Temperature dependence of coarse-grained potentials for liquid hexane
Literature Information
Karim Farah, Aoife Catherine Fogarty, Michael Christian Böhm, Florian Müller-Plathe
The present molecular dynamics study is an investigation of the temperature (T) dependence of liquid hexane coarse-grained potentials optimized with the Iterative Boltzmann Inversion method. An approach for the derivation of coarse-grained potentials at temperatures T different from the optimization temperature T0 has recently been proposed for ethylbenzene. This method is based on the use of a T-dependent scaling factor f(T) to generate ethylbenzene potentials at T ≠ T0. The approach is here extended to hexane, considering different reference temperatures T0 and functional forms for f(T). From our simulations, it appears that the accuracy of the temperature transferability depends simultaneously on the T0 chosen and the analytic form of f(T). Such a behavior is suppressed by the use of a new 2-point interpolation formula to generate coarse-grained potentials as a function of T. This scheme employs a linear interpolation based on the optimization of coarse-grained potentials at two reference temperatures, TL and TU, with TL ≤ T ≤ TU. Accurate coarse-grained simulations of liquid hexane can be performed using the new interpolation scheme. The results are encouraging for the use of potential interpolations as a practical means for devising coarse-grained potentials within a wider temperature range.
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Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.










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