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
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Martin, R. M. Electronic Structure: Basic Theory and Practical Methods (Cambridge Univ. Press, 2020). A textbook for electronic structure calculations.
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.
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.
Battaglia, P.W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at https://arxiv.org/abs/1806.01261 (2018). This paper defines the framework of graph networks.
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. https://doi.org/10.1038/s43588-022-00349-3 (2022).
Rights and permissions
About this article
Cite this article
Expanding materials science with universal many-body graph neural networks. Nat Comput Sci 2, 703–704 (2022). https://doi.org/10.1038/s43588-022-00360-8
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-022-00360-8
