arXiv cs.LGPaper
Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials
The problem is real: MLIPs trained on energy and forces leave Hessian information on the table. This solution is elegant because it adds augmentation without architectural changes or memory overhead. If you're training models for molecular dynamics or chemistry simulations, this is a useful plug-in. For general ML, it's domain-specific innovation.