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Materials Graph Library matgl

Table of contents

Introduction

MatGL (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. The goal is for MatGL to serve as an extensible platform to develop and share materials graph deep learning models.

This first version of MatGL is a collaboration between the Materials Virtual Lab and Intel Labs.

MatGL is part of the MatML ecosystem, which includes the MatGL (Materials Graph Library) and maml (MAterials Machine Learning) packages, the MatPES (Materials Potential Energy Surface) dataset, and the MatCalc (Materials Calculator).

Status

Major milestones are summarized below. Please refer to the changelog for details.

  • v2.0.0 (Nov 13 2025): QET architecture added. PYG backend is now the default.
  • v1.3.0 (Aug 12 2025): Pretrained molecular potentials and PyG framework added.
  • v1.1.0 (May 7 2024): Implementation of CHGNet + pre-trained models.
  • v1.0.0 (Feb 14 2024): Implementation of TensorNet and SO3Net.
  • 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!

Major update: v2.0.0 (Nov 12 2025)

We are in the process of moving away from the Deep Graph Library (DGL) framework to Pytorch Geometric (PyG) or even a pure PyTorch framework. This is motivated by the fact that DGL is no longer actively maintained. For now, both PYG and DGL models are available.

From v2.0.0, MatGL will default to a PyG backend, and DGL is no longer a required dependency. For now, only TensorNet has been re-implemented in PYG. To use the DGL-based models (which includes the new QET), you will need to install the DGL dependencies manually:

pip install "numpy<2"
pip install dgl==2.2.0
pip install torch==2.3.0
pip install "torchdata<=0.8.0"

and set the backend either via the environment variable MATGL_BACKEND=DGL or by using

import matgl
matgl.set_backend("DGL")

Current Architectures

m3gnet_schematic

Figure: Schematic of M3GNet/MEGNet

Here, 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 the future.

  • QET (DGL only, PYG coming soon), pronounced as “ket”, is a charge-equilibrated TensorNet architecture. It is an equivariant, charge-aware architecture that attains linear scaling with system size via an analytically solvable charge-equilibration scheme. A pre-trained QET-MatQ FP is available, which matches state-of-the-art FPs on standard materials property benchmarks but delivers qualitatively different predictions in systems dominated by charge transfer, e.g., NaCl–\ce{CaCl2} ionic liquid, reactive processes at the Li/\ce{Li6PS5Cl} solid-electrolyte interface, and supports simulations under applied electrochemical potentials.
  • TensorNet (PYG and DGL) is an O(3)-equivariant message-passing neural network architecture that leverages Cartesian tensor representations. It is a generalization of the SO3Net architecture, which is a minimalist SO(3)-equivariant neural network. In general, TensorNet has been shown to be much more data and parameter efficient than other equivariant architectures. It is currently the default architecture used in the [Materials Virtual Lab].
  • Crystal Hamiltonian Graph Network (CHGNet) (DGL only) is a graph neural network based MLIP. CHGNet involves atom graphs to capture atom bond relations and bond graph to capture angular information. It specializes in capturing the atomic charges through learning and predicting DFT atomic magnetic moments. See original implementation
  • Materials 3-body Graph Network (M3GNet) is an invariant graph neural network architecture that incorporates 3-body interactions. 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 a foundation potential that can work across the entire periodic table of the elements by training on relaxations performed in the Materials Project. 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.
  • MatErials Graph Network (MEGNet) (DGL only) is an implementation of DeepMind’s graph networks for machine learning in materials science. We have demonstrated its success in achieving low prediction errors in a broad array of properties in both molecules and crystals. New releases have included our recent work on multi-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.

For detailed performance benchmarks, please refer to the publications in the References section.

Installation

Matgl can be installed via pip:

pip install matgl

If you need to use DGL, it is recommended you install the latest version of DGL before installing matgl.

pip install dgl -f https://data.dgl.ai/wheels/torch-2.4/repo.html

CUDA (GPU) installation

If 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 the PyTorch docs and DGL docs if you run into any problems.

pip install torch==2.2.0 --index-url https://download.pytorch.org/whl/cu121
pip install dgl -f https://data.dgl.ai/wheels/cu121/repo.html
pip install dglgo -f https://data.dgl.ai/wheels-test/repo.html

Docker images

Docker images have now been built for matgl, together with LAMMPS support. They are available at the Materials Virtual Lab Docker Repository. If you wish to use MatGL with LAMMPS, this is probably the easiest option.

