fix hyperparemeter for benchmark training and added benchmark results.

training/hyper/hyper_qm9_energies.py

@@ -459,7 +459,7 @@
                 "inputs": [
                     {"shape": (None, 15), "name": "node_attributes", "dtype": "float32", "ragged": True},
                     {"shape": (None, 3), "name": "node_coordinates", "dtype": "float32", "ragged": True},
-                    {"shape": (None, 1), "name": "range_attributes", "dtype": "int64", "ragged": True},
+                    {"shape": (None, 1), "name": "range_attributes", "dtype": "float32", "ragged": True},
                     {"shape": (None, 2), "name": "range_indices", "dtype": "int64", "ragged": True}
                 ],
                 "input_tensor_type": "ragged",

training/hyper/hyper_qm9_orbitals.py

@@ -451,7 +451,7 @@
                 "inputs": [
                     {"shape": (None, 15), "name": "node_attributes", "dtype": "float32", "ragged": True},
                     {"shape": (None, 3), "name": "node_coordinates", "dtype": "float32", "ragged": True},
-                    {"shape": (None, 1), "name": "range_attributes", "dtype": "int64", "ragged": True},
+                    {"shape": (None, 1), "name": "range_attributes", "dtype": "float32", "ragged": True},
                     {"shape": (None, 2), "name": "range_indices", "dtype": "int64", "ragged": True}
                 ],
                 "input_tensor_type": "ragged",

training/results/MatProjectEFormDataset/CGCNN_make_crystal_model/CGCNN_MatProjectEFormDataset_score.yaml

@@ -0,0 +1,157 @@
+OS: posix_linux
+backend: tensorflow
+cuda_available: 'True'
+data_unit: eV/atom
+date_time: '2024-02-12 00:09:57'
+device_id: '[LogicalDevice(name=''/device:CPU:0'', device_type=''CPU''), LogicalDevice(name=''/device:GPU:0'',
+  device_type=''GPU'')]'
+device_memory: '[]'
+device_name: '[{}, {''compute_capability'': (8, 0), ''device_name'': ''NVIDIA A100
+  80GB PCIe''}]'
+epochs:
+- 1000
+- 1000
+- 1000
+- 1000
+- 1000
+execute_folds:
+- 4
+kgcnn_version: 4.0.0
+learning_rate:
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+loss:
+- 0.0026080473326146603
+- 0.0025584164541214705
+- 0.002506435848772526
+- 0.0025094763841480017
+- 0.002450938569381833
+max_learning_rate:
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+- 0.0010000000474974513
+max_loss:
+- 0.2644650340080261
+- 0.2624846398830414
+- 0.2626839280128479
+- 0.269146591424942
+- 0.2654799222946167
+max_scaled_mean_absolute_error:
+- 0.30768436193466187
+- 0.3053379952907562
+- 0.30544695258140564
+- 0.3133404552936554
+- 0.3085813522338867
+max_scaled_root_mean_squared_error:
+- 0.48864248394966125
+- 0.4834892451763153
+- 0.48622262477874756
+- 0.49567335844039917
+- 0.489039808511734
+max_val_loss:
+- 0.07701177150011063
+- 0.07798759639263153
+- 0.07978218048810959
+- 0.07236430048942566
+- 0.07760155946016312
+max_val_scaled_mean_absolute_error:
+- 0.0895666554570198
+- 0.09075972437858582
+- 0.09283629059791565
+- 0.08433927595615387
+- 0.09031086415052414
+max_val_scaled_root_mean_squared_error:
+- 0.16603174805641174
+- 0.1706741750240326
+- 0.17729216814041138
+- 0.15928615629673004
+- 0.17651261389255524
+min_learning_rate:
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+- 1.1979999726463575e-05
+min_loss:
+- 0.0025917908642441034
+- 0.0025584164541214705
+- 0.002499554306268692
+- 0.0024954641703516245
+- 0.0024449648335576057
+min_scaled_mean_absolute_error:
+- 0.00301542691886425
+- 0.002976306015625596
+- 0.002906341338530183
+- 0.0029053455218672752
+- 0.0028418991714715958
+min_scaled_root_mean_squared_error:
+- 0.011370769701898098
+- 0.010693884454667568
+- 0.009863811545073986
+- 0.011626441963016987
+- 0.011576841585338116
+min_val_loss:
+- 0.02578863501548767
+- 0.02525782398879528
+- 0.02570636384189129
+- 0.02563677355647087
+- 0.02539525367319584
+min_val_scaled_mean_absolute_error:
+- 0.030011145398020744
+- 0.029425358399748802
+- 0.029944349080324173
+- 0.02989417500793934
+- 0.029561277478933334
+min_val_scaled_root_mean_squared_error:
+- 0.07147400826215744
+- 0.07076342403888702
+- 0.07674837857484818
+- 0.07776230573654175
+- 0.07215573638677597
+model_class: make_crystal_model
+model_name: CGCNN
+model_version: '2023-11-28'
+multi_target_indices: null
+number_histories: 5
+scaled_mean_absolute_error:
+- 0.0030343900434672832
+- 0.002976306015625596
+- 0.0029143390711396933
+- 0.00292161270044744
+- 0.002848853589966893
+scaled_root_mean_squared_error:
+- 0.011370769701898098
+- 0.010693884454667568
+- 0.00986681692302227
+- 0.01163093838840723
+- 0.011578156612813473
+seed: 42
+time_list:
+- '17:20:18.229154'
+- '17:24:47.711764'
+- '17:27:54.787246'
+- '17:29:50.163244'
+- '17:24:21.128653'
+val_loss:
+- 0.02578863501548767
+- 0.02525782398879528
+- 0.025737645104527473
+- 0.02563677355647087
+- 0.02539525367319584
+val_scaled_mean_absolute_error:
+- 0.030011145398020744
+- 0.029425358399748802
+- 0.029976332560181618
+- 0.02989417500793934
+- 0.029561277478933334
+val_scaled_root_mean_squared_error:
+- 0.07228276133537292
+- 0.07152009755373001
+- 0.07757915556430817
+- 0.07864636182785034
+- 0.07326369732618332

