Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing

• URL: http://arxiv.org/abs/2601.03774v1
• Date: Wed, 07 Jan 2026 10:12:34 GMT
• Title: Scalable Machine Learning Force Fields for Macromolecular Systems Through Long-Range Aware Message Passing
• Authors: Chu Wang, Lin Huang, Xinran Wei, Tao Qin, Arthur Jiang, Lixue Cheng, Jia Zhang,
• Abstract summary: Machine learning force fields (MLFFs) have revolutionized molecular simulations by providing quantum mechanical accuracy at the speed of molecular mechanical computations.<n>However, a fundamental reliance of these models on fixed-cutoff architectures limits their applicability to macromolecular systems where long-range interactions dominate.<n>We demonstrate that this locality constraint causes force prediction errors to scale monotonically with system size, revealing a critical architectural bottleneck.<n>We introduce E2Former-LSR, an equivariant transformer that explicitly integrates long-range attention blocks. E2Former-LSR achieves stable error scaling, superior fidelity in capturing non-covalent decay, and
• Score: 18.50744268453995
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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