title: 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 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.
authors.affiliation: 1] Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742, USA [2] National Institute of Standards and Technology, Gaithersburg, MD 20899, USA [3].
authors.forename: Aaron Gilad
authors.initials: AG
authors.lastname: Kusne
authors.affiliation: 1] Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742, USA [2].
authors.forename: Tieren
authors.initials: T
authors.lastname: Gao
authors.affiliation: Stanford Synchrotron Radiation Lightsource, SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.
authors.forename: Apurva
authors.initials: A
authors.lastname: Mehta
authors.affiliation: Ames Laboratory, Iowa State University, Ames, IA 50011, USA.
authors.forename: Liqin
authors.initials: L
authors.lastname: Ke
authors.affiliation: 1] Ames Laboratory, Iowa State University, Ames, IA 50011, USA [2] Department of Physics and Astronomy, Iowa State University, Ames, IA, 50011, USA.
authors.forename: Manh Cuong
authors.initials: MC
authors.lastname: Nguyen
authors.affiliation: 1] Ames Laboratory, Iowa State University, Ames, IA 50011, USA [2] Department of Physics and Astronomy, Iowa State University, Ames, IA, 50011, USA.
authors.forename: Kai-Ming
authors.initials: KM
authors.lastname: Ho
authors.affiliation: Ames Laboratory, Iowa State University, Ames, IA 50011, USA.
authors.forename: Vladimir
authors.initials: V
authors.lastname: Antropov
authors.affiliation: 1] Ames Laboratory, Iowa State University, Ames, IA 50011, USA [2] Department of Physics and Astronomy, Iowa State University, Ames, IA, 50011, USA.
authors.forename: Cai-Zhuang
authors.initials: CZ
authors.lastname: Wang
authors.affiliation: Ames Laboratory, Iowa State University, Ames, IA 50011, USA.
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Kramer
authors.affiliation: Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742, USA.
authors.forename: Christian
authors.initials: C
authors.lastname: Long
authors.affiliation: Department of Materials Science and Engineering, University of Maryland, College Park, MD 20742, USA.
authors.forename: Ichiro
authors.initials: I
authors.lastname: Takeuchi