Lawrence Livermore National Laboratory (LLNL) scientists recently combined large-scale molecular dynamics simulations with machine learning interatomic potentials derived from first-principles calculations to examine the hydrogen bonding of water confined in carbon nanotubes (CNTs). They found that the narrower the diameter of the CNT, the more the water structure is affected in a highly complex and nonlinear fashion. The research appears on the cover of The Journal of Physical Chemistry Letters.
The post LLNL Researchers Harness Machine Learning for Advanced Simulations of Water Structure in Carbon Nanotubes appeared first on HPCwire.
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