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

Kusne, Aaron Gilad; Gao, Tieren; Mehta, Apurva; Ke, Liqin; Nguyen, Manh Cuong; Ho, Kai-Ming; Antropov, Vladimir; Wang, Cai-Zhuang; Kramer, Matthew J; Long, Christian; Takeuchi, Ichiro

doi:10.1038/srep06367

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

Journal Article · Mon Sep 15 00:00:00 EDT 2014 · Scientific Reports

DOI:https://doi.org/10.1038/srep06367· OSTI ID:1166896

Kusne, Aaron Gilad ^[1]; Gao, Tieren ^[1]; Mehta, Apurva ^[2]; Ke, Liqin ^[3]; Nguyen, Manh Cuong ^[3]; Ho, Kai-Ming ^[3]; Antropov, Vladimir ^[3]; Wang, Cai-Zhuang ^[3]; Kramer, Matthew J ^[3]; Long, Christian ^[1]; Takeuchi, Ichiro ^[1]

University of Maryland
SLAC National Accelerator Laboratory
Ames Laboratory

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 data with the inorganic crystal structure database (ICSD), we can substantially enhance the accuracy in classifying the structural phases across ternary phase spaces. We have used this approach to identify a novel magnetic phase with enhanced magnetic anisotropy which is a candidate for rare-earth free permanent magnet.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Ames Laboratory (AMES), Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

DOE Contract Number:: DE-AC02-07CH11358

OSTI ID:: 1166896

Report Number(s):: IS-J 8483

Journal Information:: Scientific Reports, Vol. 4; ISSN 2045-2322

Country of Publication:: United States

Language:: English

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