training/results/MatProjectEFormDataset/CGCNN_make_crystal_model/CGCNN_hyper.json

@@ -0,0 +1 @@
+{"model": {"class_name": "make_crystal_model", "module_name": "kgcnn.literature.CGCNN", "config": {"name": "CGCNN", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "node_frac_coordinates", "dtype": "float64", "ragged": true}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64", "ragged": true}, {"shape": [null, 3], "name": "range_image", "dtype": "float32", "ragged": true}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float64", "ragged": false}], "input_tensor_type": "ragged", "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "representation": "unit", "expand_distance": true, "make_distances": true, "gauss_args": {"bins": 60, "distance": 6, "offset": 0.0, "sigma": 0.4}, "conv_layer_args": {"units": 128, "activation_s": "kgcnn>shifted_softplus", "activation_out": "kgcnn>shifted_softplus", "batch_normalization": true}, "node_pooling_args": {"pooling_method": "mean"}, "depth": 4, "output_mlp": {"use_bias": [true, true, false], "units": [128, 64, 1], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus", "linear"]}}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 128, "epochs": 1000, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.001, "learning_rate_stop": 1e-05, "epo_min": 500, "epo": 1000, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.001}}, "loss": "mean_absolute_error"}, "scaler": {"class_name": "StandardLabelScaler", "module_name": "kgcnn.data.transform.scaler.standard", "config": {"with_std": true, "with_mean": true, "copy": true}}, "multi_target_indices": null}, "data": {"data_unit": "eV/atom"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectEFormDataset", "module_name": "kgcnn.data.datasets.MatProjectEFormDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 6.0}}]}}

training/results/README.md

@@ -168,6 +168,7 @@ Materials Project dataset from Matbench with 132752 crystal structures and their
 
 | model                     | kgcnn   |   epochs | MAE [eV/atom]          | RMSE [eV/atom]         |
 |:--------------------------|:--------|---------:|:-----------------------|:-----------------------|
+| CGCNN.make_crystal_model  | 4.0.0   |     1000 | 0.0298 ± 0.0002     | 0.0747 ± 0.0029     |
 | Schnet.make_crystal_model | 4.0.0   |      800 | **0.0211 ± 0.0003** | **0.0510 ± 0.0024** |
 
 #### MatProjectGapDataset