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Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition

Machine Learning and High-Throughput Approaches to Magnetism

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Sanvito, S; Žic, M; Nelson, J; Archer, T; Oses, C; Curtarolo, S
January 1, 2020

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.

Duke Scholars

DOI

ISBN

9783319446790

Publication Date

January 1, 2020

Start / End Page

351 / 373
 

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Sanvito, S., Žic, M., Nelson, J., Archer, T., Oses, C., & Curtarolo, S. (2020). Machine Learning and High-Throughput Approaches to Magnetism. In Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition (pp. 351–373). https://doi.org/10.1007/978-3-319-44680-6_108
Sanvito, S., M. Žic, J. Nelson, T. Archer, C. Oses, and S. Curtarolo. “Machine Learning and High-Throughput Approaches to Magnetism.” In Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition, 351–73, 2020. https://doi.org/10.1007/978-3-319-44680-6_108.
Sanvito S, Žic M, Nelson J, Archer T, Oses C, Curtarolo S. Machine Learning and High-Throughput Approaches to Magnetism. In: Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition. 2020. p. 351–73.
Sanvito, S., et al. “Machine Learning and High-Throughput Approaches to Magnetism.” Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition, 2020, pp. 351–73. Scopus, doi:10.1007/978-3-319-44680-6_108.
Sanvito S, Žic M, Nelson J, Archer T, Oses C, Curtarolo S. Machine Learning and High-Throughput Approaches to Magnetism. Handbook of Materials Modeling: Applications: Current and Emerging Materials, Second Edition. 2020. p. 351–373.
Journal cover image

DOI

ISBN

9783319446790

Publication Date

January 1, 2020

Start / End Page

351 / 373