A Comparative Study of Multiwalled WS 2 Nanotubes Using Universal Machine Learning Interatomic Potentials and an Atomistic Force Field

nanotubes using the CHGNet universal machine-learning interatomic potential (uMLIP) and the atomistic force field previously developed by our team. For the first time, both the original ("vanilla") and fine-tuned uMLIPs were benchmarked against quantum chemical calculations and atomistic force field models for single-walled and multiwalled nanotubes. The comparison reveals excellent agreement in structural features between the approaches, although the CHGNet uMLIP tends to underestimate interlayer interaction contributions, affecting certain energy-related parameters. However, uMLIPs are a significantly less labor-intensive alternative to traditional force field engineering. With further refinement, they could be used to study multiwalled nanotubes.