  Structural optimization has been a crucial component in computational
materials research, and structure predictions have relied heavily on this
technique in particular. In this study, we introduce a novel method that
enhances the efficiency of local optimization by integrating an extra
fingerprint space into the optimization process. Our approach utilizes a mixed
energy concept in the hyper potential energy surface (PES), combining real
energy and a newly introduced fingerprint energy derived from the symmetry of
local atomic environment. This method strategically guides the optimization
process toward high-symmetry, low-energy structures by leveraging the intrinsic
symmetry of atomic configurations. The effectiveness of our approach was
demonstrated through structural optimizations of silicon, silicon carbide, and
Lennard-Jones cluster systems. Our results show that the fingerprint space
biasing technique significantly enhances the performance and probability of
discovering energetically favorable, high-symmetry structures, as compared to
conventional optimizations. The proposed method is anticipated to streamline
the search for new materials and facilitate the discovery of novel,
energetically favorable configurations.
