Material synthesis and design from first principle calculations and machine learning
Abstract Desired material synthesis and design can be directly predicted on the basis of first principle calculations and machine learning. Material big data is constructed based on density functional theory where every possible element combinations are considered and then used as training sets for support vector machines. The predicted material properties for common materials are successfully matched with experimental data. In addition, material combinations based on desired material properties are also able to be predicted. Thus, the proposed work flow becomes the bridge between the material database and designing materials. The approach enables efficient material mining from material big data and could potentially reveal undiscovered desired materials. This approach could also potentially enable targeted material mining from material big data, the unveiling of undiscovered desired materials, and the execution of targeted material synthesis in experiment.