Benchmarking Universal Machine Learning Force Fields with Hydrogen-Bonding Cooperativity (Communications in Computational Chemistry, 2025)
Machine learning force fields (MLFFs) offer a promising balance between quantum mechanical (QM) accuracy andmolecular mechanics efficiency. While MLFFs have shown strong performance in modeling short-range interactions andreproducing potential energy surfaces, their ability to capture long-range cooperative effects remains underexplored. In this study,we assess the ability of three MLFF models — ANI, MACE-OFF, and Orb — to reproduce cooperative interactions arising fromenvironmental induction and dispersion, which are essential for many biomolecular processes. Using a recently proposedframework, we quantify hydrogen bond (H-bond) cooperativity in N-methylacetamide polymers. Our results show that all MLFFscapture cooperativity to some extent, with MACE-OFF yielding the closest agreement with QM data. These findings highlightthe importance of evaluating many-body effects in MLFFs and suggest that H-bond cooperativity can serve as a useful benchmarkfor improving their physical fidelity.
Keywords: Machine learning force field; cooperative effects; self-assembly; neural network potential; hydrogen bond