Generative molecular dynamics
1Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, SE-41296, Sweden.
None:
Understanding biomolecular function depends on bridging experimental observables with models that capture structural, stationary, and dynamical properties. Molecular dynamics (MD) simulations, in principle provide a bridge, but the sampling problem remains a fundamental roadblock toward this goal. In this mini-review, I outline recent progress in the area of Generative MD (GenMD)-an approach where generative AI (GenAI) is used to mimic the statistical distributions resulting from MD simulations, which are inaccessible using current numerical algorithms. Here, I highlight a few exemplars of GenMD and then outline open problems and current limitations.
Related Concept Videos
Ziegler–Natta Chain-Growth Polymerization: Overview
Molecular Models
Step-Growth Polymerization: Overview
Many natural and synthetic polymers are produced by...
Radical Chain-Growth Polymerization: Mechanism
Radical Chain-Growth Polymerization: Overview
Molecular Weight of Step-Growth Polymers
As the step-growth polymerization involves step-wise condensation of monomers, the molecular weight also builds up eventually. Consequently, high molecular weight polymers are obtained at the late stages of the polymerization, where 99% of monomers have been consumed.
The extent of the...


