  Stable or metastable crystal structures of assembled atoms can be predicted
by finding the global or local minima of the energy surface defined on the
space of the atomic configurations. Generally, this requires repeated
first-principles energy calculations that are impractical for large systems,
such as those containing more than 30 atoms in the unit cell. Here, we have
made significant progress in solving the crystal structure prediction problem
with a simple but powerful machine-learning workflow; using a machine-learning
surrogate for first-principles energy calculations, we performed non-iterative,
single-shot screening using a large library of virtually created crystal
structures. The present method relies on two key technical components: transfer
learning, which enables a highly accurate energy prediction of pre-relaxed
crystalline states given only a small set of training samples from
first-principles calculations, and generative models to create promising and
diverse crystal structures for screening. Here, first-principles calculations
were performed only to generate the training samples, and for the optimization
of a dozen or fewer finally narrowed-down crystal structures. Our shotgun
method proved to be computationally less demanding compared to conventional
methods, which heavily rely on iterations of first-principles calculations, and
achieved an exceptional prediction accuracy, reaching 92.2% in a benchmark task
involving the prediction of 90 different crystal structures.
