Abstract
In general, the design space of materials research is quite huge. The goal of materials informatics (MI) is to discover new materials or design parameters that exhibit innovative properties from such a vast search space. The basic workflow of MI consists of forward and inverse problems. The objective of the forward problem is to obtain a statistical model that forwardly predicts physicochemical properties of input materials. The inverse problem, on the other hand, predicts candidate materials with a given set of desired properties by finding the inverse map of the forward model. Here, we focus on the integration of machine learning and simulation in materials science. The biggest barrier to the implementation of data-driven materials science is the lack of a sufficient amount of data. In addition, the goal of materials research is to discover innovative materials that exist in unexplored areas where less or no data exists. Therefore, interpolative predictions based only on conventional data-driven approaches are generally insufficient to achieve this goal. This talk will present some case studies of materials exploration based on transfer learning and adaptive design of computer experiments on a high-dimensional design space, which will be the key to bridging the computer experiments and the machine learning workflow in materials science.
