Combinatorial experimentation and machine learning for materials discovery
Ichiro Takeuchi, University of Maryland, College Park, USA
Over the years, the challenges in the high-throughput combinatorial approach has evolved from synthesis of large numbers of disparate compounds to developing quantitatively accurate rapid characterization tools to analysis and digestion of large amount of data churned out by the methodology. To address the last challenge, we are increasingly relying on machine learning techniques including pattern recognition within diffraction data to construct phase diagrams and mining experimental databases to look for trends in materials properties for future predictions. I will discuss our latest effort where active learning is used to design and steer the sequence of experiments in order to maximize attainable knowledge, minimize experimental resources, and as a result further speed up the materials discovery process.