MoreRed: Molecular Relaxation by Reverse Diffusion with Time Step Prediction

MoreRed (Molecular Relaxation by Reverse Diffusion) is a generative diffusion model that can generate new structures or denoise arbitrarily noisy ones. Unlike previous geometry relaxation methods, which require labeled equilibrium and non-equilibrium structures, MoreRed is trained using exclusively unlabeled equilibrium structures. Despite this, it effectively relaxes non-equilibrium structures, achieving competitive results with much less data, exhibiting better robustness to the noise level in the input and reducing computation time during relaxation.

MoreRed is built on top of SchNetPack 2.0, an easily configurable and extendible library for constructing and training neural network models for atomistic systems like molecules. SchNetPack utilizes PyTorch Lightning for model building and Hydra for straightforward management of experimental configurations. While high level usage of the morered package to train and use the models described in the original paper does not require knowledge of its underlying dependencies, we recommend users familiarize themselves with Hydra to be able to customize their experimental configurations. Additionally, the tutorials and the documentation provided in SchNetPack 2.0 can be helpful. Below, we explain how to use the morered package.

NOTE: while the current documentation in the README file and the source code should be sufficient to use the package easily, we will continually enhance it.

Content

• News
• Installation
• Using pre-trained models
• Molecular relaxation and structure generation
• Tutorials
• Training your own models
• How to cite

News

• Under the folder morered, we uploaded the final models trained on QM9 and QM7-X datasets
• We updated the notebook denoising_tutorial.ipynb with more details on using the trained models.

Installation

Requirements:

• python >= 3.8
• SchNetPack 2.0

You can install morered from the source code using pip, which will also install all its required dependencies including SchNetPack:

Download this repository. e.g. by cloning it using:

git clone git@github.com:khaledkah/MoreRed.git
cd MoreRed

We recommend creating a new Python environment or using conda to avoid incompatibilities with previously installed packages. E.g. if using conda:

conda create -n morered python=3.12
conda activate morered

Now to install the package, inside the folder MoreRed run:

pip install .

Using pre-trained models

Under the folder models, you can find the final models trained on QM9 and Qm7-X datasets until complete convergence. You can load the models using torch.load() command. Besides, the tutorial notebooks provide details on how to use the models.

Molecular relaxation and structure generation

The notebook notebooks/denoising_tutorial.ipynb explains how the trained models can be used for denoising and generation from scratch using different samplers. Under src/morered/sampling, you can find ready-to-use Python classes implementing the different samplers: MoreRed-ITP, MoreRed-JT, MoreRed-AS, DDPM. The same classes can be used for denoising/relaxation of noisy structures as well as for new structure generation.

Tutorials

Under notebooks, we provide different tutorials in the form of Jupyter notebooks, that will be continually updated:

• diffusion_tutorial.ipynb: explains how to use the diffusion processes implemented in morered.
• denoising_tutorial.ipynb: explains how to use the trained models with the different samplers implemented in morered for noisy structure relaxation or generation from scratch. Under the folder models, we provide final models trained on QM9 and QM7-X datasets until complete convergence.

Training your own models

The human-readable and customizable YAML configuration files under src/morered/configs are all you need to train and run customizable experiments with morered. They follow the configuration structure used in SchNetPack 2.0. Here, we explain how to train and use the different models.

Installing morered using pip adds the new CLI command mrdtrain, which can be used to train the different models by running the command:

mrdtrain experiment=<my-experiemnt>

where <my-experiment> specifies the experimental configurations to be used. It can either be one of the pre-installed experiments within the package, under src/morered/configs/experiments, or a path to a new YAML file created by the user. Detailed instructions on creating custom configurations can be found in the documentation of SchNetPack 2.0.

In the original paper, three variants of MoreRed were introduced:

MoreRed-JT:

You can train the MoreRed-JT variant on QM7-X with the default configuration by simply running:

mrdtrain experiment=vp_gauss_morered_jt

MoreRed-AS/ITP:

Both variants, MoreRed-AS and MoreRed-ITP, require separately trained time and noise predictors. The noise predictor here is also the usual DDPM model and can be trained using:

mrdtrain experiment=vp_gauss_ddpm

The time predictor can be trained by running:

mrdtrain experiment=vp_gauss_time_predictor

Train on QM9

To train the models on QM9 instead of QM7-X you can append the suffix _qm9 to the experiment name, for instance by running:

mrdtrain experiment=vp_gauss_morered_jt_qm9

Otherwise, you can use the CLI to overwrite the Hydra configurations of the data set by running:

mrdtrain experiment=vp_gauss_morered_jt data=qm9_filtered

Use your config files

To use your config files, you can define the configurations in YAML files and refer to the directory containing these files:

mrdtrain --config-dir=<path/to/my_configs> experiment=<my_experiment>

More about overwriting configurations in the CLI can be found in the SchNetPack 2.0 documentation.

How to cite

if you use MoreRed in your research, please cite the corresponding publication:

Kahouli, K., Hessmann, S. S. P., Müller, K.-R., Nakajima, S., Gugler, S., & Gebauer, N. W. A. (2024). Molecular relaxation by reverse diffusion with time step prediction. Machine Learning: Science and Technology, 5(3), 035038. doi:10.1088/2632-2153/ad652c

@article{kahouli2024morered,
  doi = {10.1088/2632-2153/ad652c},
  url = {https://dx.doi.org/10.1088/2632-2153/ad652c},
  year = {2024},
  month = {aug},
  publisher = {IOP Publishing},
  volume = {5},
  number = {3},
  pages = {035038},
  author = {Khaled Kahouli and Stefaan Simon Pierre Hessmann and Klaus-Robert Müller and Shinichi Nakajima and Stefan Gugler and Niklas Wolf Andreas Gebauer},
  title = {Molecular relaxation by reverse diffusion with time step prediction},
  journal = {Machine Learning: Science and Technology},
}