Accelerate Chemical Science using Machine Learning

Discovery of a new material with desired properties is the ultimate goal of materials research. A promising way to significantly accelerate the latter process is to incorporate all available knowledge and data to plan the synthesis of the next material. In this talk, I will present several directions to use informatics to efficiently explore materials chemical space. I will first describe methods of machine learning for fast and reliable predictions of materials properties that can replace density functional calculations, an essential component needed for large scale materials design. With these tools in place for property evaluation, I will next present a few initial frameworks that we have recently developed to allow generative inverse design of inorganic crystals with optimal target properties, either in the compositional space or structural space. I will finally discuss several challenges and opportunities that lie ahead for further developments of accelerated materials platform, including synthesizability of inorganic crystals and organic molecules.

20210603_대학원세미나_정유성 교수.pdf