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Remove Run on Gradient links #137

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13 changes: 2 additions & 11 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -32,20 +32,11 @@ The package is under active development, to broaden its scope and applicability.
To generate datasets based on the paper __Repurposing Density Functional Theory to Suit Deep Learning__ [Link](https://icml.cc/virtual/2023/workshop/21476#wse-detail-28485) [PDF](https://syns-ml.github.io/2023/assets/papers/17.pdf) presented at the [Syns & ML Workshop, ICML 2023](https://syns-ml.github.io/2023/), the entry point is the notebook [DFT Dataset Generation](./notebooks/DFT-dataset-generation.ipynb), and the file [density_functional_theory.py](./density_functional_theory.py).


To run the notebook on Graphcore IPU hardware on Paperspace:

[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/YX0jlK)

### For DFT teaching and learning: nanoDFT

We also provide a lightweight implementation of the SCF algorithm, optimized for readability and hackability, in the [nanoDFT demo](notebooks/nanoDFT-demo.ipynb) notebook and in [nanodft](pyscf_ipu/nanoDFT/README.md) folder.


To run the notebook on Graphcore IPU hardware on Paperspace:

[![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/ipobmC)



Additional notebooks in [notebooks](notebooks) demonstrate other aspects of the computation.

Expand All @@ -58,7 +49,7 @@ We recommend upgrading `pip` to the latest stable release to prepare your enviro
pip install -U pip
```

This project is currently under active development.
This project is currently under active development.
For CPU simulations, we recommend installing `pyscf-ipu` from latest `main` branch as:
```bash
pip install pyscf-ipu[cpu]@git+https://github.com/graphcore-research/pyscf-ipu
Expand Down Expand Up @@ -97,7 +88,7 @@ You can then start generating (locally on CPU) a dataset using the following com
python density_functional_theory.py -generate -save -fname dataset_name -level 0 -plevel 0 -gdb 9 -backend cpu -float32
```

You can speed up the generation by using IPUs. Please try the [DFT dataset generation notebook](https://ipu.dev/YX0jlK) [![Run on Gradient](https://assets.paperspace.io/img/gradient-badge.svg)](https://ipu.dev/YX0jlK)
You can speed up the generation by using IPUs. Please try the [DFT dataset generation notebook](https://ipu.dev/YX0jlK)


## Training SchNet on [QM1B](qm1b/README.md)
Expand Down
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