TorchMD-NET

TorchMD-NET provides state-of-the-art neural networks potentials (NNPs) and a mechanism to train them. It offers efficient and fast implementations if several NNPs and it is integrated in GPU-accelerated molecular dynamics code like ACEMD, OpenMM and TorchMD. TorchMD-NET exposes its NNPs as PyTorch modules.

Documentation

Documentation is available at https://torchmd-net.readthedocs.io

Available architectures

• Equivariant Transformer (ET)
• Transformer (T)
• Graph Neural Network (GN)
• TensorNet

Installation

TorchMD-Net is available as a pip installable wheel as well as in conda-forge

TorchMD-Net provides builds for CPU-only, CUDA 11.8 and CUDA 12.4. CPU versions are only provided as reference, as the performance will be extremely limited. Depending on which variant you wish to install, you can install it with one of the following commands:

```sh

The following will install the CUDA 12.4 version by default

pip install torchmd-net

The following will install the CUDA 11.8 version

pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu118 --extra-index-url https://us-central1-python.pkg.dev/pypi-packages-455608/cu118/simple

The following will install the CUDA 12.4 version

pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cu124 --extra-index-url https://us-central1-python.pkg.dev/pypi-packages-455608/cu124/simple

The following will install the CPU only version (not recommended)

pip install torchmd-net --extra-index-url https://download.pytorch.org/whl/cpu --extra-index-url https://us-central1-python.pkg.dev/pypi-packages-455608/cpu/simple
```

Alternatively it can be installed with conda or mamba with one of the following commands. We recommend using Miniforge instead of anaconda.

shell mamba install torchmd-net cuda-version=11.8 mamba install torchmd-net cuda-version=12.4

Install from source

TorchMD-Net is installed using pip, but you will need to install some dependencies before. Check this documentation page.

Usage

Specifying training arguments can either be done via a configuration yaml file or through command line arguments directly. Several examples of architectural and training specifications for some models and datasets can be found in examples/. Note that if a parameter is present both in the yaml file and the command line, the command line version takes precedence. GPUs can be selected by setting the CUDA_VISIBLE_DEVICES environment variable. Otherwise, the argument --ngpus can be used to select the number of GPUs to train on (-1, the default, uses all available GPUs or the ones specified in CUDA_VISIBLE_DEVICES). Keep in mind that the GPU ID reported by nvidia-smi might not be the same as the one CUDA_VISIBLE_DEVICES uses.
For example, to train the Equivariant Transformer on the QM9 dataset with the architectural and training hyperparameters described in the paper, one can run: shell mkdir output CUDA_VISIBLE_DEVICES=0 torchmd-train --conf torchmd-net/examples/ET-QM9.yaml --log-dir output/ Run torchmd-train --help to see all available options and their descriptions.

Pretrained models

See here for instructions on how to load pretrained models.

Creating a new dataset

If you want to train on custom data, first have a look at torchmdnet.datasets.Custom, which provides functionalities for loading a NumPy dataset consisting of atom types and coordinates, as well as energies, forces or both as the labels. Alternatively, you can implement a custom class according to the torch-geometric way of implementing a dataset. That is, derive the Dataset or InMemoryDataset class and implement the necessary functions (more info here). The dataset must return torch-geometric Data objects, containing at least the keys z (atom types) and pos (atomic coordinates), as well as y (label), neg_dy (negative derivative of the label w.r.t atom coordinates) or both.

Custom prior models

In addition to implementing a custom dataset class, it is also possible to add a custom prior model to the model. This can be done by implementing a new prior model class in torchmdnet.priors and adding the argument --prior-model <PriorModelName>. As an example, have a look at torchmdnet.priors.Atomref.

Multi-Node Training

In order to train models on multiple nodes some environment variables have to be set, which provide all necessary information to PyTorch Lightning. In the following we provide an example bash script to start training on two machines with two GPUs each. The script has to be started once on each node. Once torchmd-train is started on all nodes, a network connection between the nodes will be established using NCCL.

