wehs7661/boltzmann_generators

A repository for the final project implementing/applying Boltzmann generators for Computational Statistical Physics (PHYS 7810) at CU Boulder

boltzmann_generators's Issues

The RC loss function was not implemented correctly.

In the current implementation of the RC loss function, we use scikit-learn to perform kernel density estimation (along with k-fold cross-validation for the optimization of the bandwidth), which returns a NumPy array instead of a Pytorch tensor. Since there is no information about the gradient of the RC loss function, the current Boltzmann generator was not able to backpropagate and the RC loss function did not actually influence the result of training. Since there are no built-in functions for kernel density estimation and k-fold cross-validation, the way of solving this issue might be coding up RC loss in using PyTorch instead of NumPy-based packages.

Currently, the development of the project has been paused, so this issue might not be addressed in the short term. We, therefore, file an issue here as a record/reminder of the problem.

Lennard-Jones Bath

Hi,

What does this Lennard-Jones Bath mean, you mean the energy well? And you can predict the interaction energy? Little confused there.

-Sul

plot_2D_potential missing

Hi,

I am running your tutorials, thanks for releasing your instructive code on Github. In the
Mueller-Brown-Potential notebook the plot_2D_potetial method seems to be missing.
Is there any address where it can be located?

Thanks.

missing library for build system and utils

Hi, I want to reproduce your result on jupyter.

doublewell model works well.
however, Dimer-LJ-Bath-Sim.ipynb needs build_system and utils library in in In[5] build_system.build_dimer_coords and In [30] utils.apply_hungarian_alg Lib directory don't include two modules.

could you provide the utility?