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Data-driven design of molecular nanomagnets

Author

Listed:
  • Yan Duan

    (Universitat de València
    South China University of Technology)

  • Lorena E. Rosaleny

    (Universitat de València)

  • Joana T. Coutinho

    (Universitat de València
    Polytechnic of Leiria)

  • Silvia Giménez-Santamarina

    (Universitat de València)

  • Allen Scheie

    (Oak Ridge National Laboratory)

  • José J. Baldoví

    (Universitat de València)

  • Salvador Cardona-Serra

    (Universitat de València)

  • Alejandro Gaita-Ariño

    (Universitat de València)

Abstract

Three decades of research in molecular nanomagnets have raised their magnetic memories from liquid helium to liquid nitrogen temperature thanks to a wise choice of the magnetic ion and coordination environment. Still, serendipity and chemical intuition played a main role. In order to establish a powerful framework for statistically driven chemical design, here we collected chemical and physical data for lanthanide-based nanomagnets, catalogued over 1400 published experiments, developed an interactive dashboard (SIMDAVIS) to visualise the dataset, and applied inferential statistical analysis. Our analysis shows that the Arrhenius energy barrier correlates unexpectedly well with the magnetic memory. Furthermore, as both Orbach and Raman processes can be affected by vibronic coupling, chemical design of the coordination scheme may be used to reduce the relaxation rates. Indeed, only bis-phthalocyaninato sandwiches and metallocenes, with rigid ligands, consistently present magnetic memory up to high temperature. Analysing magnetostructural correlations, we offer promising strategies for improvement, in particular for the preparation of pentagonal bipyramids, where even softer complexes are protected against molecular vibrations.

Suggested Citation

  • Yan Duan & Lorena E. Rosaleny & Joana T. Coutinho & Silvia Giménez-Santamarina & Allen Scheie & José J. Baldoví & Salvador Cardona-Serra & Alejandro Gaita-Ariño, 2022. "Data-driven design of molecular nanomagnets," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35336-9
    DOI: 10.1038/s41467-022-35336-9
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    References listed on IDEAS

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    1. Vahe Tshitoyan & John Dagdelen & Leigh Weston & Alexander Dunn & Ziqin Rong & Olga Kononova & Kristin A. Persson & Gerbrand Ceder & Anubhav Jain, 2019. "Unsupervised word embeddings capture latent knowledge from materials science literature," Nature, Nature, vol. 571(7763), pages 95-98, July.
    2. Conrad A. P. Goodwin & Fabrizio Ortu & Daniel Reta & Nicholas F. Chilton & David P. Mills, 2017. "Molecular magnetic hysteresis at 60 kelvin in dysprosocenium," Nature, Nature, vol. 548(7668), pages 439-442, August.
    3. Tiantian Zhang & Yi Jiang & Zhida Song & He Huang & Yuqing He & Zhong Fang & Hongming Weng & Chen Fang, 2019. "Catalogue of topological electronic materials," Nature, Nature, vol. 566(7745), pages 475-479, February.
    4. Keith T. Butler & Daniel W. Davies & Hugh Cartwright & Olexandr Isayev & Aron Walsh, 2018. "Machine learning for molecular and materials science," Nature, Nature, vol. 559(7715), pages 547-555, July.
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