Machine learning approach to muon spectroscopy analysis.

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, PCA 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.

The magnetic and electronic properties of oxyselenides-influence of transition metal ions and lanthanides.

Magnetic oxyselenides have been a topic of research for several decades, firstly 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 oxyselenides 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.