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license: cc-by-nc-sa-4.0
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This repository provides trained checkpoints of reactive Machine Learning Interatomic Potentials (MLIPs) developed using the Hessian dataset for Optimizing Reactive MLIP (HORM) — the largest Hessian-labeled quantum chemistry dataset to date. These models are specifically optimized for transition state (TS) characterization and are trained using a Hessian-informed strategy that significantly improves Hessian prediction accuracy and TS search robustness.

| Filename | Model | Training Method |
|----------|-------|----------------|
| alpha_orig.ckpt | AlphaNet | Energy-Force Training |
| alpha.ckpt | AlphaNet | Energy-Force-Hessian Training |
| left_orig.ckpt | LEFTNet | Energy-Force Training |
| left.ckpt | LEFTNet | Energy-Force-Hessian Training |
| left-df_orig.ckpt | LEFTNet-df | Energy-Force Training |
| left-df.ckpt | LEFTNet-df | Energy-Force-Hessian Training |
| eqv2_orig.ckpt | EquiformerV2 | Energy-Force Training |
| eqv2.ckpt | EquiformerV2 | Energy-Force-Hessian Training |