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\begin{document} |
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|
| 827 |
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300K using a range of energy fluxes. Uncertainties are |
| 828 |
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indicated in parentheses. } |
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|
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< |
\begin{tabular}{|cccc|} |
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> |
\begin{tabular}{|cccc| } |
| 831 |
|
\hline |
| 832 |
|
$J_z$ (MW/m$^2$) & $\langle T_{gold} \rangle$ (K) & $\langle |
| 833 |
|
T_{water} \rangle$ (K) & $G$ |
| 850 |
|
systems. Simulation results demonstrate its validity in thermal |
| 851 |
|
conductivity calculations, from Lennard-Jones fluid to multi-atom |
| 852 |
|
molecule like water and metal crystals. NIVS-RNEMD improves |
| 853 |
< |
non-Boltzmann-Maxwell distributions, which exist in previous RNEMD |
| 853 |
> |
non-Boltzmann-Maxwell distributions, which exist inb previous RNEMD |
| 854 |
|
methods. Furthermore, it develops a valid means for unphysical thermal |
| 855 |
|
transfer between different species of molecules, and thus extends its |
| 856 |
|
applicability to interfacial systems. Our calculation of gold/water |
| 867 |
|
the Center for Research Computing (CRC) at the University of Notre |
| 868 |
|
Dame. \newpage |
| 869 |
|
|
| 866 |
– |
\bibliographystyle{aip} |
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\bibliography{nivsRnemd} |
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|
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\end{doublespace} |
