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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
SchNetPack - Deep Neural Networks for Atomistic Systems
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| 2026-07-31 | 932 |
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# SchNetPack - Deep Neural Networks for Atomistic Systems [](https://github.com/python/black) [](https://github.com/hobogalaxy/lightning-hydra-template) SchNetPack is a toolbox for the development and application of deep neural networks to the prediction of potential energy surfaces and other quantum-chemical properties of molecules and materials. It contains basic building blocks of atomistic neural networks, manages their training and provides simple access to common benchmark datasets. This allows for an easy implementation and evaluation of new models. The documentation can be found [here](https://schnetpack.readthedocs.io). ##### Features - SchNet - an end-to-end continuous-filter CNN for molecules and materials [1-3] - PaiNN - equivariant message-passing for molecules and materials [4] - Output modules for dipole moments, polarizability, stress, and general response properties - Modules for electrostatics, Ewald summation, ZBL repulsion - GPU-accelerated molecular dynamics code incl. path-integral MD, thermostats, barostats ## Installation ### Install with pip The simplest way to install SchNetPack is through pip which will automatically get the source code from PyPI: ``` pip install schnetpack ``` ### Install from source You can also install the most recent code from our repository: ``` git clone https://github.com/atomistic-machine-learning/schnetpack.git cd schnetpack pip install . ``` ### Visualization with Tensorboard SchNetPack supports multiple logging backends via PyTorch Lightning. The default logger is Tensorboard. SchNetPack also supports TensorboardX. ## Getting started The best place to get started is training a SchNetPack model on a common benchmark dataset via the command line interface (CLI). When installing SchNetPack, the training script `spktrain` is added to your PATH. The CLI uses [Hydra](https://hydra.cc/) and is based on the PyTorch Lightning/Hydra template that can be found [here](https://github.com/ashleve/lightning-hydra-template). This enables a flexible configuration of the model, data and training process. To fully take advantage of these features, it might be helpful to have a look at the Hydra and PyTorch Lightning docs. ### Example 1: QM9 In the following, we focus on using the CLI to train on the QM9 dataset, but the same procedure applies for the other benchmark datasets as well. First, create a working directory, where all data and runs will be stored: ``` mkdir spk_workdir cd spk_workdir ``` Then, the training of a SchNet model with default settings for QM9 can be started by: ``` spktrain experiment=qm9_atomwise ``` The script prints the defaults for the experiment config `qm9_atomwise`. The dataset will be downloaded automatically to `spk_workdir/data`, if it does not exist yet. Then, the training will be started. All values of the config can be changed from the command line, including the directories for run and data. By default, the model is stored in a directory with a unique run id hash as a subdirectory of `spk_workdir/runs`. This can be changed as follows: ``` spktrain experiment=qm9_atomwise run.data_dir=/my/data/dir run.path=~/all_my_runs run.id=this_run ``` If you call `spktrain experiment=qm9_atomwise --help`, you can see the full config with all the parameters that can be changed. Nested parameters can be changed as follows: ``` spktrain experiment=qm9_atomwise run.data_dir=<path> data.batch_size=64 ``` Hydra organizes parameters in config groups which allows hierarchical configurations consisting of multiple yaml files. This allows to easily change the whole dataset, model or representation. For instance, changing from the default SchNet representation to PaiNN, use: ``` spktrain experiment=qm9_atomwise run.data_dir=<path> model/representation=painn ``` It is a b
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8b2efc02e9bcf175, topic:neural-network