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Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

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:

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