A review of advancements in coarse-grained molecular dynamics simulations

Over the last few years, coarse-grained molecular dynamics has emerged as a way to model large and complex systems in an efficient and inexpensive manner due to its lowered resolution, faster dynamics, and larger time steps. However, developing coarse-grained models and subsequently, the accurate interaction potentials (force-field parameters) is a challenging task. Traditional parameterisation techniques, although tedious, have been used extensively to develop CG models for a variety of solvent, soft-matter and biological systems. With the advent of sophisticated optimisation methods, machine learning, and hybrid approaches for force-field parameterisation, models with a higher degree of transferability and accuracy can be developed in a shorter period of time. We review here, some of these traditional and advanced parameterisation techniques while also shedding light on several transferable CG models developed in our group over the years using such an advanced method developed by us. These models, including solvents, polymers and biomolecules have helped us study important solute-solvent interactions and complex polymer architectures, thus paving a way to make experimentally verifiable observations.

Automated summary

Coarse-grained molecular dynamics has become an efficient way to model large systems, but developing models and accurate interaction potentials remains challenging. Traditional parameterisation techniques, while tedious, are still widely used, and advanced optimization methods and machine learning can help develop models with higher transferability and accuracy more quickly.