Neural Network Reparametrization for Accelerated Optimization in Molecular Simulations

We propose a novel approach to molecular simulations using neural network reparametrization, which offers a flexible alternative to traditional coarse-graining methods. Unlike conventional techniques that strictly reduce degrees of freedom, the complexity of the system can be adjusted in our model, sometimes increasing it to simplify the optimization process. Our approach also maintains continuous access to fine-grained modes and eliminates the need for force-matching, enhancing both the efficiency and accuracy of energy minimization. Importantly, our framework allows for the use of potentially arbitrary neural networks (e.g., Graph Neural Networks (GNN)) to perform the reparametrization, incorporating CG modes as needed. In fact, our experiments using very weak molecular forces (Lennard-Jones potential) the GNN-based model is the sole model to find the correct configuration. Similarly, in protein-folding scenarios, our GNN-based CG method consistently outperforms traditional optimization methods. It not only recovers the target structures more accurately but also achieves faster convergence to the deepest energy states. This work demonstrates significant advancements in molecular simulations by optimizing energy minimization and convergence speeds, offering a new, efficient framework for simulating complex molecular systems. 1 Scientific simulations, particularly in molecular dynamics (MD), face fundamental challenges in finding optimal configurations. The energy landscapes of these systems are characterized by numerous saddle points and local minima, making it difficult for traditional optimization methods to discover the most stable states. This complexity stems from the interplay between different scales of interactions, from strong covalent bonds to weak van der Waals forces, leading to slow convergence in gradient-based methods and often suboptimal results. For instance, in protein folding, the strong peptide bonds create steep energy barriers while weak hydrophobic interactions guide the overall folding process,