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Machine learning (ML) methods have proved to be a very successful tool in physical sciences, especially when applied to experimental data analysis. Artificial intelligence is particularly good at recognizing patterns in high dimensional data, where it usually outperforms humans. Here we applied a simple ML tool called principal component analysis (PCA) to study data from muon spectroscopy. The measured quantity from this experiment is an asymmetry function, which holds the information about the average intrinsic magnetic field of the sample. A change in the asymmetry function might indicate a phase transition; however, these changes can be very subtle, and existing methods of analyzing the data require knowledge about the specific physics of the material. PCA is an unsupervised ML tool, which means that no assumption about the input data is required, yet we found that it still can be successfully applied to asymmetry curves, and the indications of phase transitions can be recovered. The method was applied to a range of magnetic materials with different underlying physics. We discovered that performing PCA on all those materials simultaneously can have a positive effect on the clarity of phase transition indicators and can also improve the detection of the most important variations of asymmetry functions. For this joint PCA we introduce a simple way to track the contributions from different materials for a more meaningful analysis., Comment: 4 pages, 1 figure, to be published in Journal of Physics: Conference Series, proceedings paper from SCES 2020 conference

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"

The ability to tune the iron chalcogenides BaFe2Q3 from Mott insulators, to metals and then superconductors with applied pressure has renewed interest in low-dimensional iron chalcogenides and oxychalcogenides. We report here a combined experimental and theoretical study on the iron oxychalcogenides BaFe2Q2O (Q=S, Se) and show that their magnetic behaviour results from nearest-neighbour magnetic exchange interactions via oxide and selenide anions of similar strength, with properties consistent with more localised electronic structures than those of BaFe2Q3 systems.

We present the results of neutron scattering experiments to study the crystal and magnetic structures of the Mott-insulating transition metal oxyselenides Pr2O2M2OSe2 (M = Mn, Fe). The structural role of the non-Kramers Pr3+ ion is investigated and analysis of Pr3+ crystal field excitations performed. Long-range order of Pr3+ moments in Pr2O2Fe2OSe2 can be induced by an applied magnetic field.

Magnetic oxyselenides have been the topic of research for several decades being first of interest in the context of photoconductivity and thermoelectricity owing to their intrinsic semiconducting properties and ability to tune the energy gap through metal ion substitution. More recently, interest in the oxyselenides has experienced a resurgence owing to the possible relation to strongly correlated phenomena given the fact that many oxyslenides share a similar structure to unconventional superconducting pnictides and chalcogenides. The two dimensional nature of many oxyselenide systems also draws an analogy to cuprate physics where a strong interplay between unconventional electronic phases and localised magnetism has been studied for several decades. It is therefore timely to review the physics of the oxyselenides in the context of the broader field of strongly correlated magnetism and electronic phenomena. Here we review the current status and progress in this area of research with the focus on the influence of lanthanides and transition metal ions on the intertwined magnetic and electronic properties of oxyselenides. The emphasis of the review is on the magnetic properties and comparisons are made with iron based pnictide and chalcogenide systems., Comment: (34 pages, 29 figures)

The discovery of superconductivity in the 122 iron selenide materials above 30 K necessitates an understanding of the underlying magnetic interactions. We present a combined experimental and theoretical investigation of magnetic and semiconducting Ce$_{2}$O$_{2}$FeSe$_{2}$ composed of chains of edge-linked iron selenide tetrahedra. The combined neutron diffraction and inelastic scattering study and density functional calculations confirm the ferromagnetic nature of nearest-neighbour Fe -- Se -- Fe interactions in the ZrCuSiAs-related iron oxyselenide Ce$_{2}$O$_{2}$FeSe$_{2}$. Inelastic measurements provide an estimate of the strength of nearest-neighbor Fe -- Fe and Fe -- Ce interactions. These are consistent with density functional theory calculations, which reveal that correlations in the Fe--Se sheets of Ce$_{2}$O$_{2}$FeSe$_{2}$ are weak. The Fe on-site repulsion $U_{Fe}$ is comparable to that reported for oxyarsenides and K$_{1-x}$Fe$_{2-y}$Se$_{2}$, which are parents to iron-based superconductors., Comment: (12 pages, 7 figures, to appear in Phys. Rev. B)

Identifying and characterizing the parent phases of iron-based superconductors is an important step towards understanding the mechanism for their high temperature superconductivity. We present an investigation into the magnetic interactions in the Mott insulator La2O2Fe2OSe2. This iron oxyselenide adopts a 2-k magnetic structure with low levels of magnetic frustration. This magnetic ground state is found to be dominated by next-nearest neighbor interactions J2 and J2' and the magnetocrystalline anisotropy of the Fe2+ site, leading to 2D-Ising-like spin S=2 fluctuations. In contrast to calculations, the values are small and confine the spin excitations below ~ 25 meV. This is further corroborated by sum rules of neutron scattering. This indicates that superconductivity in related materials may derive from a weakly coupled and unfrustrated magnetic structure., Comment: (main text - 4 pages, 4 figures; supplementary information - 7 pages, 6 figures)