This repository contains code, datasets and models corresponding to the following publication:
Neural functional theory for inhomogeneous fluids: Fundamentals and applications
Florian Sammüller, Sophie Hermann, Daniel de las Heras, and Matthias Schmidt, Proc. Natl. Acad. Sci. 120, e2312484120 (2023); arXiv:2307.04539.
You need Tensorflow/Keras, see the installation guide at https://www.tensorflow.org/install/pip.
Additionally, the code requires the Python modules numpy, scipy and matplotlib.
Simulation data can be found in data and trained models are located in models.
A sample script for training a model from scratch is given in learn.py.
The usage of a trained model, e.g. for the self-consistent calculation of density profiles, is illustrated in neuraldft.py.
Some useful utilities are provided in utils.py, such as tools for functional calculus as well as data generators and callbacks for training.
The reference data has been generated with grand canonical Monte Carlo simulations using MBD. The analytic DFT calculations with fundamental measure theory have been performed with the Julia library ClassicalDFT.jl.