The time scales of biological processes, such as protein folding, protein recognition, and transitions between different metastable states, among others, range from 10 -6 to 10 -3 s. Thus, they are orders of magnitude slower than typical molecular motions (i.e., 10 -15 -10 -12 s) captured by all-atom molecular dynamics (AA-MD). The length scales of molecular rearrangements are also much smaller in AA-MD simulation than those of larger structural changes typically observed experimentally in biological systems. Moreover, parameterizing accurate and transferable all-atom potentials has always been challenging in biomolecular simulations. On the one hand, capturing intra-and intermolecular (short-and long-range) interactions of various complex molecular systems using a series of physically inspired mathematical functions, as done in classical force fields (e.g., AMBER, CHARMM, OPLS), can often require system-specific reparameterization and fine-tuning to reach a desired level of accuracy in predicting structural and thermodynamic properties. On the other hand, quantum-mechanical (QM) methods (e.g., coupled cluster, density functional theory, density functional tight binding) offer the ability to accurately describe molecular interactions across a wide variety of systems. However, they remain computationally prohibitive for larger systems (>thousands of atoms) and long timescales, even with the most efficient algorithms and state-of-the-art supercomputers . This presents a barrier to the application of QM methods for addressing biomolecular problems, such as protein mutations or structural transitions. To mitigate this issue, QM methods have been integrated with machine learning (ML) techniques, leading to the development of well-established approaches aimed at generating Efficient, Accurate, Scalable, and Transferable (EAST) ML models to advance biomolecular simulations with atomistic resolution. Among them, we have ML potentials (MLPs) trained on QM datasets using state-of-the-art neural network architectures , the augmentation of semi-empirical methods with ML techniques , and the development of hybrid molecular mechanics/ML approaches .