https://github.com/atomicarchitects/symphony
[ICLR'24] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
https://github.com/atomicarchitects/symphony
machine-learning molecular-modeling molecule-generation
Last synced: 5 months ago
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[ICLR'24] Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
- Host: GitHub
- URL: https://github.com/atomicarchitects/symphony
- Owner: atomicarchitects
- License: mit
- Created: 2022-11-29T02:02:34.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2025-02-24T16:22:08.000Z (over 1 year ago)
- Last Synced: 2025-09-09T12:44:46.320Z (9 months ago)
- Topics: machine-learning, molecular-modeling, molecule-generation
- Language: Python
- Homepage:
- Size: 356 MB
- Stars: 25
- Watchers: 2
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation
This is the official code-release for the paper [Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation](https://openreview.net/forum?id=MIEnYtlGyv), published at ICLR 2024.
### Instructions
Clone the repository:
```shell
git clone git@github.com:atomicarchitects/symphony.git
cd symphony
git checkout iclr_2024_final
```
Since this repository is actively being developed, we recommend using the `iclr_2024_final` branch for the most stable version of the code.
#### Default Setup
Create and activate a virtual environment:
```shell
python -m venv .venv && source .venv/bin/activate
```
Install pip dependencies with:
```shell
pip install --upgrade pip && pip install -r requirements.txt
```
For GPU support, install JAX with CUDA support afterwards:
```shell
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
```
Some of the analysis scripts require `openbabel==3.1.1`.
This can be installed through conda.
#### Checking Installation
Check that installation suceeded by running a short test:
```shell
python -m tests.train_test
```
#### Start a Training Run
Start training with a configuration defined
under `configs/`:
```shell
python -m symphony \
--config configs/qm9/e3schnet_and_nequip.py \
--workdir ./workdirs
```
The `--workdir` flag specifies the directory where the
model checkpoints, logs, and other artifacts will be saved.
#### Changing Hyperparameters
Since the configuration is defined using
[config_flags](https://github.com/google/ml_collections/tree/master#config-flags),
you can override hyperparameters.
For example, to change the number of training
steps, and the batch size:
```shell
python -m symphony --config configs/qm9/e3schnet_and_nequip.py \
--workdir ./workdirs \
--config.num_train_steps=10 --config.max_n_graphs=16
```
For more extensive changes, directly edit the configuration files,
or add your own.
## Citation
Please cite our paper if you use this code!
```bibtex
@inproceedings{
daigavane2024symphony,
title={Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation},
author={Ameya Daigavane and Song Eun Kim and Mario Geiger and Tess Smidt},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=MIEnYtlGyv}
}
```