提出多尺度结构集成模型，让机器学习力场跨尺度精准模拟物理相互作用。
Machine Learning Multiscale Interactions
- 通过软粗粒化池化构建原子到粗粒节点的平滑分配，实现多尺度表征
- 在生物分子折叠和分子-石墨烯体系中实现量子精度的能量预测
- 兼容多种力场模型，适合需跨尺度建模的分子与材料研究
真实物理系统在多个长度和时间尺度上表现出涌现相互作用，对预测性机器学习模型构成重大挑战。多数科学机器学习模型仅关注有限范围的相互作用。尽管机器学习力场（MLFF）可达到近量子精度，但普遍采用的消息传递层会忽略长程多体效应。本文提出多尺度结构集成（MuSE）模型，利用软粗粒化池化从原子到粗粒节点的平滑分数分配构建粗粒表示，使MLFF模块可在多尺度下运行。MuSE架构无关，可耦合SO3krates、MACE和PaiNN等MLFF模型，适用于分子与材料。通过海森矩阵基准测试、生物分子折叠轨迹及分子-石墨烯纳米结构的能量剖面验证，MuSE能准确捕捉相关尺度下的量子力学相互作用——而其他近期长程ML模型无法做到。
原文摘要 · Abstract (English)
Realistic physical systems are characterised by emergent interactions across multiple length and time scales, posing a significant challenge for predictive machine learning (ML) models. Most scientific ML models focus on a narrow range of interactions. While machine learning force fields (MLFFs) offer near-quantum accuracy, the ubiquitous message-passing layers miss long-range many-body effects. Here we introduce the Multiscale Structural Ensemble (MuSE), a hierarchical model that uses Soft Coarse-Graining Pooling to construct coarse representations from smooth fractional assignments of atoms to coarse nodes, enabling MLFF modules to operate across multiple scales. MuSE is architecture-agnostic and coupled with SO3krates, MACE, and PaiNN MLFFs for both molecules and materials. We demonstrate the power of MuSE through Hessian-based benchmarks, folding trajectories for biomolecules, and energy profiles in molecule-graphene nanostructures, where MuSE accurately captures quantum-mechanical interactions at relevant scales -- unlike other recent long-range ML models.
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