Yes, there are several alternative approaches to facilitate rapid transitions between metastable states in molecular dynamics simulations, beyond the refinement step currently employed in FBM. One promising method is the use of enhanced sampling techniques, such as metadynamics or umbrella sampling, which can bias the sampling process to explore rare events and transitions between states more effectively. These techniques allow for the construction of free energy landscapes that can guide the system through energy barriers, enabling faster transitions between metastable states.
Another approach is the implementation of machine learning-based methods, such as reinforcement learning or variational autoencoders, which can learn to navigate the conformational space more efficiently. By training models to recognize and exploit the pathways between metastable states, these methods can generate transitions that are both rapid and physically plausible.
Additionally, transition path sampling can be employed to focus on the most relevant pathways connecting metastable states. This method allows for the identification and sampling of transition trajectories, which can be used to inform the generation of new conformations that are more likely to represent significant transitions.
Lastly, collective variable-based methods can be utilized to define reaction coordinates that capture the essential degrees of freedom governing the transitions. By constraining the dynamics to these collective variables, the system can be guided through transitions more efficiently, reducing the time spent in less relevant conformational spaces.