Complex system

Engineering

Pure mathematics

Biology

Materials science

Variety (cybernetics)

Verifiable secret sharing

Field (mathematics)

Set (abstract data type)

Computer science

Statistical physics

Biochemical engineering

Physics

Nanotechnology

Mathematics

Transferability

Molecular dynamics

Computational chemistry

Artificial intelligence

Logit

Chemistry

Programming language

Biological system

Force field (fiction)

Machine learning

Molecular Simulation

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.

A review of advancements in coarse-grained molecular dynamics simulations