4.8 Article

Metallization of diamond

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.2013565117

Keywords

elastic strain engineering; machine learning; multiscale simulations; metallic diamond; materials under extreme conditions

Funding

  1. Office of Naval Research Multidisciplinary University Research Initiative [N00014-18-1-2497]
  2. Massachusetts Institute of Technology (MIT) Skoltech Next Generation Program [2016-7/NGP]
  3. Center for Integrated Nanotechnologies, an Office of Science User Facility [89233218CNA000001]
  4. Sandia National Laboratories [DE-NA-0003525]
  5. MIT J-Clinic for Machine Learning and Health
  6. Nanyang Technological University through the Distinguished University Professorship

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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.

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