  Accessing structures of molecules, crystals, and complex interfaces with
atomic level details is vital to the understanding and engineering of
materials, chemical reactions, and biochemical processes. Currently,
determination of accurate atomic positions heavily relies on advanced
experimental techniques that are difficult to access or quantum chemical
calculations that are computationally intensive. We describe an efficient
data-driven LOcal SImilarity Kernel Optimization (LOSIKO) approach to obtain
atomic structures by matching embedded local atomic environments with that in
databases followed by maximizing their similarity measures. We show that LOSIKO
solely leverages on geometric data and can incorporate quantum chemical
databases constructed under different approximations. By including known stable
entries, chemically informed atomic structures of organic molecules, inorganic
solids, defects, and complex interfaces can be obtained, with similar accuracy
compared to the state-of-the-art quantum chemical approaches. In addition, we
show that by carefully curating the databases, it is possible to obtain
structures with bias towards target material features for inverse design.
