  In this paper, we present a new module to predict the potential surface
reconstruction configurations of given surface structures in the framework of
our machine learning and graph theory assisted universal structure searcher
(MAGUS). In addition to random structures generated with specific lattice
symmetry, we made full use of bulk materials to obtain a better distribution of
population energy, namely, randomly appending atoms to a surface cleaved from
bulk structures or moving/removing some of the atoms on the surface, which is
inspired by natural surface reconstruction processes. In addition, we borrowed
ideas from cluster predictions to spread structures better between different
compositions, considering that surface models of different atom numbers usually
have some building blocks in common. To validate this newly developed module,
we tested it with studies on the surface reconstructions of Si (100), Si (111)
and 4H-SiC(1-102)-c(2x2), respectively. We successfully gave the known ground
states as well as a new SiC surface model in an extremely Si-rich environment.
