Predicting thermodynamic stability of inorganic compounds using ensemble machine learning based on electron configuration

Hao Zou, Haochen Zhao, Mingming Lu, Jiong Wang, Zeyu Deng and Jianxin Wang ()
Additional contact information
Hao Zou: Central South University
Haochen Zhao: Central South University
Mingming Lu: Central South University
Jiong Wang: Central South University
Zeyu Deng: National University of Singapore
Jianxin Wang: Central South University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Machine learning offers a promising avenue for expediting the discovery of new compounds by accurately predicting their thermodynamic stability. This approach provides significant advantages in terms of time and resource efficiency compared to traditional experimental and modeling methods. However, most existing models are constructed based on specific domain knowledge, potentially introducing biases that impact their performance. Here, we propose a machine learning framework rooted in electron configuration, further enhanced through stack generalization with two additional models grounded in diverse domain knowledge. Experimental results validate the efficacy of our model in accurately predicting the stability of compounds, achieving an Area Under the Curve score of 0.988. Notably, our model demonstrates exceptional efficiency in sample utilization, requiring only one-seventh of the data used by existing models to achieve the same performance. To underscore the versatility of our approach, we present three illustrative examples showcasing its effectiveness in navigating unexplored composition space. We present two case studies to demonstrate that our method can facilitate the exploration of new two-dimensional wide bandgap semiconductors and double perovskite oxides. Validation results from first-principles calculations indicate that our method demonstrates remarkable accuracy in correctly identifying stable compounds.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-55525-y Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-024-55525-y

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-55525-y

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().