MDIL-SNU · alphalm4 · Jul 22, 2026 · Jul 22, 2026
diff --git a/.gitignore b/.gitignore
@@ -16,6 +16,7 @@ sevenn/pretrained_potentials/SevenNet_omat
 sevenn/pretrained_potentials/SevenNet_omni
 sevenn/pretrained_potentials/SevenNet_omni_i8
 sevenn/pretrained_potentials/SevenNet_omni_i12
+sevenn/pretrained_potentials/SevenNet_nano
 
 # from compile
 sevenn/pair_d3*

diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -6,6 +6,11 @@ All notable changes to this project will be documented in this file.
 - Training features used for SevenNet-Omni: batch training, `OrderedSampler`, `grad_clip`, `onecyclelr`
 - Loss: MAE, L2MAE
 - Dataset type: `aselmdb`, `custom`
+- SevenNet-Nano checkpoints for `7net-nano-4.5`, `7net-nano-5.0`, `7net-nano-5.5`, and `7net-nano-6.0`
+
+### Changed
+- The default dtype of the rescale layer is changed to double precision. Use the `SEVENN_SHIFT_SCALE_DTYPE` environment variable for backward single-precision compatibility.
+- The recommended LAMMPS ML-IAP version is bumped to `stable_22Jul2025_update4`.
 
 ### Fixed
 - `D3Calculator()` segfault bug when reusing the calculator within different sized `Atoms`.

diff --git a/README.md b/README.md
@@ -73,6 +73,17 @@ If you utilize the pretrained model SevenNet-Omni or multi-task training strateg
 }
 ```
 
+If you utilize the pretrained model SevenNet-Nano, please cite the following paper:
+```bib
+@article{oh_lightweight_2026,
+	title = {A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations},
+	doi = {10.1021/acs.jcim.6c01103},
+	journal = {J. Chem. Inf. Model.},
+	author = {Oh, Sangmin and You, Jinmu and Kim, Jaesun and Lee, Jiho and An, Hyungmin and Han, Seungwu and Kang, Youngho},
+	year = {2026},
+}
+```
+
 If you utilize the reEWC forgetting-aware fine-tuning strategy for continual learning of pretrained universal machine-learning interatomic potentials, please cite the following paper:
 ```bib
 @article{kim_efficient_2026,

diff --git a/docs/source/cite.md b/docs/source/cite.md
@@ -40,3 +40,14 @@ If you utilize the pretrained model SevenNet-Omni or multi-task training strateg
 	year = {2026},
 }
 ```
+
+If you utilize the pretrained model SevenNet-Nano, please cite the following paper:
+```bib
+@article{oh_lightweight_2026,
+	title = {A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations},
+	doi = {10.1021/acs.jcim.6c01103},
+	journal = {J. Chem. Inf. Model.},
+	author = {Oh, Sangmin and You, Jinmu and Kim, Jaesun and Lee, Jiho and An, Hyungmin and Han, Seungwu and Kang, Youngho},
+	year = {2026},
+}
+```
diff --git a/docs/source/user_guide/ase_calculator.md b/docs/source/user_guide/ase_calculator.md
@@ -69,7 +69,7 @@ calc = SevenNetCalculator(model='7net-0', enable_cueq=True) # or enable_flash=Tr
 
 If you encounter the error `CUDA is not installed or nvcc is not available`, please ensure the `nvcc` compiler is available. Currently, CPU + D3 is not supported.
 
-Various pretrained SevenNet models can be accessed by setting the model variable to predefined keywords like `7net-omni`, `7net-mf-ompa`, `7net-omat`, `7net-l3i5`, and `7net-0`.
+Various pretrained SevenNet models can be accessed by setting the model variable to predefined keywords like `7net-omni`, `7net-nano-5.5`, `7net-mf-ompa`, `7net-omat`, `7net-l3i5`, and `7net-0`.
 
 User-trained models can be applied with the ASE calculator. In this case, the `model` parameter should be set to the checkpoint path from training.
