用图神经网络和力场直接学习，实现高精度分子动力学模拟。
Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems
- 基于图结构分割分子，直接预测核力矢量，避免能量梯度计算瓶颈。
- 仅需10%~20%参考构型数据，参数量减少超一个数量级，仍达耦合簇精度。
- 适用于复杂动态体系，适合追求高精度且长时程模拟的研究者。
复杂、易变的化学体系的高精度从头算分子动力学（AIMD）模拟受限于关联电子结构方法的高计算复杂度。为突破此瓶颈，我们提出一种鲁棒的图论分子分割框架，结合机器学习直接建模后哈特里-福克核力，达到耦合簇精度。相比依赖自动微分的能量面学习可能在链接原子雅可比矩阵上失效的问题，本方法直接预测核力矢量。通过将力矢量投影至各片段固定的主惯性轴，构建协变描述符，天然保持旋转、平移及置换不变性。通过向量值训练协议，可使可训练参数减少超过一个数量级；同时采用无监督小批量k-means空间剖分算法，仅需10%至20%参考构型即可构建高度代表性训练集。我们在高度易变的溶剂化Zundel阳离子H_{13}O_6^+ 上严格验证了该框架。全机器学习预测的AIMD轨迹成功再现了复杂动力学特征与关键结构特性，包括径向分布函数和速度自相关谱。最终，该可扩展、系统可改进的框架连接了高阶关联波函数理论与长时间尺度反应采样，为现代化学动力学模拟中类大模型迁移学习奠定基础。
原文摘要 · Abstract (English)
Accurate ab initio molecular dynamics (AIMD) simulations of complex, fluxional chemical systems are severely limited by the high computational scaling of correlated electronic structure methods. To overcome this bottleneck, we present a robust, graph-theoretic molecular fragmentation framework integrated with machine learning to directly model post-Hartree-Fock nuclear forces at coupled cluster accuracy. Bypassing the limitations of automatic differentiation on learned energy surfaces that may struggle with link-atom Jacobians, our approach directly predicts nuclear force vectors. By projecting these vectors onto fragment-fixed principal axes of inertia, we establish co-variant descriptors that naturally preserve rotational, translational, and permutational invariance. The methodology achieves exceptional high parameter efficiency through a vector-valued training protocol that reduces trainable parameters by over an order of magnitude, while an unsupervised mini-batch k-means space tessellation algorithm constructs highly representative training databases using only 10% to 20% of reference configurations. We rigorously validated this framework on the highly fluxional solvated Zundel cation H_{13}O_6^+ ). Our fully machine-learning-predicted AIMD trajectories successfully reproduced complex dynamical signatures and key structural characteristics, including radial distribution functions and the velocity autocorrelation power spectrum. Ultimately, this scalable, systematically improvable framework bridges the gap between high-level correlated wavefunction theories and long-timescale reactive sampling, laying the foundation for advanced, LLM-inspired transfer learning in modern chemical dynamics simulations.
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