Significant progress has been made
in the field of a priori crystal structure prediction,
with a number of recent remarkable
success stories. Herein, we briefly outline the methods that have
been developed for finding the global minimum structure and interesting
local minima without the need for experimental information. Focus
is placed on describing the XtalOpt evolutionary algorithm (EA) developed
in our group toward this end. XtalOpt is published under well-known
open-source licenses, and the EA searches can be analyzed via the
Avogadro chemical editor and visualizer. We describe new algorithmic
developments that have made it possible to predict the structures
of ever-more complex crystalline lattices. Benchmark tests, which
clearly illustrate how the new developments improve the success rate
and accelerate the discovery of the global minimum structure, are
performed. Finally, we describe how XtalOpt has been employed to predict
novel ternary hydrides that have the propensity for high-temperature
superconductivity under pressure.