Charting the Chemical Space of Zintl Phases with Graph Neural Networks and Bonding Insights

Abstract

A large number of Zintl phases have been discovered by solid-state chemists driven by empirical knowledge, chemical intuition and in some cases, through serendipitous accidents. These discoveries have only scratched the surface, given the vast compositional and structural diversity that Zintl phases can accommodate. The large chemical space of Zintl phases, as well as intermetallic compounds in general, remain under-explored. Here, we use graph neural networks and the upper bound energy minimization approach to efficiently scan a large chemical space of >90,000 hypothetical Zintl phases and accurately discover 1809 new thermodynamically stable phases with 90% precision, as validated with first-principles calculations. We show that our approach is more than 2X more accurate than M3GNet (40% precision) on the same dataset. Using a random forest model and SHAP analysis, we demonstrate the critical role of ionic bonding in the thermodynamic stability of Zintl phases. Our results not only expand the known chemical landscape of Zintl phases but also highlight the efficacy of machine learning frameworks combined with domain knowledge in uncovering chemically meaningful insights across complex intermetallics.

Supplementary files

Article information

Article type
Paper
Submitted
31 Jul 2025
Accepted
29 Aug 2025
First published
01 Sep 2025
This article is Open Access
Creative Commons BY-NC license

J. Mater. Chem. A, 2025, Accepted Manuscript

Charting the Chemical Space of Zintl Phases with Graph Neural Networks and Bonding Insights

R. Chaliha, M. Kothakonda, C. Lee, J. Law, Q. Yang, S. Bobev and P. Gorai, J. Mater. Chem. A, 2025, Accepted Manuscript , DOI: 10.1039/D5TA06210A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements