Data for Understanding domain reconstruction of twisted transition metal dichalcogenide bilayers through machine learned interatomic potentials

As the relative twist angle between the layers approaches 0, referred to as parallel (P) stacking, or 60, referred to as antiparallel (AP) stacking, reconstructions occur to maximise the area of low-energy stacking domains, with a lattice of nodes of high-energy stacking connected by domain walls (DWs).

We show that Machine Learned Interatomic Potentials (MLIPs) can provide sufficiently precise energetics of stacking, strain, shear and varying interlayer distances to exactly reproduce vdW-corrected DFT for systems dramatically larger than those that can be treated with ab initio methods. We predict, explain, and quantify the domain reconstruction patterns for all like-chalcogen combinations of the Transition Metal Dichalcogenides MoS2, MoSe2, WS2 and WSe2 down to twist angles approaching 1◦.