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Fokker-Planck Score Learning: Efficient Free-Energy Estimation Under Periodic Boundary Conditions

This package provides a proof-of-concept implementation of the Fokker-Planck score learning approach.

This package is published in:

Fokker-Planck Score Learning: Efficient Free-Energy Estimation Under Periodic Boundary Conditions,
D. Nagel, and T. Bereau,
J. Phys. Chem. B 2025, accepted doi: 10.1021/acs.jpcb.5c04579

We kindly ask you to cite this article if you use this software package in published work.

Features

  • Learning efficiently free energies from biased simulations
  • Documentation including tutorials
  • Supports Python 3.10-3.13

Getting started

Installation

The package is called fpsl and is available via PyPI. To install it, simply call: 

python3 -m pip install fpsl
Otherwise, you can install it from github. Download the repo and setup an env with fpsl installed with uv. If you do not have uv you can get it here. 
uv sync --extra cuda  # if you have an Nvidia GPU

Usage

Add here a short example.

import fpsl

ddm = fps.DrivenDDM(
    sigma_min=1e-3,
    symmetric=True,
    fourier_features=4,
    ...,
)
# load x position of MD trajectory and forces f
ddm.train(
    ...
)
...