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Development of DPD coarse-grained models: From bulk to interfacial properties.
José G Solano Canchaya1, Alain Dequidt1, Florent Goujon1
1Institut de Chimie de Clermont-Ferrand, Université Blaise Pascal, Université Clermont Auvergne, BP 10448, F-63000 Clermont-Ferrand, France.
The Journal of Chemical Physics
|August 8, 2016
Summary
A new Bayesian method enhances coarse-grained (CG) force fields for molecular dynamics by optimizing dissipative particle dynamics (DPD) models. This approach improves transferability across thermodynamic conditions, accurately predicting liquid properties like surface tension.
Area of Science:
- Computational chemistry
- Materials science
- Statistical mechanics
Background:
- Developing accurate coarse-grained (CG) force fields is crucial for large-scale molecular dynamics simulations.
- Existing methods for optimizing CG models, such as those for dissipative particle dynamics (DPD), often struggle with transferability across different thermodynamic conditions.
- Bayesian approaches offer a promising framework for robust force field development.
Purpose of the Study:
- To extend a recently introduced Bayesian method for developing CG force fields.
- To improve the transferability of CG models across various thermodynamic conditions.
- To develop and validate a CG-DPD model for n-pentane, focusing on its predictive accuracy for thermophysical properties.
Main Methods:
- Utilized a Bayesian framework to optimize CG force fields based on trajectory matching.
- Extended the method to enhance model transferability across thermodynamic states.
- Developed a CG-DPD model for n-pentane using constant-NPT atomistic simulations.
- Applied the developed CG-DPD model to calculate surface tension at the liquid-vapor interface over a wide temperature range.
Main Results:
- Successfully developed a transferable CG-DPD model for n-pentane.
- Calculated coexisting densities, vapor pressures, and surface tensions using the CG-DPD model.
- Demonstrated that the accuracy of the CG model's predictions, particularly for surface tension, depends on the training data used.
- Achieved good reproduction of experimental surface tension data on the orthobaric curve.
Conclusions:
- The extended Bayesian method provides a robust approach for developing transferable CG force fields.
- The developed CG-DPD model for n-pentane shows good predictive capabilities for thermophysical properties, including surface tension.
- Careful selection of simulation data for force field development is critical for achieving high accuracy and transferability.
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