Upload QET-MatQ-PES

README.md

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+---
+library_name: matgl
+tags:
+- matgl
+- materials-science
+- graph-neural-network
+---
+
+# Potential
+
+This is a [matgl](https://github.com/materialsvirtuallab/matgl) `Potential` model (version 3).
+
+## Usage
+
+```python
+import matgl
+
+model = matgl.load_model("<owner>/<repo>")
+```

model.json

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+{
+    "@class": "Potential",
+    "@module": "matgl.apps._pes_dgl",
+    "@model_version": 3,
+    "metadata": null,
+    "kwargs": {
+        "model": {
+            "@class": "QET",
+            "@module": "matgl.models._qet_dgl",
+            "@model_version": 1,
+            "init_args": {
+                "element_types": [
+                    "H",
+                    "He",
+                    "Li",
+                    "Be",
+                    "B",
+                    "C",
+                    "N",
+                    "O",
+                    "F",
+                    "Ne",
+                    "Na",
+                    "Mg",
+                    "Al",
+                    "Si",
+                    "P",
+                    "S",
+                    "Cl",
+                    "Ar",
+                    "K",
+                    "Ca",
+                    "Sc",
+                    "Ti",
+                    "V",
+                    "Cr",
+                    "Mn",
+                    "Fe",
+                    "Co",
+                    "Ni",
+                    "Cu",
+                    "Zn",
+                    "Ga",
+                    "Ge",
+                    "As",
+                    "Se",
+                    "Br",
+                    "Kr",
+                    "Rb",
+                    "Sr",
+                    "Y",
+                    "Zr",
+                    "Nb",
+                    "Mo",
+                    "Tc",
+                    "Ru",
+                    "Rh",
+                    "Pd",
+                    "Ag",
+                    "Cd",
+                    "In",
+                    "Sn",
+                    "Sb",
+                    "Te",
+                    "I",
+                    "Xe",
+                    "Cs",
+                    "Ba",
+                    "La",
+                    "Ce",
+                    "Pr",
+                    "Nd",
+                    "Pm",
+                    "Sm",
+                    "Eu",
+                    "Gd",
+                    "Tb",
+                    "Dy",
+                    "Ho",
+                    "Er",
+                    "Tm",
+                    "Yb",
+                    "Lu",
+                    "Hf",
+                    "Ta",
+                    "W",
+                    "Re",
+                    "Os",
+                    "Ir",
+                    "Pt",
+                    "Au",
+                    "Hg",
+                    "Tl",
+                    "Pb",
+                    "Bi",
+                    "Ac",
+                    "Th",
+                    "Pa",
+                    "U",
+                    "Np",
+                    "Pu"
+                ],
+                "units": 64,
+                "ntypes_state": null,
+                "dim_state_embedding": 0,
+                "dim_state_feats": null,
+                "include_state": false,
+                "nblocks": 3,
+                "num_rbf": 32,
+                "max_n": 9,
+                "max_l": 3,
+                "rbf_type": "SphericalBessel",
+                "use_smooth": true,
+                "activation_type": "swish",
+                "cutoff": 5.0,
+                "equivariance_invariance_group": "O(3)",
+                "dtype": "torch.float32",
+                "width": 0.5,
+                "readout_type": "weighted_atom",
+                "task_type": "regression",
+                "niters_set2set": 3,
+                "nlayers_set2set": 3,
+                "field": "node_feat",
+                "is_intensive": false,
+                "ntargets": 1,
+                "is_sigma_train": false,
+                "is_hardness_envs": true,
+                "include_magmom": false,
+                "return_features": false
+            }
+        },
+        "data_mean": "tensor(0.)",
+        "data_std": "tensor(1.6909)",
+        "element_refs": "tensor([-3.5281e+00, -8.7808e-11, -1.9537e+00, -2.9913e+00, -6.0278e+00,\n        -7.4050e+00, -7.7867e+00, -6.8388e+00, -4.6101e+00, -1.6128e-10,\n        -1.0529e+00, -1.3377e+00, -3.7258e+00, -5.2365e+00, -5.2901e+00,\n        -5.1271e+00, -2.8906e+00, -2.5143e-10, -7.7694e-01, -2.2972e+00,\n        -6.4618e+00, -7.8848e+00, -8.4645e+00, -8.1836e+00, -8.1447e+00,\n        -7.4940e+00, -6.4755e+00, -5.2968e+00, -3.1625e+00, -8.3700e-01,\n        -2.9360e+00, -4.4327e+00, -4.6906e+00, -4.2722e+00, -2.3356e+00,\n         5.3632e+00, -9.0118e-01, -1.9868e+00, -6.6585e+00, -8.5336e+00,\n        -9.2253e+00, -9.5702e+00, -8.9996e+00, -8.3030e+00, -7.0351e+00,\n        -5.2814e+00, -2.1045e+00, -3.8729e-01, -2.3067e+00, -3.8149e+00,\n        -4.0640e+00, -3.5171e+00, -1.7413e+00,  6.8076e+00, -1.0198e+00,\n        -2.3815e+00, -5.5347e+00, -6.8453e+00, -5.3180e+00, -5.4453e+00,\n        -5.3153e+00, -5.4808e+00, -1.2583e+01, -1.3186e+01, -5.0442e+00,\n        -5.2770e+00, -5.3792e+00, -5.2804e+00, -5.0316e+00, -5.3479e+00,\n        -5.1000e+00, -9.6677e+00, -1.0618e+01, -1.0603e+01, -1.0458e+01,\n        -9.4550e+00, -8.2867e+00, -6.0862e+00, -3.2633e+00,  2.3314e-01,\n        -1.8712e+00, -3.1449e+00, -3.5810e+00, -5.0483e+00, -8.2002e+00,\n        -1.0433e+01, -1.1942e+01, -1.3211e+01, -1.4421e+01])",
+        "calc_forces": true,
+        "calc_stresses": true,
+        "calc_hessian": false,
+        "calc_magmom": false,
+        "calc_charge": true,
+        "calc_repuls": false,
+        "zbl_trainable": false,
+        "debug_mode": false
+    }
+}

model.pt

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state.pt

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