Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
This work addresses the problem of predicting properties in twisted layered materials for researchers in materials science and computational physics, representing a significant but incremental advance in machine-learned interatomic potentials.
The authors tackled the challenge of predicting atomic structures in twisted layered materials by introducing a split machine-learned interatomic potential and dataset curation approach, which improved energy and force prediction accuracy tenfold compared to conventional models. They also developed a new validation metric for moiré structures and demonstrated that accurate structural predictions lead to reliable electronic properties in HfS2/GaS bilayers.
Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic structure in materials displaying large moiré domains. Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions and significantly improves model accuracy -- with a tenfold increase in energy and force prediction accuracy relative to conventional models. We further demonstrate that traditional MLIP validation metrics -- force and energy errors -- are inadequate for moiré structures and develop a more holistic, physically-motivated metric based on the distribution of stacking configurations. This metric effectively compares the entirety of large-scale moiré domains between two structures instead of relying on conventional measures evaluated on smaller commensurate cells. Finally, we establish that one-dimensional instead of two-dimensional moiré structures can serve as efficient surrogate systems for validating MLIPs, allowing for a practical model validation protocol against explicit DFT calculations. Applying our framework to HfS2/GaS bilayers reveals that accurate structural predictions directly translate into reliable electronic properties. Our model-agnostic approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moiré materials, from bilayer to complex multilayers, with rigorously validated accuracy.