Special CMSM Seminar No.315

Combinatorial studies based on the preparation and characterisation of compositionally graded thin films are being used for the screening and optimization of a range of functional materials [1]. When combined with Machine Learning (ML), such high-throughput film-based studies hold much potential to guide data driven design of materials [2,3]. In this talk I will present the entire workflow we have developed at Institut Néel for the high-throughput characterization and analysis of compositionally graded hard magnetic films [4]. I will then show some selected results from recent and on-going studies of the effect of composition and processing conditions on the structural and magnetic properties of different hard magnetic materials. I will finish up by briefly outlining preliminary results on the application of the approach to the study of magnetocaloric materials.

[1] ML Green et al., J. Appl. Phys. 113 (2013) 231101
[2] A.G. Kusne et al. Sci. Rep. 4 (2014) 6367
[3] A. Ludwig, npj Comput. Mater. 5 (2019) 70
[4] W. Rigaut et al., Advanced Engineering Materials 28 (2026) e70884

Hossein SEPEHRI-AMIN
Green Magnetic Materials Group
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