Remove Run on Gradient links (#137)

README.md

@@ -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.
 
@@ -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
@@ -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)