2026-10-14T08:58:17Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33900940 2026-09-17T16:14:53Z portal_1197 item_type_3 month_year_09_2026

Data for Understanding domain reconstruction of twisted transition metal dichalcogenide bilayers through machine learned interatomic potentials Anas Siddiqui (25016974) Chung Xu (25034158) Samuel J. Magorrian (24941272) Nicholas Hine (7188104) Machine learning Semiconductors Transition metal halides Migrated from ePrints In the study of twisted bilayers of two-dimensional materials, a detailed picture of the relaxations and layer-corrugations that occur due to interlayer interaction is crucial to predicting how their electronic and optical properties depend on twist angle and the resulting large-scale moir´e pattern. 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◦. We demonstrate effects including triangular and kagome-like patterns in low-twist P and AP systems, respectively, as well as twirling around domain nodes in heterobilayers. For homobilayers, we present and parameterize an energy model that decomposes the total energy into contributions from constituent structural units. We further provide an experimental comparison for the MoS2 homobilayer, showing strong agreement with observed reconstruction patterns. 2026-09-17T16:14:53Z Dataset Dataset 10.82444/warw.33900940.v1 https://figshare.com/articles/dataset/Data_for_Understanding_domain_reconstruction_of_twisted_transition_metal_dichalcogenide_bilayers_through_machine_learned_interatomic_potentials/33900940 CC BY-NC 4.0