CrySPY: a crystal structure prediction tool accelerated by machine learning

We have developed an open-source software called CrySPY, which is a crystal structure
prediction tool written in Python 3, and runs on Unix/Linux platforms. CrySPY enables anyone
to easily perform crystal structure prediction simulations for materials discovery and design,
and automates structure generation, structure optimization, energy evaluation, and efficiently
selecting candidates using machine learning. Several searching algorithms are available such
as random search, evolutionary algorithm, Bayesian optimization, and Look Ahead based on
Quadratic Approximation. Machine learning is employed to efficiently select candidates for
priority optimization. CrySPY does not require complex machine learning techniques for users.
In the latest version of CrySPY, both atomic and molecular random structures can be gener-
ated. CrySPY supports VASP, QUANTUM ESPRESSO, OpenMX, soiap, and LAMMPS for local
structure optimization and energy evaluation. CrySPY is distributed under the MIT license at
 https://github.com/Tomoki-YAMASHITA/CrySPY. Documentation of CrySPY is also available at
 https://Tomoki-YAMASHITA.github.io/CrySPY_doc.