ramess101
commented
6 years ago Here are my tentative responses:
1) We should try to simulate closer to the critical point (using larger systems and density distributions) 2) Forgot to mention, the distribution approach probably requires more frequent output of data and/or longer simulations 3) For my PhD I used much lower volume and swap probabilities. This would speed up the computations for an MCC significantly, but could require more MCCs for adequate sampling. I think there is a different study that addresses this. 4) My ratio is about 14%, which is good enough I think. 5) Yeah, I think we want to perform simulations with varying system sizes 6) Probably should do this as well 7) No. I think we want to use bootstrapping. We can include the uncertainty in beta. We can exclude/include different temperatures. And we can combine the Towhee and Cassandra results randomly. This would probably require an equal number of replicates though, and we don't really have Towhee results for all the system sizes and/or pressures.
This study by Siepmann's group demonstrates a robust approach for estimating the critical point:
The question is, do we need this type of precision? For example:
1) Do we need to simulate that close to the critical point 2) If so, we need to use density distribution functions. If not, we probably don't need to use density distributions. 3) How important are the MC moves? 4) How important is the liquid to vapor ratio? 5) Do we need to use very large systems? 6) Do we need to vary the cut-off? 7) Do we need to use their approach for estimating uncertainty?