Derivation of coarse-grained potentials via multistate iterative Boltzmann inversion

Area of Science:

• Computational chemistry
• Statistical mechanics
• Materials science

Background:

• Coarse-grained (CG) potentials are essential for simulating large molecular systems.
• The standard iterative Boltzmann inversion (IBI) method derives CG potentials from simulation data.
• State-dependent potentials limit the applicability of CG models across different thermodynamic conditions.

Purpose of the Study:

• To extend the iterative Boltzmann inversion (IBI) method for deriving less state-dependent coarse-grained potentials.
• To improve the accuracy and transferability of CG potentials for molecular simulations.
• To enable CG models to accurately represent systems across a range of thermodynamic states.

Main Methods:

• An extension to the standard iterative Boltzmann inversion (IBI) method was developed.
• The enhanced IBI method incorporates target data from multiple thermodynamic states.
• The algorithm was tested on systems with known potentials and for predicting n-alkane chain behavior.

Main Results:

• The multi-state IBI method yields potentials that are less state-dependent than standard IBI.
• The derived potentials better represent underlying molecular interactions by matching radial distribution functions at multiple state points.
• The enhanced method accurately predicts the behavior of n-alkane chains and can be tuned to match experimental data.

Conclusions:

• The proposed multi-state IBI method enhances the accuracy and applicability of coarse-grained potentials.
• This approach allows for more reliable simulations of systems under varying thermodynamic conditions.
• Tuning state weights offers a pathway to further refine potentials for specific applications.