bgflow | Boltzmann Generators and Normalizing Flows in PyTorch

bgflow is a Jupyter Notebook library. bgflow has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Bgflow is a pytorch framework for Boltzmann Generators (BG) and other sampling methods. Boltzmann Generators use neural networks to learn a coordinate transformation of the complex configurational equilibrium distribution to a distribution that can be easily sampled. Accurate computation of free-energy differences and discovery of new configurations are demonstrated, providing a statistical mechanics tool that can avoid rare events during sampling without prior knowledge of reaction coordinates. -- Noe et al. (2019). This library is alpha software under active development. Certain elements of its API are going to change in the future and some current implementations may not be tested. If you are interested in contributing to the library, please feel free to open a pull request or report an issue. When using bgflow in your research, please cite our preprint (coming soon). Implementation of a BG with a single Real NVP coupling block as the invertible transformation. The two-dimensional target potential is given by a double well potential in one dimension and a harmonic potential in the other. The prior distribution is a two-dimensional standard normal distribution. Note that the training procedure is not included in this example.