The MLIP model is implemented using the MACE architecture, an equivariant message-passing graph tensor network that encodes many-body atomic geometry information at each layer . In this model, we utilize two message-passing layers, with spherical expansions up to lmax = 3, and incorporate 4-body messages in each layer, corresponding to a correlation order of 3. All models are constructed with a 128- To reduce the computational expense of surface structure sampling through firstprinciples calculations, we utilized a low-cost MLIP surrogate model, trained on a finite set of DFT data. Due to the strong ionic bonding character of TiO2, long-range Coulomb interactions play a significant role . As demonstrated in Figure , the typical cutoff radius of 5-6 Å used in most descriptor-based MLIPs is inadequate for capturing these long-range interactions accurately, as the locality of the forces does not converge within this range. Consequently, previous studies employing MLIPs for such systems have adopted various strategies, including global or non-local descriptors , Δ-learning , which combines MLIP with baseline empirical potentials, machine-learned charge equilibration models , and artificial neural network potentials . Finally, message-passing neural networks can extend the range of local interatomic potentials beyond the employed cutoff while maximizing the efficiency .