Machine Learning Based Materials Properties Prediction Platform for Fast Discovery of Advanced Materials

Recent impressive achievements on artificial intelligence and its technologies have expected to bring our daily life to the yet-experienced new world. Such technologies have also applied in the literature of materials science especially on data-driven materials research so that they could reduce the computing resources and alleviate redundant simulations. Nevertheless, since these are still in the immature stage, most of the datasets are private and have made according to their own standards and policies, therefore, they are hard to be merged as well as analyzed together. We have developed the Scientific Data Repository platform to store various and complicated data including materials data, which can analyze such data on the web. As the second step, we develop a machine learning based materials properties prediction tool enabling the fast discovery of advanced materials by using the general-purpose high-precise formation energy prediction module that performs MAE 0.066 within 10 s on the web.