ALEBREW: The Atomic Learning Environment for Building REliable interatomic neural netWork potentials
Official repository for the paper "Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials". It comprises the package for building uniformly accurate machine-learned interatomic potentials using uncertainty-biased molecular dynamics and active learning. Data sets generated using this package and the configuration files necessary to reproduce the main results of the paper can be found at Zenodo. The logo for ALEBREW has been generated using DALL-E.
Please consider citing us if you find the code and paper useful:
@article{Zaverkin2024,
author={Zaverkin, Viktor and Holzm{\"u}ller, David and Christiansen, Henrik and Errica, Federico and Alesiani, Francesco and Takamoto, Makoto and Niepert, Mathias and K{\"a}stner, Johannes},
title={Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials},
journal={npj Computational Materials},
year={2024},
month={Apr},
day={29},
volume={10},
number={1},
pages={83},
issn={2057-3960},
doi={10.1038/s41524-024-01254-1},
url={https://doi.org/10.1038/s41524-024-01254-1}}
This repository implements
- Gaussian moment neural network potentials;
- Total and atom-based uncertainties for biasing and terminating atomistic simulations;
- Ensemble-free last-layer and sketched gradient-based features to compute posterior- and distance-based uncertainties;
- Ensemble-based uncertainties;
- Batch selection strategies to generate maximally diverse training data sets with active learning;
- Uncertainty calibration methods. We recommend to use inductive conformal prediction in the experiments;
- Interface with ASE to run molecular dynamics simulations (NVT, NPT, etc.) and reference calculations;
- Adversarial training to use with last-layer and sketched gradient-based uncertainties.
This source code has a non-commercial license; see LICENSE.txt for more details.
An environment with PyTorch (>=2.0.0), BMDAL-REG (version 3) and ASE (==3.22.1) installed. Also, some other dependencies may be necessary; see alebrew-{cuda,cpu,mps}.yml files.
First, clone this repository into a directory of your choice git clone https://github.com/nec-research/alebrew.git <dest_dir>. Then, move to <dest_dir> and install the required packages into a conda environment using, e.g., conda env create -f alebrew-{cuda,cpu,mps}.yml. Finally, you can set your PYTHONPATH environment variable to export PYTHONPATH=<dest_dir>:$PYTHONPATH.
Currently, we implement only Gaussian moment neural network potentials. You have to specify model parameters in the config.yaml file to train them using a data set. The default config.yaml.default file provides an example for training an interatomic potential for alanine dipeptide using the test data set from Zenodo.
To use the default configuration, first, download the ala2_test.extxyz from Zenodo and execute cp config.yaml.default config.yaml. Then, by running python scripts/run_train.py config.yaml, the training of a Gaussian moment neural network model starts, with the progress displayed in the models/ala2/train.out file; use, e.g., tail -f models/ala2/train.out. After training, potentials can be evaluated by running python scripts/run_test.py config.yaml with the test results evaluated on the data not used during training and stored in the models/ala2/test_results.json file.
For the more complicated task of generating training data sets and training machine-learned interatomic potentials from scratch using our active learning workflow, we provide a more detailed tutorial in the examples/using_alebrew.ipynb Jupyter notebook.
To reproduce the results from the paper replace the parameters in the provided Jupyter notebook, i.e., examples/using_alebrew.ipynb, by those presented in the config.yaml files from {ala2-ffs,mil53}/{task}/{learning_method} at Zenodo. See also docstrings in alebrew/task_execuption.py for more information on possible parameters.