CRYSPNet: Machine Learning Tool for Crystal Structure Predictions

Structure is the most basic and important property of crystalline solids; it determines directly or indirectly most other materials characteristics. However, predicting the crystal structure of solids remains a formidable problem; standard theoretical tools for the task are computationally expensive and not always reliable. As an alternative, we developed a tool, CRYSPNet, that can predict the Bravais lattice, space group, and lattice parameters of a material based on its chemical formula. It consists of a bag of neural network models with predictors based on aggregate features of the elements constituting the compound. The tool was trained and validated on more than 100,000 entries from the Inorganic Crystal Structure Database (ICSD). It demonstrates good predictive power and significantly outperforms naive strategies. CRYSPNet is easy to use and can be combined with tools for generating Wyckoff positions to create candidate structures for further exploration or refinement. Furthermore, activations from the hidden layers of the model can be used to measure the chemical and structural similarity between materials, which in turn can be used for predictions of materials with new functionalities.