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@@ -46,17 +46,11 @@ calc = SevenNetCalculator('7net-mf-ompa', modal='mpa') # Use modal='omat24' for
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When using the command-line interface of SevenNet, include the `--modal mpa` or `--modal omat24` option to select the desired modality.
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#### **Matbench Discovery**
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| CPS | F1 | $\kappa_{\mathrm{SRME}}$ | RMSD |
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|:---:|:---:|:---:|:---:|
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|**0.845**|**0.901**|0.317|**0.064**|
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**Maximum Atoms Supported**
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~ **1,000 atoms** (Bulk / ASE `SevenNetCalculator` / single 24GB VRAM GPU) \
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※ This number is approximate and may vary depending on your specific GPU memory (VRAM) capacity and execution environment (e.g., calculation with LAMMPS, multi-GPU...).
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[Detailed instructions for multi-fidelity learning](https://github.com/MDIL-SNU/SevenNet/blob/main/sevenn/pretrained_potentials/SevenNet_MF_0/README.md)
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[Download link for fully detailed checkpoint](https://figshare.com/articles/software/7net_MF_ompa/28590722?file=53029859)
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#### **Matbench Discovery**
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* $\kappa_{\mathrm{SRME}}$: **0.221**
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#### **Maximum Atoms Supported**
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~ **1,600 atoms** (Bulk / ASE `SevenNetCalculator` / single 24GB VRAM GPU)
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### **SevenNet-l3i5 (12Dec2024)**
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> Model keywords: `7net-l3i5` | `SevenNet-l3i5`
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|:---:|:---:|:---:|:---:|
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|0.714 |0.760|0.550|0.085|
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#### **Maximum Atoms Supported**
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~ **2,800 atoms** (Bulk / ASE `SevenNetCalculator` / single 24GB VRAM GPU)
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### **SevenNet-0 (11Jul2024)**
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|0.67|0.767|
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#### **Maximum Atoms Supported**
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~ **6,500 atoms** (Bulk / ASE `SevenNetCalculator` / single 24GB VRAM GPU)
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You can find our legacy models in [pretrained_potentials](./sevenn/pretrained_potentials).
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Various pretrained SevenNet models can be accessed by setting the model variable to predefined keywords like `7net-mf-ompa`, `7net-omat`, `7net-l3i5`, and `7net-0`.
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The following table provides **approximate** maximum atom counts of **A100 GPU (80GB)** in a bulk system.
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| Model | Max atoms |
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|:---:|:---:|
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|7net-0|~ 21,500|
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|7net-l3i5|~ 9,300|
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|7net-omat|~ 5,300|
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|7net-mf-ompa|~ 3,300|
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Note that this value depends on the target system. The limitation can be overcome by leveraging multi-GPUs with LAMMPS.
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Additionally, 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.
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