Dispersion-corrected machine learning potentials for 2D van der Waals materials

Two-dimensional (2D) materials and their heterostructures continue to attract significant research interest owing to their unique physical properties and promising technological applications. The weak van der Waals interactions between individual layers give rise to atomically sharp interfaces and intricate moire patterns, often involving unit cells with thousands of atoms. These structural complexities present major challenges for $a\phantom{\rule{0}{0ex}}b$ $i\phantom{\rule{0}{0ex}}n\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o$ simulations. In this study, the authors systematically evaluate the performance of several universal machine learning interatomic potentials (MLIPs), employing a range of structural and electronic similarity metrics tailored to 2D van der Waals heterostructures. The results indicate that the most accurate MLIPs reach a level of precision comparable to the intrinsic uncertainty of density functional theory due to the choice of exchange correlation functional.