Esselink Method
1. Generate k trials from the present configuration
- each trial handled by a different processor
- useful if trials difficult to generate (e.g., chain configurational bias)
2. Compute an appropriate weight W(i) for each new trial
- e.g., Rosenbluth weight if CCB
- more simply, W(i) = exp[-U(i)/kT]
3. Define a normalization factor
4. Select one trial with probability
Now account for reverse trial:
5. Pick a molecule from original configuration
- Need to evaluate a probability it would be generated from trial configuration
- Plan: Choose a path that includes the other (ignored) trials
6. Compute the reverse-trial W(o)