Exploring charge density waves in two-dimensional NbSe2 with machine learning
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Published version
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Author(s)
Type
Journal Article
Abstract
Niobium diselenide (NbSe2) has garnered significant attention due to the coexistence of superconductivity and charge density waves (CDWs) down to the monolayer limit. However, realistic modeling of CDWs—capturing effects such as layer number, twist angle, and strain—remains challenging due to the high computational cost of first-principles methods. Here, we develop a physically informed workflow for training machine-learning interatomic potentials (MLIPs) based on the E(3)-equivariant Allegro architecture, tailored to capture the subtle structural and dynamical signatures of CDWs in mono- and bilayer NbSe2. We find that while CDW lattice distortions are relatively easy to learn, modeling vibrational properties remains more challenging. It requires targeted dataset design and careful hyperparameter tuning, pushing the boundaries and testing the extensibility of current MLIP frameworks. Our MLIPs enable reliable simulations of commensurate and incommensurate CDW phases, including their sensitivity to dimensionality and stacking, as well as CDW dynamics, phonons, and transition temperatures estimated via the stochastic self-consistent harmonic approximation. This work opens new possibilities for studying and tuning CDWs in NbSe2 and other two-dimensional systems, with implications for electron-phonon coupling, superconductivity, and advanced materials design.
Date Issued
2026-04-24
Date Acceptance
2026-03-19
Citation
npj Computational Materials, 2026, 12 (1)
ISSN
2057-3960
Publisher
Nature Portfolio
Journal / Book Title
npj Computational Materials
Volume
12
Issue
1
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Identifier
10.1038/s41524-026-02063-4
Publication Status
Published
Article Number
239
Date Publish Online
2026-04-24