A random forest trained on molecular simulations predicts nanoconfined fluid density profiles across temperatures and pore widths with good interpolation and modest extrapolation.
Nano-Confinement Effects on Liquid Pressure
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abstract
In this work, molecular dynamics simulations are performed to estimate the equilibrium pressure of liquid confined in nanopores. The simulations show that the pressure is highly sensitive to the pore size and can significantly change from absolute positive to negative values for a very small (0.1 nm) change in pore size. The contribution from the solid-liquid interaction always dominates the pressure in the first liquid layer adjacent to the surface and the sensitiveness of pressure on the pore size is due to the atom distribution in the liquid layers. A surface influence number S is introduced to quantitatively characterize the degree of the confinement. The S number decreases with increasing pore size based on a power law function at constant system temperature. In nanopores with large S number, the pore liquid pressure is found to be independent of bulk liquid pressure while the pore pressure increases with bulk pressure in nanopores with small S number.
fields
cond-mat.soft 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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How Machine Learning Predicts Fluid Densities under Nanoconfinement
A random forest trained on molecular simulations predicts nanoconfined fluid density profiles across temperatures and pore widths with good interpolation and modest extrapolation.