XRDidentifier

Pytorch implementation of XRD spectral identification from COD database.
Details will be explained in the paper to be submitted to NeurIPS 2021 Workshop Machine Learning and the Physical Sciences (https://ml4physicalsciences.github.io/2021/).

Features

expert model

1D-CNN (1D-RegNet) + Hierarchical Deep metric learning (AdaCos + Angular Penalty Softmax Loss)

mixture of experts

73 expert models tailered to general chemical elements with sparsely-gated layer

data augmentation

Physics-informed data augmentation

Requirements

• Python 3.6
• PyTorch 1.4
• pymatgen
• scikit-learn

Dataset Construction

In the paper, I used ICSD dataset, but it is forbidden to redistribute the CIFs followed by their license. I will write the CIF dataset construction method using COD instead.

1. download cif files from COD

Go to the COD homepage, search and download the cif URL list.
http://www.crystallography.net/cod/search.html

python3 download_cif_from_cod.py --input ./COD-selection.txt --output ./cif

2. convert cif into XRD spectra

First, check the cif files. (some files are broken or physically meaningless)

python3 read_cif.py --input ./cif --output ./lithium_datasets.pkl

lithium_datasets.pkl will be created.

Second, convert the checked results into XRD spectra database.

python3 convertXRDspectra.py --input ./lithium_datasets.pkl --batch 8 --n_aug 5

XRD_epoch5.pkl will be created.

Train expert models

python3 train_expert.py --input ./XRD_epoch5.pkl --output learning_curve.csv --batch 16 --n_epoch 100

Output data

• Trained model -> regnet1d_adacos_epoch100.pt
• Learning curve -> learning_curve.csv
• Correspondence between numerical int label and crystal names -> material_labels.csv

Train Mixture-of-Experts model

You need to prepare both pre-trained expert models and pickled single XRD spectra files.
You should store the pre-trained expert models in './pretrained' folder, and the pickled single XRD spectra files in './pickles' folder.
The number of experts are automatically adjusted according to the number of the pretrained expert models.

python3 train_moe.py --data_path ./pickles --save_model moe.pt --batch 64 --epoch 100

Output data

• Trained model -> moe.pt
• Learning curve -> moe.csv

Citation

Papers

• AdaCos: https://arxiv.org/abs/1905.00292
• 1D-RegNet: https://arxiv.org/abs/2008.04063
• Physics-informed data augmentation: https://arxiv.org/abs/1811.08425v2
• Sparsely-gated layer: https://arxiv.org/abs/1701.06538

Implementation

• AdaCos: https://github.com/4uiiurz1/pytorch-adacos/blob/master/metrics.py
• 1D-RegNet: https://github.com/hsd1503/resnet1d
• Physics-informed data augmentation: https://github.com/PV-Lab/autoXRD
• Top k accuracy: https://gist.github.com/weiaicunzai/2a5ae6eac6712c70bde0630f3e76b77b
• Angular Penalty Softmax Loss: https://github.com/cvqluu/Angular-Penalty-Softmax-Losses-Pytorch
• Sparsely-gated layer: https://github.com/davidmrau/mixture-of-experts