用哈密顿信息增强分子构型，提升机器学习势能模型精度
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
- 基于泰勒展开的两种构型增广方法，无需修改训练流程
- 在非平衡与平衡数据集上显著提升势能面和力的预测准确率
- 适合需要高精度哈密顿量的分子动力学与过渡态搜索任务
尽管机器学习原子间势能（MLIP）已成功学习势能面（PES）和原子受力，但许多实际应用如振动分析和过渡态搜索仍高度依赖于PES的哈密顿矩阵。然而，标准的MLIP通常仅基于能量和力进行训练，导致哈密顿信息未被充分利用。现有显式引入哈密顿的训练方法需修改网络结构，并因高阶反向传播带来显著计算与内存开销。为此，我们提出两种基于哈密顿的构型增广方案：各向同性高斯位移（UniAug）和模态加权位移（ModeAug）。二者均采用简单的泰勒展开，实现有效增广而无需改变训练目标或扩展自动微分图，可无缝集成至现有架构与训练流程中。在非平衡与平衡数据集上的全面评估表明，该方法提升了模型精度，并提供了实用、任务导向的指导原则。
原文摘要 · Abstract (English)
While machine-learning interatomic potentials (MLIPs) have successfully learned potential energy surfaces (PES) and atomic forces, many practical applications, such as vibrational analysis and transition state search, rely heavily on the PES Hessian. Yet, standard MLIPs tend to be trained on energy and forces alone, leaving Hessian information largely unexploited. Meanwhile, existing methods that explicitly incorporate the Hessian into training objectives require architectural modifications and introduce significant computational and memory overheads due to higher-order backpropagation. To address these limitations, we propose two Hessian-derived data augmentation schemes: isotropic Gaussian displacement (\textbf{UniAug}) and normal mode-weighted displacement (\textbf{ModeAug}). Both methods utilize simple Taylor expansions, achieving effective augmentation without altering training objectives or extending the autograd graph. This allows seamless, plug-and-play integration with existing architectures and training pipelines. Comprehensive evaluations across non-equilibrium and equilibrium datasets demonstrate that our approach enhances model accuracy while providing practical, task-specific guidelines.
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