To address the limitations of DFT's high computational cost, machine-learning interatomic potentials (MLIPs) have become valuable tools . When trained on highfidelity DFT data, MLIPs can efficiently explore large configurational spaces while maintaining DFT-level accuracy . However, developing an efficient MLIP that captures the complex behavior of reduced TiO2 surfaces is difficult, especially due to TiO2's strong ionic character and the significant long-range interactions involved.