First-principles prediction of the softening of the silicon shock Hugoniot curve
Abstract
Here, whock compression of silicon (Si) under extremely high pressures (>100 Mbar) was investigated by using two first-principles methods of orbital-free molecular dynamics (OFMD) and path integral Monte Carlo (PIMC). While pressures from the two methods agree very well, PIMC predicts a second compression maximum because of 1s electron ionization that is absent in OFMD calculations since Thomas–Fermi-based theories lack inner shell structure. The Kohn–Sham density functional theory is used to calculate the equation of state (EOS) of warm dense silicon for low-pressure loadings (P < 100 Mbar). Combining these first-principles EOS results, the principal Hugoniot curve of silicon for pressures varying from 0.80 Mbar to above ~10 Gbar was derived. We find that silicon is ~20% or more softer than what was predicted by EOS models based on the chemical picture of matter. Existing experimental data (P ≈ 1–2 Mbar) seem to indicate this softening behavior of Si, which calls for future strong-shock experiments (P > 10 Mbar) to benchmark our results.
- Authors:
-
- Univ. of Rochester, Rochester, NY (United States)
- Univ. of California, Berkeley, CA (United States)
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Publication Date:
- Research Org.:
- Univ. of Rochester, NY (United States). Lab. for Laser Energetics
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1328774
- Alternate Identifier(s):
- OSTI ID: 1324863
- Grant/Contract Number:
- NA0001944; AC52-06NA25396; SC0010517
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Physical Review B
- Additional Journal Information:
- Journal Volume: 94; Journal Issue: 9; Journal ID: ISSN 2469-9950
- Publisher:
- American Physical Society (APS)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 36 MATERIALS SCIENCE
Citation Formats
Hu, S. X., Militzer, B., Collins, L. A., Driver, K. P., and Kress, J. D. First-principles prediction of the softening of the silicon shock Hugoniot curve. United States: N. p., 2016.
Web. doi:10.1103/PhysRevB.94.094109.
Hu, S. X., Militzer, B., Collins, L. A., Driver, K. P., & Kress, J. D. First-principles prediction of the softening of the silicon shock Hugoniot curve. United States. https://doi.org/10.1103/PhysRevB.94.094109
Hu, S. X., Militzer, B., Collins, L. A., Driver, K. P., and Kress, J. D. Thu .
"First-principles prediction of the softening of the silicon shock Hugoniot curve". United States. https://doi.org/10.1103/PhysRevB.94.094109. https://www.osti.gov/servlets/purl/1328774.
@article{osti_1328774,
title = {First-principles prediction of the softening of the silicon shock Hugoniot curve},
author = {Hu, S. X. and Militzer, B. and Collins, L. A. and Driver, K. P. and Kress, J. D.},
abstractNote = {Here, whock compression of silicon (Si) under extremely high pressures (>100 Mbar) was investigated by using two first-principles methods of orbital-free molecular dynamics (OFMD) and path integral Monte Carlo (PIMC). While pressures from the two methods agree very well, PIMC predicts a second compression maximum because of 1s electron ionization that is absent in OFMD calculations since Thomas–Fermi-based theories lack inner shell structure. The Kohn–Sham density functional theory is used to calculate the equation of state (EOS) of warm dense silicon for low-pressure loadings (P < 100 Mbar). Combining these first-principles EOS results, the principal Hugoniot curve of silicon for pressures varying from 0.80 Mbar to above ~10 Gbar was derived. We find that silicon is ~20% or more softer than what was predicted by EOS models based on the chemical picture of matter. Existing experimental data (P ≈ 1–2 Mbar) seem to indicate this softening behavior of Si, which calls for future strong-shock experiments (P > 10 Mbar) to benchmark our results.},
doi = {10.1103/PhysRevB.94.094109},
journal = {Physical Review B},
number = 9,
volume = 94,
place = {United States},
year = {Thu Sep 15 00:00:00 EDT 2016},
month = {Thu Sep 15 00:00:00 EDT 2016}
}
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