In addition to the environment variables the argument --num-nodes has to be specified with the number of nodes involved during training.

```shell export NODERANK=0 export MASTERADDR=hostname1 export MASTER_PORT=12910

mkdir -p output CUDAVISIBLEDEVICES=0,1 torchmd-train --conf torchmd-net/examples/ET-QM9.yaml.yaml --num-nodes 2 --log-dir output/ ```

• NODE_RANK : Integer indicating the node index. Must be 0 for the main node and incremented by one for each additional node.
• MASTER_ADDR : Hostname or IP address of the main node. The same for all involved nodes.
• MASTER_PORT : A free network port for communication between nodes. PyTorch Lightning suggests port 12910 as a default.

Known Limitations

• Due to the way PyTorch Lightning calculates the number of required DDP processes, all nodes must use the same number of GPUs. Otherwise training will not start or crash.
• We observe a 50x decrease in performance when mixing nodes with different GPU architectures (tested with RTX 2080 Ti and RTX 3090).
• Some CUDA systems might hang during a multi-GPU parallel training. Try export NCCL_P2P_DISABLE=1, which disables direct peer to peer GPU communication.

Cite

If you use TorchMD-NET in your research, please cite the following papers:

Main reference

@misc{pelaez2024torchmdnet, title={TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations}, author={Raul P. Pelaez and Guillem Simeon and Raimondas Galvelis and Antonio Mirarchi and Peter Eastman and Stefan Doerr and Philipp Thölke and Thomas E. Markland and Gianni De Fabritiis}, year={2024}, eprint={2402.17660}, archivePrefix={arXiv}, primaryClass={cs.LG} }

TensorNet

@inproceedings{simeon2023tensornet, title={TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials}, author={Guillem Simeon and Gianni De Fabritiis}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, year={2023}, url={https://openreview.net/forum?id=BEHlPdBZ2e} }

Equivariant Transformer

@inproceedings{ tholke2021equivariant, title={Equivariant Transformers for Neural Network based Molecular Potentials}, author={Philipp Th{\"o}lke and Gianni De Fabritiis}, booktitle={International Conference on Learning Representations}, year={2022}, url={https://openreview.net/forum?id=zNHzqZ9wrRB} }

Graph Network

@article{Majewski2023, title = {Machine learning coarse-grained potentials of protein thermodynamics}, volume = {14}, ISSN = {2041-1723}, url = {http://dx.doi.org/10.1038/s41467-023-41343-1}, DOI = {10.1038/s41467-023-41343-1}, number = {1}, journal = {Nature Communications}, publisher = {Springer Science and Business Media LLC}, author = {Majewski, Maciej and Pérez, Adrià and Th\"{o}lke, Philipp and Doerr, Stefan and Charron, Nicholas E. and Giorgino, Toni and Husic, Brooke E. and Clementi, Cecilia and Noé, Frank and De Fabritiis, Gianni}, year = {2023}, month = sep }

Developer guide

Implementing a new architecture

To implement a new architecture, you need to follow these steps:
1. Create a new class in torchmdnet.models that inherits from torch.nn.Model. Follow TorchMDET as a template. This is a minimum implementation of a model:
```python class MyModule(nn.Module): def _init(self, parameter1, parameter2): super(MyModule, self).init_() # Define your model here self.layer1 = nn.Linear(10, 10) ... # Initialize your model parameters here self.resetparameters()

def reset_parameters(self):
  # Initialize your model parameters here
  nn.init.xavier_uniform_(self.layer1.weight)
...

def forward(self, z: Tensor, # Atomic numbers, shape (natoms, 1) pos: Tensor, # Atomic positions, shape (natoms, 3) batch: Tensor, # Batch vector, shape (natoms, 1). All atoms in the same molecule have the same value and are contiguous. q: Optional[Tensor] = None, # Atomic charges, shape (natoms, 1) s: Optional[Tensor] = None, # Atomic spins, shape (natoms, 1) ) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]: # Define your forward pass here scalarfeatures = ... vectorfeatures = ... # Return the scalar and vector features, as well as the atomic numbers, positions and batch vector return scalarfeatures, vectorfeatures, z, pos, batch `` **2.** Add the model to thealllist intorchmdnet.models.init.py`. This will make the tests pick your model up.
3. Tell models.model.createmodel how to initialize your module by adding a new entry, for instance:
python elif args["model"] == "mymodule": from torchmdnet.models.torchmd_mymodule import MyModule is_equivariant = False # Set to True if your model is equivariant representation_model = MyModule( parameter1=args["parameter1"], parameter2=args["parameter2"], **shared_args, # Arguments typically shared by all models )

4. Add any new parameters required to initialize your module to scripts.train.getargs. For instance:
```python parser.addargument('--parameter1', type=int, default=32, help='Parameter1 required by MyModule') ... **5.** Add an example configuration file to `torchmd-net/examples` that uses your model. **6.** Make tests use your configuration file by adding a case to tests.utils.load_example_args. For instance: python if modelname == "mymodule": configfile = join(dirname(dirname(file)), "examples", "MyModule-QM9.yaml") ```

At this point, if your module is missing some feature the tests will let you know, and you can add it. If you add a new feature to the package, please add a test for it.

Code style

We use black. Please run black on your modified files before committing.

Testing

To run the tests, install the package and run pytest in the root directory of the repository. Tests are a good source of knowledge on how to use the different components of the package.