Transferable dispersion-aware machine learning interatomic potentials for multilayer transition metal dichalcogenide heterostructures

Y Shaidu and MH Naik and SG Louie and JB Neaton, NPJ COMPUTATIONAL MATERIALS, 11, 273 (2025).

DOI: 10.1038/s41524-025-01761-9

Stacking atomically thin transition metal dichalcogenides (TMDs) into heterostructures enables exploration of exotic quantum phases, particularly through twist-angle-controlled moir & eacute; superlattices. These structures exhibit novel electronic and optical behaviors driven by atomic-scale structural reconstruction. However, studying such systems with DFT is computationally demanding due to their large unit cells and van der Waals (vdW) interactions between layers. To address this, we develop a transferable neural network potential (NNP) that includes long-range vdW corrections up to 12 angstrom with minimal overhead. Trained on vdW-corrected DFT data for Mo- and W-based TMDs with S, Se, and Te, the NNP accurately models monolayers, bilayers, heterostructures, and their interaction with h-BN substrates. It reproduces equilibrium structures, energy landscapes, phonon dispersions, and matches experimental atomic reconstructions in twisted WS2 and MoS2/WSe2 systems. We demonstrate that our NNP achieves DFT- level accuracy and high computational efficiency, enabling large-scale simulations of TMD-based moir & eacute; superlattices both with and without substrates.

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