Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing
This work addresses a critical bottleneck for simulating large-scale chemical and biological systems, enabling high-fidelity molecular dynamics.
The paper tackled the problem of machine learning force fields (MLFFs) being limited by fixed-cutoff architectures for macromolecular systems, and introduced E2Former-LSR, an equivariant transformer that achieved stable error scaling and up to 30% speedup compared to local models.
Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations. However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate. We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck. To overcome this, we establish the systematically designed MolLR25 ({Mol}ecules with {L}ong-{R}ange effect) benchmark up to 1200 atoms, generated using high-fidelity DFT, and introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR exhibits stable error scaling, achieves superior fidelity in capturing non-covalent decay, and maintains precision on complex protein conformations. Crucially, its efficient design provides up to 30% speedup compared to purely local models. This work validates the necessity of non-local architectures for generalizable MLFFs, enabling high-fidelity molecular dynamics for large-scale chemical and biological systems.