Coarse Grained Force Field

Coarse-grained (CG) force fields simplify molecular simulations by representing groups of atoms as single interaction sites, enabling studies of larger systems and longer timescales than all-atom methods. Current research heavily utilizes machine learning, particularly graph neural networks and normalizing flows, to efficiently and accurately parameterize these CG force fields, often focusing on improving their transferability across different thermodynamic conditions. This enhanced efficiency and accuracy is crucial for advancing our understanding of complex molecular processes in fields like materials science and drug discovery, where detailed simulations of large biomolecules are essential.