Usage

Pre-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:

  1. To perform a relaxation,

     mgl relax --infile Li2O.cif --outfile Li2O_relax.cif

  2. To use one of the pre-trained property models,

     mgl predict --model M3GNet-MP-2018.6.1-Eform --infile Li2O.cif

  3. To clear the cache,

     mgl clear

For a full range of options, use mgl -h.

Code

Users who just want to use the models out of the box should use the newly implemented matgl.load_model convenience method. The following is an example of a prediction of the formation energy for CsCl.

from pymatgen.core import Lattice, Structure
import matgl

model = 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,

import matgl
print(matgl.get_available_pretrained_models())

Pytorch Hub

The pre-trained models are also available on Pytorch hub. To use these models, simply install matgl and use the following commands:

import torch

# To obtain a listing of models
torch.hub.list("materialsvirtuallab/matgl", force_reload=True)

# To load a model
model = torch.hub.load("materialsvirtuallab/matgl", 'm3gnet_universal_potential')

Model Training

In the PES training, the unit of energies, forces and stresses (optional) in the training, validation and test sets is extremely important to be consistent with the unit used in MatGL.

  • energies: a list of energies with unit eV.
  • forces: a list of nx3 force matrix with unit eV/Å, where n is the number of atom in each structure. n does not need to be the same for all structures.
  • stresses: a list of 3x3 stress matrices with unit GPa (optional)

Note: For stresses, we use the convention that compressive stress gives negative values. Stresses obtained from VASP calculations (default unit is kBar) should be multiplied by -0.1 to work directly with the model.

Tutorials

We wrote tutorials on how to use MatGL. These were generated from Jupyter notebooks, which can be directly run on Google Colab.

Resources

  • API docs for all classes and methods.
  • Developer Guide outlines the key design elements of matgl, especially for developers wishing to train and contribute matgl models.
  • AdvancedSoft has implemented a LAMMPS interface to both the TF and MatGL version of M3GNet.

References

A manuscript for MatGL has been published in npj Computational Materials. please cite the following:

MatGL

Ko, T. W.; Deng, B.; Nassar, M.; Barroso-Luque, L.; Liu, R.; Qi, J.; Thakur, A. C.; Mishra, A. R.; Liu, E.; Ceder, G.; Miret, S.; Ong, S. P. Materials Graph Library (MatGL), an Open-Source Graph Deep Learning Library for Materials Science and Chemistry. npj Comput Mater 11, 253 (2025). DOI: https://doi.org/10.1038/s41524-025-01742-y.

If you are using any of the pretrained models, please cite the relevant works below:

MEGNet

Chen, 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 MEGNet

Chen, 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.

M3GNet

Chen, 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.

CHGNet

Deng, B., Zhong, P., Jun, K. et al. CHGNet: as a pretrained universal neural network potential for charge-informed atomistic modelling. Nat Mach Intell 5, 1031–1041 (2023). DOI:10.1038/s42256-023-00716-3

TensorNet

Simeon, G. De Fabritiis, G. Tensornet: Cartesian tensor representations for efficient learning of molecular potentials. Adv. Neural Info. Process. Syst. 36, (2024). DOI: 10.48550/arXiv.2306.06482

SO3Net

Schütt, K. T., Hessmann, S. S. P., Gebauer, N. W. A., Lederer, J., Gastegger, M. SchNetPack 2.0: A neural network toolbox for atomistic machine learning. J. Chem. Phys. 158, 144801 (2023). DOI: 10.1063/5.0138367

FAQs

  1. The M3GNet-MP-2021.2.8-PES differs from the original TensorFlow (TF) implementation!

    Answer: M3GNet-MP-2021.2.8-PES is 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 (see examples). 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.

  2. I am getting errors with matgl.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 your matgl to the latest version and clearing your cache using the following command mgl 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.

  3. 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.

  4. How do I contribute to matgl?

    Answer: For code contributions, please fork and submit pull requests. You should read the developer guide to 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 a README.md and 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.

  5. None of your models do what I need. Where can I get help?

    Answer: Please contact Prof Ong with 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 offer consulting services for companies with unique needs, including but not limited to custom data generation, model development and materials design.

Acknowledgments

This work was primarily supported by the Materials 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.

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