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The stopping site of the muon in a muon-spin relaxation experiment ({\mu}+SR)is in general unknown. There are some techniques that can be used to guess themuon stopping site, but they often rely on approximations and are not generallyapplicable to all cases. In this work, we propose a purely theoretical methodto predict muon stopping sites in crystalline materials from first principles.The method is based on a combination of ab initio calculations, randomstructure searching and machine learning, and it has successfully predicted theMuT and MuBC stopping sites of muonium in Si, Diamond and Ge, as well as themuonium stopping site in LiF, without any recourse to experimental results. Themethod makes use of Soprano, a Python library developed to aid ab-initiocomputational crystallography, that was publicly released and contains all thesoftware tools necessary to reproduce our analysis.

Computational Prediction of Muon Stopping Sites Using Ab Initio Random Structure Searching (AIRSS)