• Understanding and prediction of quantum materials via modelling and computation

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    • Keywords


      Quantum materials; density functional theory; machine learning; prediction.

    • Abstract


      This article provides a short review of interesting results on application of modelling and computation in understanding and prediction of quantum materials. Modelling and computation are used in understanding structure–property relationship, designing novel functionality in existing materials and prediction of new materials with targeted properties. Examples are drawn from applications in uncovering structure–property relation in high $T_c$ cuprate superconductors, low-dimensional quantum spin systems, engineering cooperative spin crossover phenomena in magnetic hybrid perovskites and coordination polymers, and machine learning assisted prediction of magnetic double perovskites and permanent magnets.

    • Author Affiliations



      1. Department of Condensed Matter Physics and Materials Science, S. N. Bose National Centre for Basic Sciences, Kolkata 700106, India
    • Dates

  • Bulletin of Materials Science | News

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    • Editorial Note on Continuous Article Publication

      Posted on July 25, 2019

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