Dispersion-corrected machine learning potentials for 2D van der Waals materials
Phys. Rev. Materials 9, 074007 – Published 23 July, 2025
DOI: https://doi.org/10.1103/cl8c-8f1f
Abstract
Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio accuracy. A number of MLIPs trained on energies and forces from density functional theory (DFT) calculations employing semi-local exchange-correlation (xc) functionals have recently been introduced. Here, we benchmark the performance of six dispersion-corrected MLIPs on a dataset of van der Waals heterobilayers containing between 4 and 300 atoms in the moiré cell. Using various structure similarity metrics, we compare the relaxed heterostructures to the ground truth DFT results. Several of the models reach a precision within the uncertainty on the DFT results stemming from the choice of xc functional. We further explore how the structural inaccuracies propagate to the electronic properties, and find mean absolute errors on the band energies as low as 35 meV. Our results demonstrate that recent MLIPs after dispersion corrections are on par with DFT for general vdW heterostructures, and thus justify their application to complex and experimentally relevant two-dimensional (2D) materials.
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This article appears in the following collection:
Machine Learning for Materials Discovery and Understanding
The Editors of Physical Review Materials are pleased to present the Collection on Machine Learning for Materials Discovery and Understanding, highlighting cutting-edge advances in machine learning method development and applications for materials discovery and fundamental understanding of the structure-property-function relationship. The Collection is being guest-edited by Deyu Lu of Brookhaven National Laboratory (USA) and Jinlan Wang of Southeast University (China). Every article published in this collection underwent a rigorous peer review process, adhering to the same high standards applied to all papers. The Physical Review Materials editorial team managed the peer review and made all editorial decisions.
