update for keras 3.0

kgcnn/literature/PAiNN/_layers.py

@@ -263,30 +263,31 @@ def call(self, inputs, **kwargs):
             inputs = ops.repeat(inputs, self.units, axis=-1)
 
         if self.method == "zeros":
-            out = ops.zeros_like(inputs)
+            out = ops.zeros_like(inputs, dtype=self.dtype)
             out = ops.expand_dims(out, axis=1)
             out = ops.repeat(out, self.dim, axis=1)
         elif self.method == "eps":
-            out = ops.zeros_like(inputs) + ks.backend.epsilon()
+            out = ops.zeros_like(inputs, dtype=self.dtype) + ks.backend.epsilon()
             out = ops.expand_dims(out, axis=1)
             out = ops.repeat(out, self.dim, axis=1)
         elif self.method == "normal":
             raise NotImplementedError()
         elif self.method == "ones":
-            out = ops.ones_like(inputs)
+            out = ops.ones_like(inputs, dtype=self.dtype)
             out = ops.expand_dims(out, axis=1)
             out = ops.repeat(out, self.dim, axis=1)
         elif self.method == "eye":
-            out = ops.eye(self.dim, ops.shape(inputs)[1], dtype=inputs.dtype)
+            out = ops.eye(self.dim, ops.shape(inputs)[1], dtype=self.dtype)
             out = ops.expand_dims(out, axis=0)
             out = ops.repeat(out, ops.shape(inputs)[0], axis=0)
         elif self.method == "const":
-            out = ops.ones_like(inputs)*self.value
+            out = ops.ones_like(inputs, dtype=self.dtype)*self.value
             out = ops.expand_dims(out, axis=1)
             out = ops.repeat(out, self.dim, axis=1)
         elif self.method == "node":
             out = ops.expand_dims(inputs, axis=1)
             out = ops.repeat(out, self.dim, axis=1)
+            out = ops.cast(out, dtype=self.dtype)
         else:
             raise ValueError("Unknown initialization method %s" % self.method)
         return out

training/hyper/hyper_md17.py

@@ -92,4 +92,96 @@
             "kgcnn_version": "4.0.0"
         }
     },
+    "PAiNN.EnergyForceModel": {
+        "model": {
+            "class_name": "EnergyForceModel",
+            "module_name": "kgcnn.models.force",
+            "config": {
+                "name": "PAiNN",
+                "nested_model_config": True,
+                "output_to_tensor": False,
+                "output_squeeze_states": True,
+                "coordinate_input": 1,
+                "inputs": [
+                    {"shape": [None], "name": "atomic_number", "dtype": "int32"},
+                    {"shape": [None, 3], "name": "node_coordinates", "dtype": "float32"},
+                    {"shape": [None, 2], "name": "range_indices", "dtype": "int64"},
+                    {"shape": (), "name": "total_nodes", "dtype": "int64"},
+                    {"shape": (), "name": "total_ranges", "dtype": "int64"}
+                ],
+                "model_energy": {
+                    "class_name": "make_model",
+                    "module_name": "kgcnn.literature.PAiNN",
+                    "config": {
+                        "name": "PAiNNEnergy",
+                        "inputs": [
+                            {"shape": [None], "name": "atomic_number", "dtype": "int32"},
+                            {"shape": [None, 3], "name": "node_coordinates", "dtype": "float32"},
+                            {"shape": [None, 2], "name": "range_indices", "dtype": "int64"},
+                            {"shape": (), "name": "total_nodes", "dtype": "int64"},
+                            {"shape": (), "name": "total_ranges", "dtype": "int64"}
+                        ],
+                        "input_embedding": None,
+                        "input_node_embedding": {"input_dim": 95, "output_dim": 128},
+                        "equiv_initialize_kwargs": {"dim": 3, "method": "eps"},
+                        "bessel_basis": {"num_radial": 20, "cutoff": 5.0, "envelope_exponent": 5},
+                        "pooling_args": {"pooling_method": "scatter_sum"},
+                        "conv_args": {"units": 128, "cutoff": None},
+                        "update_args": {"units": 128}, "depth": 3, "verbose": 10,
+                        "output_embedding": "graph",
+                        "output_mlp": {"use_bias": [True, True], "units": [128, 1], "activation": ["swish", "linear"]},
+                    }
+                },
+                "outputs": {"energy": {"name": "energy", "shape": (1,)},
+                            "force": {"name": "force", "shape": (None, 3)}}
+            }
+        },
+        "training": {
+            "fit": {
+                "batch_size": 32, "epochs": 1000, "validation_freq": 1, "verbose": 2,
+                "callbacks": []
+            },
+            "compile": {
+                "optimizer": {
+                    "class_name": "Adam", "config": {
+                        "learning_rate": {
+                            "class_name": "kgcnn>LinearWarmupExponentialDecay", "config": {
+                                "learning_rate": 0.001, "warmup_steps": 150.0, "decay_steps": 20000.0,
+                                "decay_rate": 0.01
+                            }
+                        }, "amsgrad": True, "use_ema": True
+                    }
+                },
+                "loss_weights": {"energy": 0.02, "force": 0.98}
+            },
+            "scaler": {"class_name": "EnergyForceExtensiveLabelScaler",
+                       "config": {"standardize_scale": True}},
+        },
+        "data": {
+        },
+        "dataset": {
+            "class_name": "MD17Dataset",
+            "module_name": "kgcnn.data.datasets.MD17Dataset",
+            "config": {
+                # toluene_ccsd_t, aspirin_ccsd, malonaldehyde_ccsd_t, benzene_ccsd_t, ethanol_ccsd_t
+                "trajectory_name": trajectory_name
+            },
+            "methods": [
+                {"rename_property_on_graphs": {"old_property_name": "E", "new_property_name": "energy"}},
+                {"rename_property_on_graphs": {"old_property_name": "F", "new_property_name": "force"}},
+                {"rename_property_on_graphs": {"old_property_name": "z", "new_property_name": "atomic_number"}},
+                {"rename_property_on_graphs": {"old_property_name": "R", "new_property_name": "node_coordinates"}},
+                {"map_list": {"method": "set_range", "max_distance": 5, "max_neighbours": 10000,
+                              "node_coordinates": "node_coordinates"}},
+                {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges",
+                              "count_edges": "range_indices", "count_nodes": "atomic_number",
+                              "total_nodes": "total_nodes"}},
+            ]
+        },
+        "info": {
+            "postfix": "",
+            "postfix_file": "_"+trajectory_name,
+            "kgcnn_version": "4.0.0"
+        }
+    },
 }