 

diff --git a/docs/source/user_guide/pretrained.md b/docs/source/user_guide/pretrained.md
@@ -42,6 +42,12 @@ Multiple inference tasks are available for multi-fidelity architecture models, S
   - Multi-task
   - `mpa` (PBE+U) <br> `matpes_r2scan` (r²SCAN) <br> `omol25_low` (ωB97M-V) <br> and 10 more tasks
 
+* - [SevenNet-Nano](#sevennet-nano)
+  - Lightweight model distilled from the SevenNet-Omni `mpa` task
+  - $l_{\mathrm{max}}=2$ <br> $N_{\mathrm{layer}}=3$ <br> parity=full <br> cutoff=4.5, 5.0, 5.5, or 6.0 Å
+  - Single-task
+  - N/A
+
 * - [SevenNet-MF-ompa](#sevennet-mf-ompa)
   - MPtrj, sAlex, OMat24
   - $l_{\mathrm{max}}=3$ <br> $N_{\mathrm{layer}}=5$ <br> parity=full
@@ -146,6 +152,29 @@ SevenNet-Omni-i12 follows the same training strategy as [SevenNet-Omni](#sevenne
 |:---:|:---:|:---:|:---:|
 |**0.873**|0.906|0.192|0.0617|
 
+---
+## SevenNet-Nano
+> Model keywords: `7net-nano-4.5` | `7net-nano-5.0` | `7net-nano-5.5` | `7net-nano-6.0`
+
+SevenNet-Nano is a lightweight pretrained model released with [A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations](https://doi.org/10.1021/acs.jcim.6c01103). It is provided as four single-task checkpoint variants with different radial cutoffs. Distilled from the SevenNet-Omni `mpa` task, SevenNet-Nano compresses broad cross-domain chemical knowledge into a 105k-parameter model, achieving over an order-of-magnitude speedup over its teacher while retaining balanced accuracy across diverse atomistic environments.
+
+Choose the cutoff explicitly through the model keyword:
+
+```python
+from sevenn.calculator import SevenNetCalculator
+calc = SevenNetCalculator('7net-nano-5.5')
+```
+
+```{table} SevenNet-Nano cutoff variants
+:widths: 40 60
+| Model keyword | Cutoff |
+|---------------|--------|
+| `7net-nano-4.5` | 4.5 Å |
+| `7net-nano-5.0` | 5.0 Å |
+| `7net-nano-5.5` | 5.5 Å |
+| `7net-nano-6.0` | 6.0 Å |
+```
+
 ---
 ## SevenNet-MF-ompa
 > Model keywords: `7net-mf-ompa` | `SevenNet-mf-ompa`

diff --git a/sevenn/_const.py b/sevenn/_const.py
@@ -66,6 +66,10 @@
 SEVENNET_omni = f'{_prefix}/SevenNet_omni/checkpoint_sevennet_omni.pth'
 SEVENNET_omni_i8 = f'{_prefix}/SevenNet_omni_i8/checkpoint_sevennet_omni_i8.pth'
 SEVENNET_omni_i12 = f'{_prefix}/SevenNet_omni_i12/checkpoint_sevennet_omni_i12.pth'
+SEVENNET_nano_4_5 = f'{_prefix}/SevenNet_nano/checkpoint_7net_nano_4.5.pth'
+SEVENNET_nano_5_0 = f'{_prefix}/SevenNet_nano/checkpoint_7net_nano_5.0.pth'
+SEVENNET_nano_5_5 = f'{_prefix}/SevenNet_nano/checkpoint_7net_nano_5.5.pth'
+SEVENNET_nano_6_0 = f'{_prefix}/SevenNet_nano/checkpoint_7net_nano_6.0.pth'
 
 _git_prefix = 'https://github.com/MDIL-SNU/SevenNet/releases/download'
 CHECKPOINT_DOWNLOAD_LINKS = {
@@ -74,6 +78,10 @@
     SEVENNET_omni: f'{_git_prefix}/v0.12.0.cp/checkpoint_sevennet_omni.pth',
     SEVENNET_omni_i8: f'{_git_prefix}/v0.12.1.cp/checkpoint_sevennet_omni_i8.pth',
     SEVENNET_omni_i12: f'{_git_prefix}/v0.12.1.cp/checkpoint_sevennet_omni_i12.pth',
+    SEVENNET_nano_4_5: f'{_git_prefix}/v0.13.1.cp/checkpoint_7net_nano_4.5.pth',
+    SEVENNET_nano_5_0: f'{_git_prefix}/v0.13.1.cp/checkpoint_7net_nano_5.0.pth',
+    SEVENNET_nano_5_5: f'{_git_prefix}/v0.13.1.cp/checkpoint_7net_nano_5.5.pth',
+    SEVENNET_nano_6_0: f'{_git_prefix}/v0.13.1.cp/checkpoint_7net_nano_6.0.pth',
 }
 # to avoid torch script to compile torch_geometry.data
 AtomGraphDataType = Dict[str, torch.Tensor]  # But it can contain 'lmp_data'

diff --git a/sevenn/calculator.py b/sevenn/calculator.py
@@ -44,8 +44,9 @@ def __init__(
         Parameters
         ----------
         model: str | Path | AtomGraphSequential, default='7net-0'
-            Name of pretrained models (7net-omni, 7net-mf-ompa, 7net-omat, 7net-l3i5,
-            7net-0) or path to the checkpoint, deployed model or the model itself
+            Name of pretrained models (7net-omni, 7net-nano-5.5, 7net-mf-ompa,
+            7net-omat, 7net-l3i5, 7net-0) or path to the checkpoint, deployed model
+            or the model itself
