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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. |
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| | Filename | Model | Training Method | |
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| | 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 | |
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