Code Repository for "Direct prediction of phonon density of states with Euclidean neural network"

This is the repository to accompany the paper: https://onlinelibrary.wiley.com/doi/10.1002/advs.202004214 (or on arXiv https://arxiv.org/pdf/2009.05163.pdf). Please direct any questions to Zhantao ([email protected]).

We later rewrote the code with latest e3nn in https://github.com/ninarina12/phononDoS_tutorial, which was prepared for the E3NN tutorial in MRS 2021 Fall Meeting.

Dependency installation instructions

These are very explicit installation instructions that do NOT assume you have experience installing specific python packages or using virtual environments. It's important that you follow these detailed instructions because one of our important dependencies torch-geometric uses custom CUDA kernels (for Nvidia GPUs) that are compiled for specific combinations of torch and CUDA (e.g. version 9.2, 10.1, or 10.2). These instructions assume you have python 3.6+ installed.

We require the following specific package verisons:

• torch==1.5.1
• torch-geometric==1.5.0
• e3nn at commit 4653db57d0af05e1834e65b6da73fa26927824bc

Please see below for how to install these packages.

Clone this repository

To make a copy of this repository on your computer run the following at the command line.
git clone https://github.com/zhantaochen/phonondos_e3nn.git

The change into the directory.
cd phonodos_e3nn

Setting up a virtual environment

Because we use specific versions of these packages, you may want to make and activate a new virtual environment. In the command line run,

virtualenv --python=`which python3` venv
source venv/bin/activate

Whenver you want to exit this environment (not right now because we want to install packages), enter deactivate in the command line. You can reactivate the environment using source venv/bin/activate like above.

Installing dependencies

First, let's install torch and torchvision.
pip install torch==1.5.1 torchvision==0.6.1

Next we will install torch-geometric which is a versatile library for doing deep learning on graphs. Please first check the following before installing torch-geometric.

In a python, check the CUDA version torch is using.

import torch
print(torch.version.cuda)

At the command line, check your NVIDIA compiler version.
nvcc --version

If you do not have the nvcc command, this likely means you do not have CUDA installed and / or do not have a GPU. If this is the case, you will set CUDA=cpu in the be

These versions must match before proceeding. If they match, at the command line set the variable CUDA to be cu92, cu101, or cu102 to match your CUDA version or cpu to not install GPU dependences. For example, CUDA=cu102 will set the CUDA variable to indicate you want to install a version for CUDA 10.2. CUDA=cpu will install torch-geometric without GPU dependencies. You can check that the variable CUDA is set by running echo CUDA.

Then run the following.

pip install --no-index torch-scatter -f https://pytorch-geometric.com/whl/torch-1.5.0+${CUDA}.html
pip install --no-index torch-sparse -f https://pytorch-geometric.com/whl/torch-1.5.0+${CUDA}.html
pip install --no-index torch-cluster -f https://pytorch-geometric.com/whl/torch-1.5.0+${CUDA}.html
pip install --no-index torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.5.0+${CUDA}.html
pip install torch-geometric==1.5.0

Then install e3nn at commit 4653db57d0af05e1834e65b6da73fa26927824bc.
pip install git+git://github.com/e3nn/e3nn.git@4653db57d0af05e1834e65b6da73fa26927824bc

and finally pymatgen which we use to interact with the Materials Project API and manipulate crystal structure data. Installing e3nn will likely have already installed this package, but you can also install it with.
pip install pymatgen==2020.6.8

Using a virtual environment with Jupyter

In order to use this virtual environment in Jupyter you will need to add it to with ipykernel.

pip install ipykernel
python -m ipykernel install --user --name=phonondos_e3nn

Now, if you start a jupyter notebook outside this virtual enviroment (like in a new command line window, but still in the phonondos_e3nn folder) and navigate to wherever these notebook are hosted, for example localhost:8888, you will be able to select the phonondos_e3nn Kernel from the Kernel drop down menu in Jupyter when you view any of the notebooks in this repository.

Download data from the Materials Project

This repository uses data from the Materials Project. To access the Materials Project dataset via pymatgen, you will need an API key which can obtain via https://www.materialsproject.org/dashboard. See https://pymatgen.org/pymatgen.ext.matproj.html for more details.

One you have your Materials Project API key, you can add it to your pymatgen config file (~/.pmgrc.yaml) with the following command.
pmg config --add PMG_MAPI_KEY YOUR_API_KEY

To download the data needed, run the following command
python download_mpcifs.py
or to set the API key manually.
python download_mpcifs.py YOUR_API_KEY

Other details

These dependencies (also called requirements) can be found in requirements.txt but installing with pip install -r requirements.txt will NOT work so please follow the instructions above. If you run into problems installing pytorch or torch-geometric, please consult the official pytorch and torch-geometric installation instructions for relevant information.

This repository uses a specific commit of e3nn between 0.0.0 and 0.0.1 releases (commit 4653db57d0af05e1834e65b6da73fa26927824bc); you must use this version to ensure that you can import saved model parameters. If your torch-geometric version is equal or higher than 1.6.0, then additional fixes from the next commit are needed, please check commits history here.

We are planning to prepare another notebook for the latest release of e3nn and dependencies in near future.

Notebooks and python files

download_mpcifs.py and data/mp_data.csv

• Download *.cif files from the Materials Project, with material IDs listed in data/mp_data.csv.
• Usage: python download_mpcifs.py YOUR_MAPI_KEY

phdos_train.ipynb

• Trains a E(3)NN model that predicts phonon density of states (DoS) based on crystal structures.

phdos_eval_MPdata.ipynb and class_evaluate_MPdata.py

• Evaluates phonon DoS and specific heat capacities for around 4,400 crystals on Materials Project.

models/200803-1018_len51max1000_fwin101ord3_trial_run_full_data.torch

• The trained model presented in the paper.

models/cif_unique_files.pkl

• Contains Materials Project IDs that used for searching high heat capacity materials, the materials presented in traning/validation/testing datasets are excluded.

models/phdos_e3nn_len51max1000_fwin101ord3.zip

• Zipped file that contains interpolated phonon DoS and other necessary information for training (cif files, etc.). The included information is curated from the dataset presented in the work: Petretto, Guido, et al. "High-throughput density-functional perturbation theory phonons for inorganic materials." Scientific data 5 (2018): 180065.

Citing

@article{chen2020direct,
  title={Direct prediction of phonon density of states with Euclidean neural network},
  author={Chen, Zhantao and Andrejevic, Nina and Smidt, Tess and Ding, Zhiwei and Chi, Yen-Ting and Nguyen, Quynh T and Alatas, Ahmet and Kong, Jing and Li, Mingda},
  journal={arXiv preprint arXiv:2009.05163},
  year={2020}
}