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 and not fully solved problem; standard theoretical tools for the task are computationally expensive and not always reliable. In this talk I will present an alternative approach that utilizes machine learning for crystal structure predictions. We developed a tool that can predict the Bravais lattice, space group and lattice parameters of a material based only on its chemical composition. It consists of a series of neural network models with predictors based on aggregate properties 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 alternative strategies. This machine learning tool is easy to use, and can be utilized both as an independent prediction engine or as a data-informed method to generate candidate structures for further exploration.