https://github.com/balaranjan/mat2vec

Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

https://github.com/balaranjan/mat2vec

Repository

Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).

https://github.com/balaranjan/mat2vec/blob/master/

### Supplementary Materials for "Unsupervised word embeddings capture latent knowledge from materials science literature", *Nature*  571, 9598 (2019).
#### Tshitoyan, V., Dagdelen, J., Weston, L., Dunn, A., Rong, Z., Kononova, O., Persson, K. A., Ceder, G. and Jain, A. 
doi: [10.1038/s41586-019-1335-8](https://www.nature.com/articles/s41586-019-1335-8)

A view-only (no download) link to the paper: https://rdcu.be/bItqk

### Set up

1. Make sure you have `python3.6` and the `pip` module installed. 
We recommend using [conda environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html).
1. Navigate to the root folder of this repository (the same folder that contains this README file)
and run `pip install --ignore-installed -r requirements.txt`. Note: If you are using a conda env and any packages fail to compile during this step, you may need to first install those packages separately with `conda install package_name`. 
1. Wait for all the requirements to be downloaded and installed.
1. Run `python setup.py install` to install this module. This will also download the Word2vec model files.
If the download fails, manually download the [model](https://storage.googleapis.com/mat2vec/pretrained_embeddings), 
[word embeddings](https://storage.googleapis.com/mat2vec/pretrained_embeddings.wv.vectors.npy) and 
[output embeddings](https://storage.googleapis.com/mat2vec/pretrained_embeddings.trainables.syn1neg.npy) and put them in mat2vec/training/models.
1. Finalize your chemdataextractor installation by executing ``cde data download`` (You may need to restart your virtual environment for the cde command line interface to be found).
1. You are ready to go!

#### Processing

Example python usage:

```python
from mat2vec.processing import MaterialsTextProcessor
text_processor = MaterialsTextProcessor()
text_processor.process("LiCoO2 is a battery cathode material.")
```
> (['CoLiO2', 'is', 'a', 'battery', 'cathode', 'material', '.'], [('LiCoO2', 'CoLiO2')])

For the various methods and options see the docstrings in the code.

#### Pretrained Embeddings

Load and query for similar words and phrases:
```python
from gensim.models import Word2Vec
w2v_model = Word2Vec.load("mat2vec/training/models/pretrained_embeddings")
w2v_model.wv.most_similar("thermoelectric")
```
> [('thermoelectrics', 0.8435688018798828), ('thermoelectric_properties', 0.8339033126831055), ('thermoelectric_power_generation', 0.7931368350982666), ('thermoelectric_figure_of_merit', 0.7916493415832
52), ('seebeck_coefficient', 0.7753845453262329), ('thermoelectric_generators', 0.7641351819038391), ('figure_of_merit_ZT', 0.7587921023368835), ('thermoelectricity', 0.7515754699707031), ('Bi2Te3', 0
.7480161190032959), ('thermoelectric_modules', 0.7434879541397095)]

Phrases can be queried with underscores:
```python
w2v_model.wv.most_similar("band_gap", topn=5)
```
> [('bandgap', 0.934801459312439), ('band_-_gap', 0.933477520942688), ('band_gaps', 0.8606899380683899), ('direct_band_gap', 0.8511275053024292), ('bandgaps', 0.818678617477417)]

Analogies:
```python
# helium is to He as ___ is to Fe? 
w2v_model.wv.most_similar(
    positive=["helium", "Fe"], 
    negative=["He"], topn=1)
```
> [('iron', 0.7700884938240051)]

Material formulae need to be normalized before analogies:
```python
# "GaAs" is not normalized
w2v_model.wv.most_similar(
    positive=["cubic", "CdSe"], 
    negative=["GaAs"], topn=1)
```
> KeyError: "word 'GaAs' not in vocabulary"
```python
from mat2vec.processing import MaterialsTextProcessor
text_processor = MaterialsTextProcessor()
w2v_model.wv.most_similar(
    positive=["cubic", text_processor.normalized_formula("CdSe")], 
    negative=[text_processor.normalized_formula("GaAs")], topn=1)
```
> [('hexagonal', 0.6162797212600708)]

Keep in mind that words should also be processed before queries.
Most of the time this is as simple as lowercasing, however, it is the safest
to use the `process()` method of `mat2vec.processing.MaterialsTextProcessor`.

#### Training

To run an example training, navigate to *mat2vec/training/* and run

```shell
python phrase2vec.py --corpus=data/corpus_example --model_name=model_example
```

from the terminal. It should run an example training and save the files in *models*
and *tmp* folders. It should take a few seconds since the example corpus has only 5 abstracts.

For more options, run

```shell
python phrase2vec.py --help
```

### Related Work

- Weston, L., Tshitoyan, V., Dagdelen, J., Kononova, O., Persson, K. A., Ceder, G. and Jain, A. Named Entity Recognition and Normalization Applied to Large-Scale Information Extraction from the Materials Science Literature, [ChemRxiv. Preprint.](https://chemrxiv.org/articles/Named_Entity_Recognition_and_Normalization_Applied_to_Large-Scale_Information_Extraction_from_the_Materials_Science_Literature/8226068) (2019).

### Issues?

You can either report an issue on github or contact one of us directly. 
Try [vahe.tshitoyan@gmail.com](mailto:vahe.tshitoyan@gmail.com), 
[jdagdelen@berkeley.edu](mailto:jdagdelen@berkeley.edu), 
[lweston@lbl.gov](mailto:lweston@lbl.gov) or 
[ajain@lbl.gov](mailto:ajain@lbl.gov).

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