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From QM/MM to ML/MM: A New Era in Multiscale Modeling 1. This review article explores the evolution from Quantum Mechanics/Molecular Mechanics (QM/MM) to Machine Learning/Molecular Mechanics (ML/MM) methods, highlighting how ML potentials offer a faster alternative to QM while maintaining accuracy. This transition is crucial for simulating complex biological and condensed-phase environments with reduced computational costs. 2. The article emphasizes the key challenge of coupling ML and MM regions, addressing this through three main strategies: Mechanical Embedding (ME), Polarization-Corrected Mechanical Embedding (PCME), and Environment-Integrated Embedding (EIE). Each strategy offers different trade-offs between accuracy and computational efficiency. 3. Mechanical Embedding (ME) is highlighted as the simplest approach, where the ML region interacts with fixed MM charges via classical electrostatics. This method is computationally efficient but neglects polarization effects, which can be critical in some systems. 4. Polarization-Corrected Mechanical Embedding (PCME) supplements a vacuum-trained ML potential with post-hoc electrostatic corrections. This approach preserves transferability while approximating environment effects, making it suitable for systems where polarization plays a significant role. 5. Environment-Integrated Embedding (EIE) involves training ML potentials with explicit inclusion of MM-derived fields. While this enhances accuracy, it requires specialized data and is more computationally intensive, making it ideal for applications where high fidelity is essential. 6. The review surveys existing ML/MM frameworks, categorizing them based on the embedding strategies used. It also highlights key applications, such as protein-ligand binding and solvation studies, demonstrating the practical utility of ML/MM methods. 7. The article concludes by discussing the current state-of-the-art in ML/MM and outlining future challenges, including the need for more transferable and general-purpose ML potentials, as well as the development of larger and more diverse training datasets. 📜Paper: doi.org/10.26434/chemrxiv-20… #MultiscaleModeling #MLMM #QM_MM #ComputationalChemistry #MachineLearning #Biophysics