         file_type: str, default='checkpoint'
             one of 'checkpoint' | 'model_instance'
         device: str | torch.device, default='auto'
@@ -255,8 +256,9 @@ def __init__(
         Parameters
         ----------
         model: str | Path | AtomGraphSequential
-            Name of pretrained models (7net-omni, 7net-mf-ompa, 7net-omat, 7net-l3i5,
-            7net-0) or path to the checkpoint, deployed model or the model itself
+            Name of pretrained models (7net-omni, 7net-nano-5.5, 7net-mf-ompa,
+            7net-omat, 7net-l3i5, 7net-0) or path to the checkpoint, deployed model
+            or the model itself
         file_type: str, default='checkpoint'
             one of 'checkpoint' | 'model_instance'
         device: str | torch.device, default='auto'

diff --git a/sevenn/main/sevenn_get_model.py b/sevenn/main/sevenn_get_model.py
@@ -7,8 +7,8 @@
     'deploy LAMMPS model from the checkpoint'
 )
 checkpoint_help = (
-    'Pretrained model name (7net-omni, 7net-omni-i8, 7net-omni-i12, etc.) '
-    'or path to checkpoint file. See documentation for all available models.'
+    'Pretrained model name (7net-omni, 7net-nano-5.5, etc.) or path to '
+    'checkpoint file. See documentation for all available models.'
 )
 output_name_help = 'filename prefix'
 get_parallel_help = 'deploy parallel model'

diff --git a/sevenn/util.py b/sevenn/util.py
@@ -289,6 +289,14 @@ def pretrained_name_to_path(name: str) -> str:
         checkpoint_path = _const.SEVENNET_omni_i8
     elif name in [f'{n}-omni-i12' for n in heads]:
         checkpoint_path = _const.SEVENNET_omni_i12
+    elif name in [f'{n}-nano-4.5' for n in heads]:
+        checkpoint_path = _const.SEVENNET_nano_4_5
+    elif name in [f'{n}-nano-5.0' for n in heads]:
+        checkpoint_path = _const.SEVENNET_nano_5_0
+    elif name in [f'{n}-nano-5.5' for n in heads]:
+        checkpoint_path = _const.SEVENNET_nano_5_5
+    elif name in [f'{n}-nano-6.0' for n in heads]:
+        checkpoint_path = _const.SEVENNET_nano_6_0
     else:
         raise ValueError('Not a valid pretrained model name')
     url = _const.CHECKPOINT_DOWNLOAD_LINKS.get(checkpoint_path)
@@ -331,6 +339,10 @@ def get_available_pretrained_models() -> List[str]:
         'SEVENNET_omni': '7net-omni',
         'SEVENNET_omni_i8': '7net-omni-i8',
         'SEVENNET_omni_i12': '7net-omni-i12',
+        'SEVENNET_nano_4_5': '7net-nano-4.5',
+        'SEVENNET_nano_5_0': '7net-nano-5.0',
+        'SEVENNET_nano_5_5': '7net-nano-5.5',
+        'SEVENNET_nano_6_0': '7net-nano-6.0',
     }
 
     models = []

diff --git a/tests/unit_tests/test_pretrained.py b/tests/unit_tests/test_pretrained.py
@@ -373,14 +373,14 @@ def test_7net_omni_mpa(atoms_pbc, atoms_mol):
     )
     g1_ref_s = -1 * torch.tensor(
         # xx, yy, zz, xy, yz, zx
-        [-0.6500675, -0.0290563, -0.0290563 , 0.02576996, 0.00374571, 0.02576996]
+        [-0.6500675, -0.0290563, -0.0290563, 0.02576996, 0.00374571, 0.02576996]
     )
 
     g2_ref_e = torch.tensor([-12.918253898620605])
     g2_ref_f = torch.tensor(
         [
             [0.0, -13.32638, 7.1434574],
-            [0.0, 9.442289 , -9.77207],
+            [0.0, 9.442289, -9.77207],
             [0.0, 3.8840904, 2.6286132],
         ]
     )
@@ -486,3 +486,137 @@ def test_7net_omni_i12_mpa(atoms_pbc, atoms_mol):
 
     assert acl(g2.inferred_total_energy, g2_ref_e)
     assert acl(g2.inferred_force, g2_ref_f)
+
+
+@pytest.mark.parametrize(
+    'name, cutoff_ref, g1_ref_e, g1_ref_f, g1_ref_s, g2_ref_e, g2_ref_f',
+    [
+        (
+            '7net-nano-4.5',
+            4.5,
+            -3.5564712945510024,
+            [
+                [11.843839645385742, -0.06617936491966248, -0.06617937982082367],
+                [-11.843839645385742, 0.06617936491966248, 0.0661793053150177],
+            ],
+            [
+                0.605344831943512,
+                0.02144481986761093,
+                0.021444812417030334,
+                -0.03438035026192665,