training/results/MatProjectGapDataset/Schnet_make_crystal_model/Schnet_MatProjectGapDataset_score.yaml

@@ -0,0 +1,155 @@
+OS: posix_linux
+backend: torch
+cuda_available: 'True'
+data_unit: eV
+date_time: '2023-11-17 13:52:35'
+device_id: '[0]'
+device_memory: '[{''allocated'': 0.0, ''cached'': 3.3}]'
+device_name: '[''NVIDIA A100 80GB PCIe'']'
+epochs:
+- 800
+- 800
+- 800
+- 800
+- 800
+execute_folds:
+- 4
+kgcnn_version: 4.0.0
+learning_rate:
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+loss:
+- 0.7510426640510559
+- 0.7514869570732117
+- 0.0296995397657156
+- 0.09922590851783752
+- 0.752254843711853
+max_learning_rate:
+- 0.0005000000237487257
+- 0.0005000000237487257
+- 0.0005000000237487257
+- 0.0005000000237487257
+- 0.0005000000237487257
+max_loss:
+- 152.89744567871094
+- 2.6082866191864014
+- 1.3898086547851562
+- 43.36865234375
+- 70.06098175048828
+max_scaled_mean_absolute_error:
+- 244.31700134277344
+- 4.172955513000488
+- 2.223109722137451
+- 69.44451141357422
+- 112.0015640258789
+max_scaled_root_mean_squared_error:
+- 25650.35546875
+- 865.424072265625
+- 533.6707153320312
+- 19664.10546875
+- 20820.58203125
+max_val_loss:
+- 0.7587342262268066
+- 1.2743823528289795
+- 0.7526670098304749
+- 0.7512235045433044
+- 2.563725709915161
+max_val_scaled_mean_absolute_error:
+- 1.210221767425537
+- 2.039233446121216
+- 1.20370614528656
+- 1.2025171518325806
+- 4.099420070648193
+max_val_scaled_root_mean_squared_error:
+- 1.9370406866073608
+- 110.60933685302734
+- 1.8700799942016602
+- 1.881477952003479
+- 422.8948669433594
+min_learning_rate:
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+- 1.06999996205559e-05
+min_loss:
+- 0.1837150901556015
+- 0.17997939884662628
+- 0.0296995397657156
+- 0.09922590851783752
+- 0.17990800738334656
+min_scaled_mean_absolute_error:
+- 0.2933097779750824
+- 0.28793007135391235
+- 0.04748707264661789
+- 0.15884126722812653
+- 0.2875083088874817
+min_scaled_root_mean_squared_error:
+- 0.6002561450004578
+- 0.5936135053634644
+- 0.204745814204216
+- 0.3796391487121582
+- 0.593903124332428
+min_val_loss:
+- 0.20087158679962158
+- 0.19898207485675812
+- 0.1612144559621811
+- 0.174364373087883
+- 0.198722705245018
+min_val_scaled_mean_absolute_error:
+- 0.32050538063049316
+- 0.31839296221733093
+- 0.25761139392852783
+- 0.2782728374004364
+- 0.31677064299583435
+min_val_scaled_root_mean_squared_error:
+- 0.6462162733078003
+- 0.6535683274269104
+- 0.5569960474967957
+- 0.5836672186851501
+- 0.6555573344230652
+model_class: make_crystal_model
+model_name: Schnet
+model_version: '2023-09-07'
+multi_target_indices: null
+number_histories: 5
+scaled_mean_absolute_error:
+- 1.1996724605560303
+- 1.201776385307312
+- 0.04748707264661789
+- 0.15884126722812653
+- 1.202290654182434
+scaled_root_mean_squared_error:
+- 1.8552837371826172
+- 1.8511769771575928
+- 0.204745814204216
+- 0.38051795959472656
+- 1.8522521257400513
+seed: 42
+time_list:
+- '17:33:58.401059'
+- '17:09:57.241122'
+- '16:43:25.549148'
+- '16:16:21.689500'
+- '16:59:59.434099'
+val_loss:
+- 0.756482720375061
+- 0.7494837641716003
+- 0.16426515579223633
+- 0.17509213089942932
+- 1.9805660247802734
+val_scaled_mean_absolute_error:
+- 1.2068122625350952
+- 1.19844651222229
+- 0.26236721873283386
+- 0.2794942855834961
+- 3.16611647605896
+val_scaled_root_mean_squared_error:
+- 1.8646117448806763
+- 1.852222204208374
+- 0.5867417454719543
+- 0.5928786396980286
+- 286.9599609375

