Upload SO3Net-PES-ANI-1x-Subset

README.md

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+# Description
+
+This model is a M3GNet potential for 4 elements including H, C, N, O. It has broad applications in the
+dynamic simulations of organic molecules.
+
+# Training dataset
+
+ANI-1x-Subset: 300K MD simulations and Materials Project ground state calculations.
+- Training set size: 991735
+- Validation set size: 248355
+- Test set size: 248355
+
+# Performance metrics
+
+## Training and validation errors
+
+MAEs of energies, forces and stresses, respectively
+- Training: 2.281 meV/atom, 46 meV/A
+- Validation: 2.286 meV/atom, 46 meV/A
+- Test: 1.596 meV/atom, 35 meV/A
+
+# References
+
+```txt
+Ko, T.W., Deng, B., Nassar, M. et al. Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry. npj Computation Materials 11, 253 (2025).
+```

model.json

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+{
+    "@class": "Potential",
+    "@module": "matgl.apps.pes",
+    "@model_version": 3,
+    "metadata": null,
+    "kwargs": {
+        "model": {
+            "@class": "SO3Net",
+            "@module": "matgl.models._so3net",
+            "@model_version": 0,
+            "init_args": {
+                "element_types": [
+                    "H",
+                    "C",
+                    "N",
+                    "O"
+                ],
+                "units": 64,
+                "dim_state_embedding": 0,
+                "ntypes_state": null,
+                "dim_state_feats": null,
+                "nblocks": 3,
+                "nmax": 5,
+                "cutoff": 5.0,
+                "rbf_learnable": false,
+                "target_property": "atomwise",
+                "task_type": "regression",
+                "readout_type": "weighted_atom",
+                "niters_set2set": 3,
+                "nlayers_set2set": 3,
+                "nlayers_readout": 2,
+                "is_intensive": false,
+                "include_state": false,
+                "use_vector_representation": false,
+                "correct_charges": false,
+                "predict_dipole_magnitude": false,
+                "activation_type": "swish",
+                "ntargets": 1,
+                "return_vector_representation": false,
+                "dim_node_embedding": 64,
+                "lmax": 2
+            }
+        },
+        "data_mean": "tensor(0.)",
+        "data_std": "tensor(3.70301199)",
+        "element_refs": "tensor([  -16.51807976, -1036.37304688, -1488.65246582, -2046.00830078])",
+        "calc_forces": true,
+        "calc_stresses": false,
+        "calc_hessian": false,
+        "calc_magmom": false,
+        "calc_repuls": false,
+        "zbl_trainable": false,
+        "debug_mode": false
+    }
+}

model.pt

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+size 3969478

state.pt

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