{"id":4062,"date":"2021-04-26T11:09:17","date_gmt":"2021-04-26T10:09:17","guid":{"rendered":"https:\/\/research.kent.ac.uk\/pqm\/?p=4062"},"modified":"2021-04-28T19:02:42","modified_gmt":"2021-04-28T18:02:42","slug":"machine-learning-approach-to-muon-spectroscopy-analysis","status":"publish","type":"post","link":"https:\/\/research.kent.ac.uk\/pqm\/2021\/04\/26\/machine-learning-approach-to-muon-spectroscopy-analysis\/","title":{"rendered":"Machine learning approach to muon spectroscopy analysis"},"content":{"rendered":"

The technique developed by the authors provides a new way to analyse experimental data on advanced materials. The data were obtained using a well-established technique called “moun spin relaxation” where muons (a type of submatomic particle which can be created using powerful particle accelerators) are implanted in a sampled and the radiation they emit is detected. That radiation contains atomic-scale information about the electronic properties of the material. Traditionally the measured data are fitted to various functions based on researcher’s expectations of the underlying physics and chemistry of a given substance. The new technique is an unbiased approach which does not rely on pre-existing models and should work for any substance. This is the first direct application of machine-learning algorithms to this widely used experimental technique in condensed matter physics and materials science.<\/p>\n

References:<\/p>\n