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Understanding and prediction of quantum materials via modelling and computation
Volume 44 Published: 23 October 2021 Article ID 0270
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Fulltext
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Permanent link:
https://www.ias.ac.in/article/fulltext/boms/044/0270 -
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.
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Author Affiliations
- Department of Condensed Matter Physics and Materials Science, S. N. Bose National Centre for Basic Sciences, Kolkata 700106, India
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Dates
- Published
- 23 October 2021
- Manuscript received
- 11 June 2021
- Accepted
- 8 September 2021
- Final version published online
- 23 October 2021
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Bulletin of Materials Science
Volume 46, 2023
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Bulletin of Materials Science | News
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Dr Shanti Swarup Bhatnagar for Science and Technology
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