Package: matgl
Materials Graph LibraryOfficial Documentationhttps://matgl.aiIntroductionMatGL (Materials Graph Library) is a graph deep learning library for materials science. Mathematical graphs are a
natural representation for a collection of atoms. Graph deep learning models have been shown to consistently deliver
exceptional performance as surrogate models for the prediction of materials properties.MatGL is built on theDeep Graph Library (DGL)and PyTorch, with suitable adaptations for materials-specific
applications. The goal is for MatGL to serve as an extensible platform to develop and share materials graph deep
learning models. For the initial release, we have re-implemented the [MatErials 3-body Graph Network (M3GNet)]m3gnetand its predecessor,MEGNet, which were originally implemented in Tensorflow, to improve the
usability, extensibility and scalability of these models. Here are some key improvements over the TF implementations:A more intuitive API and class structure based on DGL.Multi-GPU support via PyTorch Lightning.This effort is a collaboration between theMaterials Virtual Laband Intel Labs (Santiago Miret, Marcel
Nassar, Carmelo Gonzales).StatusMajor milestones are summarized below. Please refer to thechangelogfor details.v0.5.1 (Jun 9 2023): Model versioning implemented.v0.5.0 (Jun 8 2023): Simplified saving and loading of models. Now models can be loaded with one line of code!v0.4.0 (Jun 7 2023): Near feature parity with original TF implementations. Re-trained M3Gnet universal potential now
available.v0.1.0 (Feb 16 2023): Initial implementations of M3GNet and MEGNet architectures have been completed. Expect
bugs!Current ArchitecturesHere, we summarize the currently implemented architectures in MatGL. It should be stressed that this is by no means
an exhaustive list, and we expect new architectures to be added by the core MatGL team as well as other contributors
in future.Figure: Schematic of M3GNet/MEGNetMEGNetMatErials Graph Network (MEGNet)is an implementation of DeepMind'sgraph networksfor
machine learning in materials science. We have demonstrated its success in achieving low prediction errors in a broad
array of properties in bothmolecules and crystals. New releases have included our recent work onmulti-fidelity materials property modeling. Figure 1 shows the sequential update steps of the graph
network, whereby bonds, atoms, and global state attributes are updated using information from each other, generating an output graph.M3GNetMaterials 3-body Graph Network (M3GNet)is a new materials graph neural network architecture that
incorporates 3-body interactions in MEGNet. An additional difference is the addition of the coordinates for atoms and
the 3×3 lattice matrix in crystals, which are necessary for obtaining tensorial quantities such as forces and
stresses via auto-differentiation. As a framework, M3GNet has diverse applications, including:Interatomic potential development.With the same training data, M3GNet performs similarly to state-of-the-art
machine learning interatomic potentials (MLIPs). However, a key feature of a graph representation is its
flexibility to scale to diverse chemical spaces. One of the key accomplishments of M3GNet is the development of auniversal IPthat can work across the entire periodic table of the elements by training on relaxations
performed in theMaterials Project.Surrogate models for property predictions.Like the previous MEGNet architecture, M3GNet can be used to develop
surrogate models for property predictions, achieving in many cases accuracies that are better or similar to other
state-of-the-art ML models.For detailed performance benchmarks, please refer to the publications in theReferencessection.InstallationMatgl can be installed via pip for the latest stable version:pipinstallmatglFor the latest dev version, please clone this repo and install using:pipinstall-e.CUDA (GPU) installationIf you intend to use CUDA (GPU) to speed up training, it is important to install the appropriate versions of PyTorch
and DGL. The basic instructions are given below, but it is recommended that you consult thePyTorch docsandDGL docsif you
run into any problems.pipinstalltorchtorchvisiontorchaudio--index-urlhttps://download.pytorch.org/whl/cu118
pipinstalldgl-fhttps://data.dgl.ai/wheels/cu118/repo.html
pipinstalldglgo-fhttps://data.dgl.ai/wheels-test/repo.htmlUsagePre-trained M3GNet universal potential and MEGNet models for the Materials Project formation energy and
multi-fidelity band gap are now available.Command line (from v0.6.2)A CLI tool now provides the capability to perform quick relaxations or predictions using pre-trained models, as well
as other simple administrative tasks (e.g., clearing the cache). Some simple examples:To perform a relaxation,mglrelax--infileLi2O.cif--outfileLi2O_relax.cifTo use one of the pre-trained property models,mglpredict--modelM3GNet-MP-2018.6.1-Eform--infileLi2O.cifTo clear the cache,mglclearFor a full range of options, usemgl -h.CodeUsers who just want to use the models out of the box should use the newly implementedmatgl.load_modelconvenience
