An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene

2025/06/18 by Yangshuai Wang, Wang, Yangshuai, Drake Clark +13
Chemistry · Materials Science · #Catalytic Processes in Materials Science #Computational Physics (physics.comp-ph) #FOS: Physical sciences #Fullerene Chemistry and Applications #Graphene research and applications #Materials Science (cond-mat.mtrl-sci)

paper · pdf · doi:10.48550/arxiv.2506.15061

openalex publication_date 2025/06/18 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

Abstract

Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machine learning interatomic potentials (MLIPs) are a promising approach to model such systems. Our work develops a method to generate training and test datasets for fitting MLIPs that capture all possible misalignments but remain small-scale to facilitate efficient data generation and parameter estimation. To achieve this, we generate configurations with periodic boundary conditions suitable for DFT calculations, and then introduce an internal twist and shift within those supercell structures. Using this technique, supplemented with an active learning workflow, we fit an Atomic Cluster Expansion potential for simulating twisted multilayer graphene and test it for accuracy and robustness on a range of simulation tasks.

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