Thermal conductivity calculation with the molecular dynamics direct method II: Improving the computational efficiency

Journal of Computational and Theoretical Nanoscience

Volumn 8, Issue 10, 2011, Pages 2144-2154

  Howell, P C ^a  

^a SIEMENS AG   (Germany)

Author keywords

Direct Method; Molecular Dynamics; Statistical Uncertainty; Thermal Conductivity

Indexed keywords

BULK SYSTEM; COMPUTATIONAL EFFORT; CONFIDENCE INTERVAL; DIRECT METHOD; ERROR PROPAGATION; LONG DURATION; NON EQUILIBRIUM; OPTIMAL SETS; SEMI CONDUCTOR; SIMULATION DATA; STATISTICAL UNCERTAINTY; THERMAL CONDUCTORS;

MOLECULAR DYNAMICS;

THERMAL CONDUCTIVITY;

EID: 84856868368     PISSN: 15461955     EISSN: None     Source Type: Journal    
DOI: 10.1166/jctn.2011.1936     Document Type: Article

References (23)

1. A. J. H. McGaughey and M. Kaviany, Advances in Heat Transfer 39, 169 (2006).
2. S. Stackhouse and L. Stixrude, Reviews in Mineralogy and Geochemistry 71, 253 (2010).
3. P. K. Schelling, S. R. Phillpot, and P. Keblinski, Phys. Rev. B 65, 144306 (2002).
4. X. W. Zhou, S. Aubry, R. E. Jones, A. Greenstein, and P. K. Schelling, Phys. Rev. B 79, 115201 (2009).
5. P. C. Howell, J. Comput. Theor. Nanosci. 8, 2129 (2011).
6. C. Oligschleger and J. C. Schön, Phys. Rev. B 59, 4125 (1999).
7. D. P. Sellan, E. S. Landry, J. E. Turney, A. J. H. McGaughey, and C. H. Amon, Phys. Rev. B 81, 214305 (2010).
8. N. Ohtori, M. Salanne, and P. A. Madden, J. Chem. Phys. 130, 104507 (2009).
9. S. Stackhouse, L. Stixrude, and B. B. Karki, Phys. Rev. Lett. 104, 208501 (2010).
10. P. K. Schelling, Computational Materials Science 48, 336 (2010).
11. E. S. Landry and A. J. H. McGaughey, Phys. Rev. B 79, 075316 (2009).
12. J. R. Lukes and H. Zhong, J. Heat Transfer 129, 705 (2007).
13. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical recipes: The art of scientific computing, Cambridge University Press, Cambridge (2007).
14. B. L. Holian, A. F. Voter, and R. Ravelo, Phys. Rev. E 52, 2338 (1995).
15. P. Heino, Phys. Rev. B 71, 144302 (2005).
16. P. K. Schelling and S. R. Phillpot, J. Am. Ceram. Soc. 84, 2997 (2001).
17. J. R. Lukes, D. Y. Li, X.-G. Liang, and C.-L. Tien, Transactions of the ASME 122, 536 (2000).
18. A. S. Henry and G. Chen, J. Comput. Theor. Nanosci. 5, 141 (2008).
19. T. Watanabe, S. G. Srivilliputhur, P. K. Schelling, J. S. Tulenko, S. B. Sinnott, and S. R. Phillpot, J. Am. Ceram. Soc. 92, 850 (2009).
20. P. Shukla, T. Watanabe, J. C. Nino, J. S. Tulenko, and S. R. Phillpot, J. Nucl. Mater. 380, 1 (2008).
21. F. Müller-Plathe, J. Chem. Phys. 106, 6082 (1997).
22. S. J. Plimpton, J. Comput. Phys. 117, 1 (1995).
23. Note that this plot provides an intuitive reason why tN-2 is large when N is small. If only a few data-points are available, the risk of under-estimating σk∞-1 due to sampling error is not negligible. tN-2 is a scaling factor in the y-direction which by construction ensures that 95% of the estimated confidence intervals encompass the correct value, i.e., that they lie above the line x = y, and so it must then be large enough to compensate for this risk.