An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene

TL;DR

This paper develops an efficient method to generate datasets and fit an atomic cluster expansion potential for modeling twisted multilayer graphene, capturing all misalignments with high accuracy and robustness.

Contribution

It introduces a novel dataset generation approach combined with active learning to develop a transferable ML potential for twisted multilayer graphene.

Findings

The potential accurately reproduces DFT calculations.

It demonstrates robustness across various simulation tasks.

The method enables efficient modeling of complex moiré patterns.

Abstract

Twisted multilayer graphene, characterized by its moir\'e 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.