Evolution of combinatorial materials science: From synthesis of large scale libraries to AI-driven materials discovery

抄録

With its ability to enable rapid screening of a large number of different materials, the combinatorial high-throughput approach has become an integral part of the experimental toolbox for materials exploration and discovery efforts across virtually all areas of materials science. With the advent of the Materials Genome Initiative in the U.S., high-throughput materials synthesis and characterization has come to play the complementing role to the surge of activities in computational materials science. In this article, I provide my perspective on how the combinatorial approach has evolved over the years and how informatics and machine learning have come to play a central role in the field.