing the right fitting function requires knowledge about the underlying physics of the probed material. Conversely, Principal Component Analysis focuses on small differences in the asymmetry curves and works without any prior assumptions about the studied samples. We discovered that the PCA method works well in detecting phase transitions in muon spectroscopy experiments and can serve as an alternative to current analysis, especially if the physics of the studied material are not entirely known. Additionally, we found out that our ML technique seems to work best with large numbers of measurements, regardless of whether the algorithm takes data only for a single material or whether the analysis is performed simultaneously for many materials with different physical properties., Comment: 11 pages, 7 figures, to be submitted to the Journal of Physics: Condensed Matter special issue on "Machine Learning in Condensed Matter Physics"

Superconductivity and magnetism are antagonistic states of matter. The presence of spontaneous magnetic fields inside the superconducting state is, therefore, an intriguing phenomenon prompting extensive experimental and theoretical research. In this review, we discuss recent experimental discoveries of unconventional superconductors which spontaneously break time-reversal symmetry and theoretical efforts in understanding their properties. We discuss the main experimental probes and give an extensive account of theoretical approaches to understand the order parameter symmetries and the corresponding pairing mechanisms including the importance of multiple bands., 29 pages, 10 figures

© 2018 IOP Publishing Ltd. Room temperature electron irradiation in aqueous environment is applied to CeO2 nanoparticles using a transmission electron microscope equipped with liquid environmental cell. Oxide dissolution kinetics become accessible at unprecedented scale of spatial and time resolution through irradiation activation of water within a sub-μm size volume, allowing direct measurements of transformation rate and morphologies. Successful live-observation of the formation of nano-needles provides essential inside in how 1D-nanostructures can form. Furthermore, formation of hydrogen bubbles is found and interpreted in relation to the dose needed for ceria dissolution. The results are of importance for many research applications of ceria in water, e.g. for catalysis, environmental remediation, biomedical radiation protection, anti-corrosion coatings, and ultimately via analogy to UO2 also for fission-power fuel engineering and waste disposal.

We have employed large scale exact numerical diagonalization in Haldane spherical geometry in a comparative analysis of the correlated many-electron states in the half-filled low Landau levels of graphene and such conventional semiconductors as GaAs, including both spin and valley (i.e., pseudospin) degrees of freedom. We present evidence that the polarized Fermi sea of essentially non-interacting composite fermions remains stable against a pairing transition in both lowest Landau levels of graphene. However, it undergoes spontaneous depolarization, which in (ideal) graphene is unprotected for the lack of a single-particle pseudospin splitting. These results point to the absence of the non-Abelian Pfaffian phase in graphene.

By means of first-principles calculations, various properties of SrRuO3 are investigated, focusing on its lattice dynamical properties. Despite having a Goldschmidt tolerance factor very close to 1, the phonon dispersion curves of the high-temperature cubic phase of SrRuO3 show strong antiferrodistortive instabilities. The energetics of metastable phases with different tilt patterns are discussed, concluding that the coupling of oxygen-rotation modes with anti-polar Sr motion plays a key role in stabilizing the Pnma phase with respect to alternative rotation patterns. This not only explains the ground state of SrRuO3 but also contributes to rationalize why most ABO3 perovskites exhibit an orthorhombic ground state. The zone-center phonon modes of the Pnma phase have been computed, from which we propose partial reassignment of available experimental data. The full dispersion curves have also been obtained, constituting benchmark results for the interpretation of future measurements and providing access to thermodynamical properties., Comment: Also see LabTalk (http://iopscience.iop.org/0953-8984/labtalk-article/55704) and HighLight of 2014 (http://iopscience.iop.org/0953-8984/page/Highlights-of-2014)