Accurate and efficient prediction of molecular properties is a cornerstone of computational chemistry. While quantum mechanical (QM) calculations provide unparalleled precision, their computational cost often limits their application to large-scale systems. To bridge this gap, machine learning (ML) potentials have emerged as promising alternatives, capable of predicting molecular properties with reasonable accuracy and remarkable efficiency.