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Machine Learning and High-Throughput Approaches to Magnetism

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Handbook of Materials Modeling

Abstract

Magnetic materials have underpinned human civilization for at least one millennium and now find applications in the most diverse technologies, ranging from data storage, to energy production and delivery, to sensing. Such great diversity, associated to the fact that only a limited number of elements can sustain a magnetic order, makes magnetism rare and fascinating. The discovery of a new high-performance magnet is often a complex process, where serendipity plays an important role. Here we present a range of novel approaches to the discovery and design of new magnetic materials, which is rooted in high-throughput electronic structure theory and machine learning models. Such combination of methods has already demonstrated the ability of discovering ferromagnets with high Curie temperature at an unprecedented speed.

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Acknowledgements

This work is supported by Science Foundation Ireland (Grants No. 14/IA/2624). JN thank the Irish Research Council for financial support. SC and CO acknowledge support by DOD-ONR (N00014-13-1-0635, N00014-15-1-2863, N00014-16-1-2326) and the consortium AFLOW.org – Duke University – for computational assistance. SC acknowledges the Alexander von Humboldt Foundation for financial support.

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Authors and Affiliations

  1. School of Physics and CRANN Institute, Trinity College, Dublin, Ireland

    S. Sanvito, M. Žic, J. Nelson & T. Archer

  2. Center for Materials Genomics, Duke University, Durham, NC, USA

    C. Oses & S. Curtarolo

  3. Departments of Mechanical Engineering and Materials Science, Physics, and Chemistry, Duke University, Durham, NC, USA

    C. Oses & S. Curtarolo

Authors
  1. S. Sanvito
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  2. M. Žic
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  3. J. Nelson
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  4. T. Archer
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  5. C. Oses
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  6. S. Curtarolo
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Corresponding author

Correspondence to S. Sanvito .

Editor information

Editors and Affiliations

  1. Institute of Physics, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Wanda Andreoni

  2. Department of Nuclear Science & Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sidney Yip

Section Editor information

  1. Department of Physics, Trinity College, Dublin, Ireland

    Stefano Sanvito

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Sanvito, S., Žic, M., Nelson, J., Archer, T., Oses, C., Curtarolo, S. (2018). Machine Learning and High-Throughput Approaches to Magnetism. In: Andreoni, W., Yip, S. (eds) Handbook of Materials Modeling. Springer, Cham. https://doi.org/10.1007/978-3-319-50257-1_108-1

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  • DOI: https://doi.org/10.1007/978-3-319-50257-1_108-1

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