[2603.25373] Hessian-informed machine learning interatomic potential towards bridging theory and experiments

Computer Science > Machine Learning arXiv:2603.25373 (cs) [Submitted on 26 Mar 2026] Title:Hessian-informed machine learning interatomic potential towards bridging theory and experiments Authors:Bangchen Yin, Jian Ouyang, Zhen Fan, Kailai Lin, Hanshi Hu, Dingshun Lv, Weiluo Ren, Hai Xiao, Ji Chen, Changsu Cao View a PDF of the paper titled Hessian-informed machine learning interatomic potential towards bridging theory and experiments, by Bangchen Yin and 9 other authors View PDF HTML (experimental) Abstract:Local curvature of potential energy surfaces is critical for predicting certain experimental observables of molecules and materials from first principles, yet it remains far beyond reach for complex systems. In this work, we introduce a Hessian-informed Machine Learning Interatomic Potential (Hi-MLIP) that captures such curvature reliably, thereby enabling accurate analysis of associated thermodynamic and kinetic phenomena. To make Hessian supervision practically viable, we develop a highly efficient training protocol, termed Hessian INformed Training (HINT), achieving two to four orders of magnitude reduction for the requirement of expensive Hessian labels. HINT integrates critical techniques, including Hessian pre-training, configuration sampling, curriculum learning and stochastic projection Hessian loss. Enabled by HINT, Hi-MLIP significantly improves transition-state search and brings Gibbs free-energy predictions close to chemical accuracy especially in data-scarc...