This repository implements the multi-task graph neural networks used in the paper "Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties".
This software package requires:
Intructions for installing the prerequisites can be found in above websites.
We provide two different types of training procedules.
To perform single task training, run the following command:
python single_task_train.py --log10 0 data/logp/noise_0.64
The code will perform single task training that can reduce the random errors in the noisy training data.
To perform multi task training, run the following command:
python multi_task_train.py data/conductivity/5ns data/conductivity/50ns
The code will perform multi task training that can reduce systematic errors between 5ns and 50ns simulations.
We provide a script to quickly rerun most experiments in paper.
bash run.sh
Please consider citing the following paper if you find our code & data useful.
@article{xie2022accelerating,
title={Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties},
author={Xie, Tian and France-Lanord, Arthur and Wang, Yanming and Lopez, Jeffrey and Stolberg, Michael A and Hill, Megan and Leverick, Graham Michael and Gomez-Bombarelli, Rafael and Johnson, Jeremiah A and Shao-Horn, Yang and others},
journal={Nature communications},
volume={13},
number={1},
pages={1--10},
year={2022},
publisher={Nature Publishing Group}
}