volume 192 pages 179-186

Atomistic structure and anomalous heat capacity of low-density liquid carbon: Molecular dynamics study with machine learning potential

Publication typeJournal Article
Publication date2022-06-01
scimago Q1
wos Q1
SJR2.320
CiteScore21.4
Impact factor11.6
ISSN00086223, 18733891
General Chemistry
General Materials Science
Abstract
Liquid carbon remains the source of several unsolved questions related to its structure and region of thermodynamic stability. Experiments demonstrate a drastic decrease in the density for liquid carbon along the graphite melting line in the pressure range P = 1–3 GPa and the nature of this phenomenon is unclear. A recent experimental study [A.M. Kondratyev and A.D. Rakhel, PRL (2019)] revealed another peculiar and still unexplained feature of the liquid carbon – its excessive heat capacity. Using classical molecular dynamics with machine learning potential GAP-20, we study the structural properties of liquid carbon and demonstrate that at P < 1–2 GPa it resembles a net of sp -hybridized chains, rather than a typical covalent liquid, with nanoscale porosity of this phase being responsible for the density decrease. We also show that excessive heat capacity could be a direct manifestation of a smooth transition from a high-density sp 2 -hybridized phase into a low-density sp -hybridized.
Found 
Found 

Top-30

Journals

1
2
3
Journal of Molecular Liquids
3 publications, 15%
Carbon
1 publication, 5%
JETP Letters
1 publication, 5%
Journal of Chemical Physics
1 publication, 5%
Condensed Matter
1 publication, 5%
Modern Physics Letters B
1 publication, 5%
Applied Physics Letters
1 publication, 5%
International Journal of Heat and Mass Transfer
1 publication, 5%
High Temperature
1 publication, 5%
Fluid Phase Equilibria
1 publication, 5%
Technical Physics
1 publication, 5%
Письма в Журнал экспериментальной и теоретической физики
1 publication, 5%
Теплофизика высоких температур
1 publication, 5%
Sustainability
1 publication, 5%
APL Machine Learning
1 publication, 5%
Physics-Uspekhi
1 publication, 5%
Progress in Natural Science: Materials International
1 publication, 5%
1
2
3

Publishers

1
2
3
4
5
6
7
Elsevier
7 publications, 35%
Pleiades Publishing
3 publications, 15%
AIP Publishing
3 publications, 15%
MDPI
2 publications, 10%
Institute of Electrical and Electronics Engineers (IEEE)
1 publication, 5%
World Scientific
1 publication, 5%
Akademizdatcenter Nauka
1 publication, 5%
The Russian Academy of Sciences
1 publication, 5%
Uspekhi Fizicheskikh Nauk Journal
1 publication, 5%
1
2
3
4
5
6
7
  • We do not take into account publications without a DOI.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
20
Share
Cite this
GOST |
Cite this
GOST Copy
Orekhov N. D. et al. Atomistic structure and anomalous heat capacity of low-density liquid carbon: Molecular dynamics study with machine learning potential // Carbon. 2022. Vol. 192. pp. 179-186.
GOST all authors (up to 50) Copy
Orekhov N. D., Logunov M. V. Atomistic structure and anomalous heat capacity of low-density liquid carbon: Molecular dynamics study with machine learning potential // Carbon. 2022. Vol. 192. pp. 179-186.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1016/j.carbon.2022.02.058
UR - https://doi.org/10.1016/j.carbon.2022.02.058
TI - Atomistic structure and anomalous heat capacity of low-density liquid carbon: Molecular dynamics study with machine learning potential
T2 - Carbon
AU - Orekhov, Nikita D.
AU - Logunov, Mikhail V.
PY - 2022
DA - 2022/06/01
PB - Elsevier
SP - 179-186
VL - 192
SN - 0008-6223
SN - 1873-3891
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2022_Orekhov,
author = {Nikita D. Orekhov and Mikhail V. Logunov},
title = {Atomistic structure and anomalous heat capacity of low-density liquid carbon: Molecular dynamics study with machine learning potential},
journal = {Carbon},
year = {2022},
volume = {192},
publisher = {Elsevier},
month = {jun},
url = {https://doi.org/10.1016/j.carbon.2022.02.058},
pages = {179--186},
doi = {10.1016/j.carbon.2022.02.058}
}