method. The following is an example of a prediction of the formation energy for CsCl.frompymatgen.coreimportLattice,Structureimportmatglmodel=matgl.load_model("MEGNet-MP-2018.6.1-Eform")# This is the structure obtained from the Materials Project.struct=Structure.from_spacegroup("Pm-3m",Lattice.cubic(4.1437),["Cs","Cl"],[[0,0,0],[0.5,0.5,0.5]])eform=model.predict_structure(struct)print(f"The predicted formation energy for CsCl is{float(eform.numpy()):.3f}eV/atom.")To obtain a listing of available pre-trained models,importmatglprint(matgl.get_available_pretrained_models())Pytorch HubThe pre-trained models are also available on Pytorch hub. To use these models, simply install matgl and use the
following commands:importtorch# To obtain a listing of modelstorch.hub.list("materialsvirtuallab/matgl",force_reload=True)# To load a modelmodel=torch.hub.load("materialsvirtuallab/matgl",'m3gnet_universal_potential')TutorialsWe wrotetutorialson how to use MatGL. These were generated from [Jupyter notebooks]jupyternb, which can be directly run onGoogle Colab.ResourcesAPI docsfor all classes and methods.Developer Guideoutlines the key design elements ofmatgl, especially for developers wishing to
train and contribute matgl models.AdvancedSoft has implemented aLAMMPS interfaceto both the TF and MatGL version of M3Gnet.ReferencesA MatGL publication is currently being written. For now, pls refer to the CITATION.cff file for the citation
information. If you are using any of the pretrained models, please cite the relevant works below:MEGNetChen, C.; Ye, W.; Zuo, Y.; Zheng, C.; Ong, S. P.Graph Networks as a Universal Machine Learning Framework for
Molecules and Crystals.Chem. Mater. 2019, 31 (9), 3564–3572. DOI:10.1021/acs.chemmater.9b01294.Multi-fidelity MEGNetChen, C.; Zuo, Y.; Ye, W.; Li, X.; Ong, S. P.Learning Properties of Ordered and Disordered Materials from
Multi-Fidelity Data.Nature Computational Science, 2021, 1, 46–53. DOI:10.1038/s43588-020-00002-x.M3GNetChen, C., Ong, S.P.A universal graph deep learning interatomic potential for the periodic table.Nature
Computational Science, 2023, 2, 718–728. DOI:10.1038/s43588-022-00349-3.FAQsTheM3GNet-MP-2021.2.8-PESdiffers from the original TensorFlow (TF) implementation!Answer:M3GNet-MP-2021.2.8-PESis a refitted model with some data improvements and minor architectural changes.
Porting over the weights from the TF version to DGL/PyTorch is non-trivial. We have performed reasonable benchmarking
to ensure that the new implementation reproduces the broad error characteristics of the original TF implementation
(seeexamples). However, it is not expected to reproduce the TF version exactly. This refitted model
serves as a baseline for future model improvements. We do not believe there is value in expending the resources
to reproduce the TF version exactly.I am getting errors withmatgl.load_model()!Answer:The most likely reason is that you have a cached older version of the model. We often refactor models to
ensure the best implementation. This can usually be solved by updating yourmatglto the latest version
and clearing your cache using the following commandmgl clear. On the next run, the latest model will be
downloaded. With effect from v0.5.2, we have implemented a model versioning scheme that will detect code vs model
version conflicts and alert the user of such problems.What pre-trained models should I be using?Answer:There is no one definitive answer. In general, the newer the architecture and dataset, the more likely
the model performs better. However, it should also be noted that a model operating on a more diverse dataset may
compromise on  performance on a specific system. The best way is to look at the READMEs included with each model
and do some tests on the systems you are interested in.How do I contribute to matgl?Answer:For code contributions, please fork and submit pull requests. You should read thedeveloper guideto understand the general design guidelines. We welcome pre-trained model
contributions as well, which should also be submitted via PRs. Please follow the folder structure of the
pretrained models. In particular, we expect all models to come with aREADME.mdand notebook
documenting its use and its key performance metrics. Also, we expect contributions to be on new properties
or systems or to significantly outperform the existing models. We will develop an alternative means for model
sharing in the future.None of your models do what I need. Where can I get help?Answer:Please contactProf Ongwith a brief description of your needs. For simple problems, we are
glad to advise and point you in the right direction. For more complicated problems, we are always open to
academic collaborations or projects. We also offerconsulting servicesfor companies with unique needs,
including but not limited to custom data generation, model development and materials design.AcknowledgmentsThis work was primarily supported by theMaterials Project, funded by the U.S. Department of Energy, Office of
Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under contract no.
DE-AC02-05-CH11231: Materials Project program KC23MP. This work used the Expanse supercomputing cluster at the Extreme
Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number
ACI-1548562.