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Metallization of diamond.

Zhe ShiMing DaoEvgenii TsymbalovAlexander ShapeevJu LiSubra Suresh
Published in: Proceedings of the National Academy of Sciences of the United States of America (2020)
Experimental discovery of ultralarge elastic deformation in nanoscale diamond and machine learning of its electronic and phonon structures have created opportunities to address new scientific questions. Can diamond, with an ultrawide bandgap of 5.6 eV, be completely metallized, solely under mechanical strain without phonon instability, so that its electronic bandgap fully vanishes? Through first-principles calculations, finite-element simulations validated by experiments, and neural network learning, we show here that metallization/demetallization as well as indirect-to-direct bandgap transitions can be achieved reversibly in diamond below threshold strain levels for phonon instability. We identify the pathway to metallization within six-dimensional strain space for different sample geometries. We also explore phonon-instability conditions that promote phase transition to graphite. These findings offer opportunities for tailoring properties of diamond via strain engineering for electronic, photonic, and quantum applications.
Keyphrases
  • neural network
  • machine learning
  • molecular dynamics
  • finite element
  • monte carlo
  • small molecule
  • high resolution
  • mass spectrometry