Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
Paper recorded by Signals 4 on 2026-09-04 in cs.LG. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07
Category: cs.LG · 机器学习 · first seen 2026-09-07
Abstract
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
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Cite this page: Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials: the #123 most recent of 215 cs.LG papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/hessian-based-molecular-conformation-augmentation-for-a-scalable-and-efficient-s.html
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