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arXiv Machine Learning By Bumju Kwak, Jeonghee Jo

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

Read the original on arXiv Machine Learning →

The paper introduces two Hessian-based data augmentation techniques—UniAug and ModeAug—to improve machine‑learning interatomic potentials (MLIPs). These methods use simple Taylor expansions to generate augmented configurations without modifying training objectives or increasing computational overhead. Experiments on both non‑equilibrium and equilibrium datasets show that the augmentations enhance model accuracy and provide practical guidelines for specific tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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