An energy minimization strategy based on an improved nonlinear conjugate gradient method for accelerating the charged...

Area of Science:

• Computational Chemistry
• Polymer Physics
• Materials Science

Background:

• Simulating charged polymers requires efficient energy minimization techniques.
• Existing non-linear conjugate gradient (NCG) methods face challenges with large-scale optimization problems.
• Brownian dynamics (BD) is crucial for modeling polymer chain dynamics.

Purpose of the Study:

• To develop an improved NCG method for enhanced energy minimization in charged polymer simulations.
• To investigate the efficiency and accuracy of the proposed NCG coefficient (βLPRPk).
• To assess the impact of NCG-optimized configurations on pre-equilibrium simulations.

Main Methods:

• Hybrid simulation combining NCG for optimization and BD for polymer dynamics.
• Development and implementation of an improved NCG coefficient (βLPRPk) with global convergence properties.
• Comparison of the proposed NCG method against other numerical techniques for energy minimization.

Main Results:

• The βLPRPk coefficient demonstrates superior efficiency over existing NCG methods for practical test problems.
• NCG-optimized configurations significantly increase computational efficiency in pre-equilibrium simulations (up to 70x).
• Relative energy errors are controlled below 1 × 10⁻² and 4.5 × 10⁻³ for different systems.

Conclusions:

• The improved NCG method offers a cost-effective and efficient approach for energy minimization in polymer simulations.
• Applying NCG-optimized configurations enhances the computational performance of BD simulations.
• The hybrid NCG-BD approach provides accurate pre-equilibrium configurations comparable to traditional methods.