Layerwise Stratification and Band Reordering in Twisted Multilayer MoTe$_2$

We introduce a generalizable, physics informed strategy for generating training data that enables a machine learning force field accurate over a broad range of twist angles and stacking layer numbers in moire systems. Applying this to multilayer twisted MoTe2 (tMoTe2), we identify a structural and electronic stratification: the two moire interface (MI) layers retain substantial lattice reconstruction even in thick multilayers, while outer bulk like layers show rapidly attenuated distortions.Surprisingly, this stratification becomes strongest not in the ultra-small twist angle regime (<~1{\deg}), where in plane domain formation is well known, but rather at intermediate angles (2-5{\deg}). Simultaneously, interlayer hybridization across the MI-bulk boundary is strongly suppressed, leading to electronic isolation. In twisted double bilayer MoTe2, this stratification gives rise to coexisting honeycomb and triangular lattice motifs in the frontier valence bands. We further demonstrate that twist angle and weak gating can create energy shift of bands belonging to the two motifs, producing Chern band reordering and nonlinear electric polarization with modest hole doping. Our approach allows efficient simulation of multilayer moire systems and reveals structural-electronic separation phenomena absent in bilayer systems.