:red_circle: :warning: Experimental and non-official Graphcore product :warning: :red_circle:

Installation guide | Example DFT Computations | Generating data | Training SchNet | QM1B dataset

PySCF on IPU

PySCF-IPU is built on top of the PySCF package, porting some of the PySCF algorithms to the Graphcore IPU.

Only a small portion of PySCF is currently ported, specifically Restricted Kohn Sham DFT (based on RKS, KohnShamDFT and hf.RHF).

The package is under active development, to broaden its scope and applicability. Current limitations are:

• Number of atomic orbitals less than 70 mol.nao_nr() <= 70.
• Larger numerical errors due to np.float32 instead of np.float64.
• Limited support for jax.grad(.)

QuickStart

For ML dataset generation (SynS & ML Workshop 2023)

To generate datasets based on the paper Repurposing Density Functional Theory to Suit Deep Learning Link PDF presented at the Syns & ML Workshop, ICML 2023, the entry point is the notebook DFT Dataset Generation, and the file density_functional_theory.py.

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 notebook and in nanodft folder.

Additional notebooks in notebooks demonstrate other aspects of the computation.

Installation

PySCF on IPU requires Python 3.8, JAX IPU experimental, TessellateIPU library and Graphcore Poplar SDK 3.2.

We recommend upgrading pip to the latest stable release to prepare your environment.

pip install -U pip

This project is currently under active development. For CPU simulations, we recommend installing pyscf-ipu from latest main branch as:

pip install pyscf-ipu[cpu]@git+https://github.com/graphcore-research/pyscf-ipu

and on IPU equipped machines:

pip install pyscf-ipu[ipu]@git+https://github.com/graphcore-research/pyscf-ipu

Example DFT Computations

The following commands may be useful to check the installation. Each command runs a test-case which compares PySCF against our DFT computation using different options.

python density_functional_theory.py -methane -backend cpu # defaults to float64 as used in PySCF
python density_functional_theory.py -methane -backend cpu -float32
python density_functional_theory.py -methane -backend ipu -float32

This will automatically compare our DFT against PySCF for methane CH4 and report numerical errors.

Generating New Datasets

This section contains an example on how to generate a DFT dataset based on GDB. This is not needed if you just want to train on the QM1B dataset (to be released soon).

Download the gdb11.tgz file from https://zenodo.org/record/5172018 and extract its content in gdb/ directory:

wget -p -O ./gdb/gdb11.tgz https://zenodo.org/record/5172018/files/gdb11.tgz\?download\=1
tar -xvf ./gdb/gdb11.tgz --directory ./gdb/

To utilize caching you need to sort the SMILES strings by the number of hydrogens RDKit adds to them. This means molecule i and i+1 in most cases have the same number of hydrogens which allows our code to reuse/cache the computational graph for DFT. This can be done by running the following Python script:

python ./gdb/sortgdb.py ./gdb/gdb11_size09.smi

You can then start generating (locally on CPU) a dataset using the following command:

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

Training SchNet on QM1B

We used PySCF on IPU to generate the QM1B dataset with one billion training examples (to be released soon). See Training SchNet on QM1B for an example implementation of a neural network trained on this dataset.

License

Copyright (c) 2023 Graphcore Ltd. The project is licensed under the Apache License 2.0, with the exception of the folders electron_repulsion/ and exchange_correlation/.

The library is built on top of the following main dependencies:

Cite

Please use the following citation for the pyscf-ipu project:

@inproceedings{mathiasen2023qm1b,
  title={Generating QM1B with PySCF $ \_ $\{$$\backslash$text $\{$IPU$\}$$\}$ $},
  author={Mathiasen, Alexander and Helal, Hatem and Klaeser, Kerstin and Balanca, Paul and Dean, Josef and Luschi, Carlo and Beaini, Dominique and Fitzgibbon, Andrew William and Masters, Dominic},
  booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
  year={2023}
}