I've found this paper which use Cross Validation and Gaussian Mixture Models to estimate Free Energy landscapes (they also compare them to other ways of estimating the densities), but apparently the GMM models are best for estimating the free energy values in the regions where data is sparse.
They've developed their code in matlab but I'm pretty sure it can be replicated using scikit learn's mixture package and CV utilities.
If I find time I can try to replicate that, I think it'd be a cool addition to msmexplorer.
Hi,
I've found this paper which use Cross Validation and Gaussian Mixture Models to estimate Free Energy landscapes (they also compare them to other ways of estimating the densities), but apparently the GMM models are best for estimating the free energy values in the regions where data is sparse.
They've developed their code in matlab but I'm pretty sure it can be replicated using scikit learn's mixture package and CV utilities.
If I find time I can try to replicate that, I think it'd be a cool addition to msmexplorer.
Let me know if you have some thoughts on this :)