基于Hessian的分子构型增强方法
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
新方法解决了MLIPs训练中Hessian信息利用不足的问题，无需架构修改就能提升模型精度。
研究人员提出两种Hessian衍生数据增强方案：各向同性高斯位移(UniAug)和正态模式加权位移(ModeAug)。这些方法利用简单泰勒展开实现有效增强，无需修改训练目标或扩展自动微分图。在非平衡和平衡数据集上的综合评估显示，该方法能提升模型准确性并提供实用的任务特定指导。
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
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.
