Update HIP

README.md

@@ -1,83 +1,29 @@
-# Molecular Hessians Without Derivatives
+# HIP: Hessian Interatomic Potentials Without Derivatives
 
-See https://github.com/BurgerAndreas/gad-ff
+Code: https://github.com/BurgerAndreas/hip
+Paper: https://arxiv.org/abs/2509.21624
 
 
 ## Available checkpoints
 
-- `hesspred_v1`: Used for the paper. Trained to predict Hessians. Can be used for energies, forces, learned and autograd Hessians.
-- `hesspred_v2`: Potentially better Hessian prediction, less tested. Trained with MAE.
-- `hesspred_v3`: Potentially better Hessian prediction, less tested. Trained for longer.
-- `ckpt/eqv2.ckpt`: HORM EquiformerV2 finetuned on the HORM Hessian dataset. Not trained to predict Hessians! Can be used for energies, forces, and autograd Hessian.
-
+- `hip_v2`: Recommended. Trained end-to-end using `scripts/train.py lr=cos2`
+- `hesspred_v1`: Used for the paper. Finetuned from HORM EquiformerV2 to predict Hessians
 
 ## Use our model
 
-Use our model, that 
-```bash
-# download checkpoints from HuggingFace
-cd gadff/ckpt/
-wget https://huggingface.co/andreasburger/heigen/resolve/main/ckpt/hesspred_v1.ckpt?download=true -O hesspred_v1.ckpt
-```
-
-```python
-import os
-import torch
-from gadff.equiformer_torch_calculator import EquiformerTorchCalculator
-from gadff.equiformer_ase_calculator import EquiformerASECalculator # also try this
-from gadff.inference_utils import get_dataloader
-from gadff.frequency_analysis import analyze_frequencies_torch
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# you might need to change this
-project_root = os.path.dirname(os.path.dirname(__file__))
-checkpoint_path = os.path.join(project_root, "ckpt/hesspred_v1.ckpt")
-calculator = EquiformerTorchCalculator(
-    checkpoint_path=checkpoint_path,
-    hessian_method="predict",
-)
-
-# Example 1: load a dataset file and predict the first batch
-dataset_path = os.path.join(project_root, "data/sample_100.lmdb")
-dataloader = get_dataloader(
-    dataset_path, calculator.potential, batch_size=1, shuffle=False
-)
-batch = next(iter(dataloader))
-results = calculator.predict(batch)
-print("\nExample 1:")
-print(f"  Energy: {results['energy'].shape}")
-print(f"  Forces: {results['forces'].shape}")
-print(f"  Hessian: {results['hessian'].shape}")
-
-print("\nGAD:")
-gad = calculator.get_gad(batch)
-print(f"  GAD: {gad['gad'].shape}")
-
-# Example 2: create a random data object with random positions and predict
-n_atoms = 10
-elements = torch.tensor([1, 6, 7, 8])  # H, C, N, O
-pos = torch.randn(n_atoms, 3)  # (N, 3)
-atomic_nums = elements[torch.randint(0, 4, (n_atoms,))]  # (N,)
-results = calculator.predict(coords=pos, atomic_nums=atomic_nums)
-print("\nExample 2:")
-print(f"  Energy: {results['energy'].shape}")
-print(f"  Forces: {results['forces'].shape}")
-print(f"  Hessian: {results['hessian'].shape}")
-
-print("\nFrequency analysis:")
-hessian = results["hessian"]
-frequency_analysis = analyze_frequencies_torch(hessian, pos, atomic_nums)
-print(f"eigvals: {frequency_analysis['eigvals'].shape}")
-print(f"eigvecs: {frequency_analysis['eigvecs'].shape}")
-print(f"neg_num: {frequency_analysis['neg_num']}")
-print(f"natoms: {frequency_analysis['natoms']}")
-```
+Please have a look at https://github.com/BurgerAndreas/hip
 
 
 ## Citation
 
 ```bibtex
-TODO
+@misc{burger2025hiphessian,
+  title={Shoot from the HIP: Hessian Interatomic Potentials without derivatives}, 
+  author={Andreas Burger and Luca Thiede and Nikolaj Rønne and Varinia Bernales and Nandita Vijaykumar and Tejs Vegge and Arghya Bhowmik and Alan Aspuru-Guzik},
+  year={2025},
+  eprint={2509.21624},
+  archivePrefix={arXiv},
+  primaryClass={cs.LG},
+  url={https://arxiv.org/abs/2509.21624}, 
+}
 ```