+                -0.003832500660791993,
+                -0.03438035771250725,
+            ],
+            -12.919640449266296,
+            [
+                [0.0, -13.341249465942383, 7.200963020324707],
+                [0.0, 9.452506065368652, -9.814258575439453],
+                [0.0, 3.8887438774108887, 2.6132965087890625],
+            ],
+        ),
+        (
+            '7net-nano-5.0',
+            5.0,
+            -3.4316864360099615,
+            [
+                [12.497757911682129, -0.009577874094247818, -0.009577933698892593],
+                [-12.497756958007812, 0.009577878750860691, 0.009577957913279533],
+            ],
+            [
+                0.6648563146591187,
+                0.040433332324028015,
+                0.04043332114815712,
+                -0.029892124235630035,
+                -0.003740792628377676,
+                -0.029892126098275185,
+            ],
+            -12.910758047958524,
+            [
+                [0.0, -13.562267303466797, 7.167722702026367],
+                [0.0, 9.584957122802734, -9.880509376525879],
+                [0.0, 3.9773099422454834, 2.7127861976623535],
+            ],
+        ),
+        (
+            '7net-nano-5.5',
+            5.5,
+            -3.532371955806397,
+            [
+                [12.454666137695312, -0.034893378615379333, -0.0348934531211853],
+                [-12.454666137695312, 0.034893378615379333, 0.03489343076944351],
+            ],
+            [
+                0.6540243029594421,
+                0.021435830742120743,
+                0.021435843780636787,
+                -0.03487098217010498,
+                -0.00511842779815197,
+                -0.03487098589539528,
+            ],
+            -12.922036098234884,
+            [
+                [0.0, -13.198136329650879, 7.108638763427734],
+                [0.0, 9.389389038085938, -9.699457168579102],
+                [0.0, 3.8087470531463623, 2.5908186435699463],
+            ],
+        ),
+        (
+            '7net-nano-6.0',
+            6.0,
+            -3.484466470155766,
+            [
+                [12.169546127319336, -0.02828906662762165, -0.028289003297686577],
+                [-12.169546127319336, 0.028289061039686203, 0.02828901819884777],
+            ],
+            [
+                0.6406453251838684,
+                0.026448842138051987,
+                0.026448845863342285,
+                -0.026171253994107246,
+                -0.003374285064637661,
+                -0.026171250268816948,
+            ],
+            -12.934674727250979,
+            [
+                [0.0, -13.23235034942627, 7.185378074645996],
+                [0.0, 9.451717376708984, -9.761429786682129],
+                [0.0, 3.780632972717285, 2.576051712036133],
+            ],
+        ),
+    ],
+    ids=['7net-nano-4.5', '7net-nano-5.0', '7net-nano-5.5', '7net-nano-6.0'],
+)
+def test_7net_nano(
+    name,
+    cutoff_ref,
+    g1_ref_e,
+    g1_ref_f,
+    g1_ref_s,
+    g2_ref_e,
+    g2_ref_f,
+    atoms_pbc,
+    atoms_mol,
+):
+    cp_path = pretrained_name_to_path(name)
+    cp_dict = torch.load(cp_path, map_location='cpu', weights_only=False)
+    assert sorted(cp_dict) == ['config', 'hash', 'model_state_dict', 'time']
+
+    model, config = model_from_checkpoint(
+        cp_path, enable_flash=False, enable_cueq=False
+    )
+    cutoff = config['cutoff']
+    assert cutoff == cutoff_ref
+
+    g1 = AtomGraphData.from_numpy_dict(unlabeled_atoms_to_graph(atoms_pbc, cutoff))
+    g2 = AtomGraphData.from_numpy_dict(unlabeled_atoms_to_graph(atoms_mol, cutoff))
+
+    model.set_is_batch_data(False)
+    g1 = model(g1)
+    g2 = model(g2)
+
+    assert acl(g1.inferred_total_energy, torch.tensor(g1_ref_e))
+    assert acl(g1.inferred_force, torch.tensor(g1_ref_f))
+    assert acl(g1.inferred_stress, torch.tensor(g1_ref_s))
+
+    assert acl(g2.inferred_total_energy, torch.tensor(g2_ref_e))
+    assert acl(g2.inferred_force, torch.tensor(g2_ref_f))