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 generated. CrySPY supports VASP, QUANTUM ESPRESSO, OpenMX, soiap, and LAMMPS for local structure optimization and energy evaluation.
Tomoki Yamashita, Shinichi Kanehira, Nobuya Sato, Hiori Kino, Kei Terayama, Hikaru Sawahata, Takumi Sato, Futoshi Utsuno, Koji Tsuda, Takashi Miyake & Tamio Oguchi (2021) CrySPY: a crystal structure prediction tool accelerated by machine learning, Science and Technology of Advanced Materials: Methods, 1:1, 87-97, DOI: 10.1080/27660400.2021.1943171
crystal structure prediction, Evolutionary Algorithm, Bayesian Optimization, Quadratic Approximation,
Curator: qinyang