Ultra-Fast Force Fields (UF3)

S. R. Xie, M. Rupp, and R. G. Hennig, "Ultra-fast interpretable machine-learning potentials", preprint arXiv:2110.00624 (2021).

All-atom dynamics simulations have become an indispensable quantitative tool in physics, chemistry, and materials science, but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive accuracy. The UF3 framework is built to address this challenge by combinining effective two- and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable, sufficiently accurate for applications, and as fast as the fastest traditional empirical potentials.

Documentation: https://uf3.readthedocs.io/

This repository is still under construction. Please feel free to open new issues for feature requests and bug reports.

Setup

UF3 can be installed in two different ways:

1. Download and install automatically from PyPI (recommended):

pip install uf3

2. Download and install manually from GitHub:

git clone https://github.com/uf3/uf3.git
cd uf3
pip install .

Getting Started

Please see the examples in uf3/examples/tungsten_extxyz for basic usage.

Overviews for individual modules can be found in uf3/examples/modules (WIP).

Standalone scripts and configuration generators/parsers are in development.

Optional Dependencies

Elastic constants:

pip install setuptools_scm
pip install uf3[elastic_constants]

Phonon spectra:

pip install uf3[phonon_spectra]

LAMMPS interface:

conda install numpy==1.20.3 --force-reinstall
conda install -c conda-forge lammps --no-update-deps

The environment variable $ASE_LAMMPSRUN_COMMAND must also be set to use the LAMMPS interface within python. See the ASE documentation for details.

Dependencies

• We rely on ase to handle parsing outputs from atomistic codes like LAMMPS, VASP, and CP2K.
• We use Pandas to keep track of atomic configurations and their energies/forces as well as organizing data for featurization and training.
• B-spline evaluations use scipy, numba, and ndsplines.
• PyTables is used for reading/writing HDF5 files.
• Matplotlib is used for plotting.
• We use sklearn for regression.
• We use tqdm for progress bars.
• We use plotly for interactive plots.
• We use PyYaml for configuration files.
• We use numpy for array operations.