training/results/MatProjectGapDataset/Schnet_make_crystal_model/Schnet_hyper.json

@@ -0,0 +1 @@
+{"model": {"module_name": "kgcnn.literature.Schnet", "class_name": "make_crystal_model", "config": {"name": "Schnet", "inputs": [{"shape": [null], "name": "node_number", "dtype": "int32"}, {"shape": [null, 3], "name": "node_coordinates", "dtype": "float32"}, {"shape": [null, 2], "name": "range_indices", "dtype": "int64"}, {"shape": [null, 3], "name": "range_image", "dtype": "int64"}, {"shape": [3, 3], "name": "graph_lattice", "dtype": "float32"}, {"shape": [], "name": "total_nodes", "dtype": "int64"}, {"shape": [], "name": "total_ranges", "dtype": "int64"}], "cast_disjoint_kwargs": {"padded_disjoint": false}, "input_node_embedding": {"input_dim": 95, "output_dim": 64}, "interaction_args": {"units": 128, "use_bias": true, "activation": "kgcnn>shifted_softplus", "cfconv_pool": "scatter_sum"}, "node_pooling_args": {"pooling_method": "scatter_mean"}, "depth": 4, "gauss_args": {"bins": 25, "distance": 5, "offset": 0.0, "sigma": 0.4}, "verbose": 10, "last_mlp": {"use_bias": [true, true, true], "units": [128, 64, 1], "activation": ["kgcnn>shifted_softplus", "kgcnn>shifted_softplus", "linear"]}, "output_embedding": "graph", "use_output_mlp": false, "output_mlp": null}}, "training": {"cross_validation": {"class_name": "KFold", "config": {"n_splits": 5, "random_state": 42, "shuffle": true}}, "fit": {"batch_size": 32, "epochs": 800, "validation_freq": 10, "verbose": 2, "callbacks": [{"class_name": "kgcnn>LinearLearningRateScheduler", "config": {"learning_rate_start": 0.0005, "learning_rate_stop": 1e-05, "epo_min": 100, "epo": 800, "verbose": 0}}]}, "compile": {"optimizer": {"class_name": "Adam", "config": {"learning_rate": 0.0005}}, "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"}, "info": {"postfix": "", "postfix_file": "", "kgcnn_version": "4.0.0"}, "dataset": {"class_name": "MatProjectGapDataset", "module_name": "kgcnn.data.datasets.MatProjectGapDataset", "config": {}, "methods": [{"map_list": {"method": "set_range_periodic", "max_distance": 5, "max_neighbours": 32}}, {"map_list": {"method": "count_nodes_and_edges", "total_edges": "total_ranges", "count_edges": "range_indices", "count_nodes": "node_number", "total_nodes": "total_nodes"}}]}}

