Author

Jeonghee Jo

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Preprint Sep 2026

Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials

Comprehensive evaluations across non-equilibrium and equilibrium datasets demonstrate that the proposed Hessian-derived data augmentation schemes enhances model accuracy where reference forces are large and training data are limited.

Bumju Kwak, Jeonghee Jo · 0 citations

#machine learning Preprint Sep 2026

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 are trained on energy and forces alone, and existi...

Bumju Kwak, Jeonghee Jo · 0 citations