  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 the
muon stopping site, but they often rely on approximations and are not generally
applicable to all cases. In this work, we propose a purely theoretical method
to predict muon stopping sites in crystalline materials from first principles.
The method is based on a combination of ab initio calculations, random
structure searching and machine learning, and it has successfully predicted the
MuT and MuBC stopping sites of muonium in Si, Diamond and Ge, as well as the
muonium stopping site in LiF, without any recourse to experimental results. The
method makes use of Soprano, a Python library developed to aid ab-initio
computational crystallography, that was publicly released and contains all the
software tools necessary to reproduce our analysis.
