However, the rise of various artificial intelligence (AI) techniques has sparked a wave of developing machine learning interatomic potentials (MLIPs). MLIPs are trained using machine learning algorithms to reproduce ab initio quantities, such as potential energy and atomic forces. For example, ANI-2x, which was trained on data from ωB97x-D/6-31G(d) calculations, achieves near-DFT levels of accuracy while maintaining computational efficiency comparable to molecular mechanics. Given by their accuracy and performance, MLIPs could potentially serve the role of ab initio models in simulating biomolecular systems with QM/MM. Incorporation of MLIPs into a molecular dynamics engine to develop a brand-new multi-scale simulation technique is appealing given their near-QM level of accuracy and near-MM level of efficiency. Thus, machine learning/molecular mechanics molecular dynamics (ML/MM MD), represents a promising opportunity for biomolecular simulations. Many MLIP models represented by the ANI series and MACE series have demonstrated robustness, transferability, and high performance. Therefore, they are ideal candidates for incorporation into MD simulation platforms.