  The discovery of new multicomponent inorganic compounds can provide direct
solutions to many scientific and engineering challenges, yet the vast size of
the uncharted material space dwarfs current synthesis throughput. While the
computational crystal structure prediction is expected to mitigate this
frustration, the NP-hardness and steep costs of density functional theory (DFT)
calculations prohibit material exploration at scale. Herein, we introduce
SPINNER, a highly efficient and reliable structure-prediction framework based
on exhaustive random searches and evolutionary algorithms, which is completely
free from empiricism. Empowered by accurate neural network potentials, the
program can navigate the configuration space faster than DFT by more than
10$^{2}$-fold. In blind tests on 60 ternary compositions diversely selected
from the experimental database, SPINNER successfully identifies experimental
(or theoretically more stable) phases for ~80% of materials within 5000
generations, entailing up to half a million structure evaluations for each
composition. When benchmarked against previous data mining or DFT-based
evolutionary predictions, SPINNER identifies more stable phases in the majority
of cases. By developing a reliable and fast structure-prediction framework,
this work opens the door to large-scale, unbounded computational exploration of
undiscovered inorganic crystals.
