Machine-Enabled Inverse Design of Solid-State Materials
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정유성 KAIST Discovery of a new material with desired properties is the ultimate goal of materials research. To date, a generally successful strategy has been to use chemical intuition and empirical rules to design new materials, but these conventional approaches require a significant amount of time and cost due to almost unlimited combinatorial possibilities of inorganic materials to explore in chemical space. 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 in any flavors of 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. |