  Global optimization of atomistic structure rely on the generation of new
candidate structures in order to drive the exploration of the potential energy
surface (PES) in search for the global minimum energy (GM) structure. In this
work, we discuss a type of structure generation, which locally optimizes
structures in complementary energy (CE) landscapes. These landscapes are
formulated temporarily during the searches as machine learned potentials (MLPs)
using local atomistic environments sampled from collected data. The CE
landscapes are deliberately incomplete MLPs that rather than mimicking every
aspect of the true PES are sought to become much smoother, having only few
local minima. This means that local optimization in the CE landscapes may
facilitate identification of new funnels in the true PES. We discuss how to
construct the CE landscapes and we test their influence on global optimization
of a reduced rutile SnO2(110)-(4x1) surface, and an olivine (Mg2SiO4)4 cluster
for which we report a new global minimum energy structure.
