Aviary
The aviary contains:
-
roost,wren,cgcnn.
The aim is to contain multiple models for materials discovery under a common interface
Environment Setup
To use aviary you need to create an environment with the correct dependencies. The easiest way to get up and running is to use anaconda. A cudatoolkit=11.1 environment file is provided environment-gpu-cu111.yml allowing a working environment to be created with:
conda env create -f environment-gpu-cu111.yml
If you are not using cudatoolkit=11.1 or do not have access to a GPU this setup will not work for you. If so please check the following pages PyTorch, PyTorch-Scatter for how to install the core packages and then install the remaining requirements as detailed in requirements.txt.
The code was developed and tested on Linux Mint 19.1 Tessa. The code should work with other Operating Systems but it has not been tested for such use.
Aviary Setup
Once you have set up an environment with the correct dependencies you can install aviary using the following commands from the top of the directory:
conda activate aviary
python setup.py sdist
pip install -e .
This will install the library in an editable state allowing for advanced users to make changes as desired.
Example Use
To test the input files generation and cleaning/canonicalization please run:
python examples/inputs/poscar2df.py
This script will load and parse a subset of raw POSCAR files from the TAATA dataset and produce the datasets/examples/examples.csv file used for the next example. The raw files have been selected to ensure that the subset contains all the correct endpoints for the 5 elemental species in the Hf-N-Ti-Zr-Zn chemical system. All the models used share can be run on the input file produced by this example code. To test each of the three models provided please run:
python examples/roost-example.py --train --evaluate --data-path examples/inputs/examples.csv --targets E_f --tasks regression --losses L1 --robust --epoch 10
python examples/wren-example.py --train --evaluate --data-path examples/inputs/examples.csv --targets E_f --tasks regression --losses L1 --robust --epoch 10
python examples/cgcnn-example.py --train --evaluate --data-path examples/inputs/examples.csv --targets E_f --tasks regression --losses L1 --robust --epoch 10
Please note that for speed/demonstration purposes this example runs on only ~68 materials for 10 epochs - running all these examples should take < 30s. These examples do not have sufficient data or training to make accurate predictions, however, the same scripts have been used for all experiments conducted.
Cite This Work
If you use this code please cite the relevant work:
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. [Paper] [arXiv]
@article{goodall2020predicting,
title={Predicting materials properties without crystal structure: Deep representation learning from stoichiometry},
author={Goodall, Rhys EA and Lee, Alpha A},
journal={Nature Communications},
volume={11},
number={1},
pages={1--9},
year={2020},
publisher={Nature Publishing Group}
}
Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining. [arXiv]
@article{goodall2021rapid,
title={Rapid Discovery of Novel Materials by Coordinate-free Coarse Graining},
author={Goodall, Rhys EA and Parackal, Abhijith S and Faber, Felix A and Armiento, Rickard and Lee, Alpha A},
journal={arXiv preprint arXiv:2106.11132},
year={2021}
}
Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. [Paper] [arXiv]
@article{xie2018crystal,
title={Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties},
author={Xie, Tian and Grossman, Jeffrey C},
journal={Physical review letters},
volume={120},
number={14},
pages={145301},
year={2018},
publisher={APS}
}
Disclaimer
This research code is provided as-is. We have checked for potential bugs and believe that the code is being shared in a bug-free state. As this is an archive version we will not be able to amend the code to fix bugs/edge-cases found at a later date. However, this code will likely continue to be developed at the location described in the metadata.
2 Feb 07, 2022
39 Dec 26, 2022
3 Jun 06, 2022
58 Jan 04, 2023
2 Nov 12, 2021
61 Dec 10, 2022
0 Oct 03, 2021
2 Jan 10, 2022
50 Dec 20, 2022
1 Dec 11, 2021
14 Oct 17, 2022
6 Nov 14, 2022
16 Nov 10, 2022
9 Sep 15, 2022
484 Jan 01, 2023
32 Jan 19, 2022
2 Oct 23, 2022
163 Oct 14, 2022
6 Jan 04, 2022
2 Oct 03, 2021