On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets.

ABSTRACT: Advanced materials characterization techniques with ever-growing data acquisition speed and storage capabilities represent a challenge in modern materials science, and new procedures to quickly assess and analyze the data are needed. Machine learning approaches are effective in reducing the complexity of data and rapidly homing in on the underlying trend in multi-dimensional data. Here, we show that by employing an algorithm called the mean shift theory to a large amount of diffraction data in high-throughput experimentation, one can streamline the process of delineating the structural evolution across compositional variations mapped on combinatorial libraries with minimal computational cost. Data collected at a synchrotron beamline are analyzed on the fly, and by integrating experimental

SUBMITTER: Kusne AG 

PROVIDER: S-EPMC4163667 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature