Coarse Grained Molecular

Coarse-grained molecular modeling simplifies complex molecular systems by representing groups of atoms as single particles, enabling simulations at larger scales and longer timescales than all-atom methods. Current research focuses on improving the accuracy and transferability of coarse-grained force fields, employing machine learning techniques like graph neural networks and diffusion models to learn effective inter-particle interactions from data, often bypassing traditional force-matching approaches. These advancements are significantly accelerating simulations of diverse systems, from protein folding to material fracture, offering valuable insights into complex biological and material processes at previously inaccessible scales.