Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Expanding materials science with universal many-body graph neural networks

A universal interatomic potential for the periodic table has been developed by combining graph neural networks with three-body interactions. This M3GNet potential can perform structural relaxations, dynamic simulations and property predictions for materials across a diverse chemical space.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type



Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Many-body graph convolution.


  1. Martin, R. M. Electronic Structure: Basic Theory and Practical Methods (Cambridge Univ. Press, 2020). A textbook for electronic structure calculations.

  2. Drautz, R. Atomic cluster expansion for accurate and transferable interatomic potentials. Phys. Rev. B 99, 014104 (2019). This paper reports complete descriptors for local atomic environment-based IAPs.

    Article  Google Scholar 

  3. Jain, A. et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013). This paper reports the Materials Project database.

    Article  Google Scholar 

  4. Battaglia, P.W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at (2018). This paper defines the framework of graph networks.

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Chen, C. et al. A universal graph deep learning interatomic potential for the periodic table. Nat. Comput. Sci. (2022).

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Expanding materials science with universal many-body graph neural networks. Nat Comput Sci 2, 703–704 (2022).

Download citation

  • Published:

  • Issue Date:

  • DOI:


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing