基于海森矩阵的分子构象增强：一种可扩展且高效的机器学习原子间势策略
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
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中文总结 AI 辅助
该研究针对机器学习原子间势未充分利用海森矩阵信息的问题，提出两种基于海森矩阵的构象增强方案，实现即插即用集成并提升模型精度。
中文摘要 AI 辅助
尽管机器学习原子间势（MLIPs）已成功学习到势能面（PES）和原子力，但许多实际应用（如振动分析和过渡态搜索）严重依赖PES海森矩阵。然而，标准MLIPs通常仅基于能量和力进行训练，导致海森矩阵信息未被充分利用。同时，现有将海森矩阵显式纳入训练目标的方法需要修改架构，并因高阶反向传播引入显著的计算和内存开销。为解决这些局限，我们提出两种基于海森矩阵的数据增强方案：各向同性高斯位移（UniAug）和正则模式加权位移（ModeAug）。两种方法均采用简单的泰勒展开，在不改变训练目标或扩展自动微分图的情况下实现有效增强，可即插即用式与现有架构和训练流程无缝集成。对非平衡和平衡数据集的综合评估表明，我们的方法提升了模型精度，同时提供了实用的任务特定指南。
英文摘要
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 are trained on energy and forces alone, and existing methods that incorporate the Hessian into training objectives require architectural modifications and incur significant computational and memory overheads from 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 where reference forces are large while providing practical, task-specific guidelines.
