skip to main content

SciTech ConnectSciTech Connect

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

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.
 [1] ;  [1] ;  [2] ;  [3] ;  [3] ;  [3] ;  [3] ;  [3] ;  [3] ;  [1] ;  [1]
  1. University of Maryland
  2. SLAC National Accelerator Laboratory
  3. Ames Laboratory
Publication Date:
OSTI Identifier:
Report Number(s):
IS-J 8483
Journal ID: ISSN 2045-2322
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Scientific Reports; Journal Volume: 4
Research Org:
Ames Laboratory (AMES), Ames, IA (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States