Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 117, Issue 40, Pages 24634-24639Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2013565117
Keywords
elastic strain engineering; machine learning; multiscale simulations; metallic diamond; materials under extreme conditions
Categories
Funding
- Office of Naval Research Multidisciplinary University Research Initiative [N00014-18-1-2497]
- Massachusetts Institute of Technology (MIT) Skoltech Next Generation Program [2016-7/NGP]
- Center for Integrated Nanotechnologies, an Office of Science User Facility [89233218CNA000001]
- Sandia National Laboratories [DE-NA-0003525]
- MIT J-Clinic for Machine Learning and Health
- Nanyang Technological University through the Distinguished University Professorship
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available