Neutron three-axis spectrometers (TAS) provide the opportunity to model lattice dynamics and magnetic interactions by measuring the energy loss of neutron while interacting with material, and applying physical knowledge on the results. In result, the forces forming chemical structures, the origin of magnetic order and the reasons for hybridized excitation modes can be determined qualitatively...
Keywords: Materials Characterization, Diffraction Techniques, Machine Learning, Probabilistic Models, Structural Analysis.
Understanding a material inexorably requires from the determination of its atomic structure by means of neutron and x-rays based diffraction techniques. However, although artificial intelligence has shown valuable help in property-prediction lately [1], previous machine...
Supervised machine learning (ML) models are frequently trained on large datasets of physics-based simulations with the aim of being applied for experimental scattering or spectroscopy data analysis. However, ML models trained on simulated data often struggle to perform on experimental data. Two primary challenges are handling data from structures not present in the training database and...
Inelastic neutron scattering instruments allow detailed studies of the dynamical structure factor, $S(Q, \omega)$, where $Q$ is a scattering vector in reciprocal space and $h\omega = \Delta E$ an energy transfer. One of the work horses of modern neutron scattering is the triple-axis instrument, which typically have a high neutron flux and good energy resolution.Novel multiplexing triple-axis...
Determination of crystal structures of nano-crystalline, or amorphous compounds is a great challenge in solid states chemistry and physics. Structural analysis using atomic pair distribution function (PDF) of X-ray or neutron total scattering data has the potential to become a very efficient method in this field. Unfortunately, for real-space structure refinements using this method, an initial...
