A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials

Transition-state (TS) characterization underpins reaction modeling but conventional DFT is costly. Machine-learning interatomic potentials (MLIPs) promise quantum-level accuracy at lower cost, yet, lacking large-scale Hessian data, most are pretrained only on energies and forces, limiting TS optimization. We present HORM, the largest quantum-chemistry Hessian dataset for reactive systems: 1.84 million matrices at the ωB97x/6-31G(d) level. To exploit second-order information efficiently, we...