training/results/README.md

@@ -124,6 +124,14 @@ Materials Project dataset from Matbench with 4764 crystal structures and their c
 |:--------------------------|:--------|---------:|:-----------------------|:-----------------------|
 | Schnet.make_crystal_model | 4.0.0   |      800 | **0.3180 ± 0.0359** | **1.8509 ± 0.5854** |
 
+#### MatProjectGapDataset
+
+Materials Project dataset from Matbench with 106113 crystal structures and their band gap as calculated by PBE DFT from the Materials Project, in eV. We use a random 5-fold cross-validation. 
+
+| model                     | kgcnn   |   epochs | MAE [eV]               | RMSE [eV]                 |
+|:--------------------------|:--------|---------:|:-----------------------|:--------------------------|
+| Schnet.make_crystal_model | 4.0.0   |      800 | **1.2226 ± 1.0573** | **58.3713 ± 114.2957** |
+
 #### MatProjectIsMetalDataset
 
 Materials Project dataset from Matbench with 106113 crystal structures and their corresponding Metallicity determined with pymatgen. 1 if the compound is a metal, 0 if the compound is not a metal. We use a random 5-fold cross-validation. 

training/results/summary.py

@@ -177,21 +177,21 @@
     #          "is_min_max": True}
     #     ]
     # },
-    # "MatProjectGapDataset": {
-    #     "general_info": [
-    #         "Materials Project dataset from Matbench with 106113 crystal structures ",
-    #         "and their band gap as calculated by PBE DFT from the Materials Project, in eV. ",
-    #         "We use a random 5-fold cross-validation. "
-    #     ],
-    #     "targets": [
-    #         {"metric": "val_scaled_mean_absolute_error", "name": "MAE [eV]", "find_best": "min"},
-    #         {"metric": "val_scaled_root_mean_squared_error", "name": "RMSE [eV]", "find_best": "min"},
-    #         {"metric": "min_val_scaled_mean_absolute_error", "name": "*Min. MAE*", "find_best": "min",
-    #          "is_min_max": True},
-    #         {"metric": "min_val_scaled_root_mean_squared_error", "name": "*Min. RMSE*", "find_best": "min",
-    #          "is_min_max": True}
-    #     ]
-    # },
+    "MatProjectGapDataset": {
+        "general_info": [
+            "Materials Project dataset from Matbench with 106113 crystal structures ",
+            "and their band gap as calculated by PBE DFT from the Materials Project, in eV. ",
+            "We use a random 5-fold cross-validation. "
+        ],
+        "targets": [
+            {"metric": "val_scaled_mean_absolute_error", "name": "MAE [eV]", "find_best": "min"},
+            {"metric": "val_scaled_root_mean_squared_error", "name": "RMSE [eV]", "find_best": "min"},
+            {"metric": "min_val_scaled_mean_absolute_error", "name": "*Min. MAE*", "find_best": "min",
+             "is_min_max": True},
+            {"metric": "min_val_scaled_root_mean_squared_error", "name": "*Min. RMSE*", "find_best": "min",
+             "is_min_max": True}
+        ]
+    },
     "MatProjectIsMetalDataset": {
         "general_info": [
             "Materials Project dataset from Matbench with 106113 crystal structures ",

training/train_force.py

@@ -21,8 +21,8 @@
 # Input arguments from command line.
 parser = argparse.ArgumentParser(description='Train a GNN on an Energy-Force Dataset.')
 parser.add_argument("--hyper", required=False, help="Filepath to hyper-parameter config file (.py or .json).",
-                    default="hyper/hyper_iso17.py")
-parser.add_argument("--category", required=False, help="Graph model to train.", default="Schnet.EnergyForceModel")
+                    default="hyper/hyper_md17.py")
+parser.add_argument("--category", required=False, help="Graph model to train.", default="PAiNN.EnergyForceModel")
 parser.add_argument("--model", required=False, help="Graph model to train.", default=None)
 parser.add_argument("--dataset", required=False, help="Name of the dataset.", default=None)
 parser.add_argument("--make", required=False, help="Name of the class for model.", default=None)