Merge pull request #125 from usnistgov/develop

Develop
usnistgov · Jul 10, 2023 · 7d21422 · 7d21422
2 parents 598d5b6 + 3e4943a
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diff --git a/README.md b/README.md
@@ -187,7 +187,7 @@ A basic web-app is for direct-prediction available at [JARVIS-ALIGNN app](https:
 ALIGNN-FF
 -------------------------
 
-[ASE calculator](https://wiki.fysik.dtu.dk/ase/ase/calculators/calculators.html) provides interface to various codes. An example for ALIGNN-FF is give below:
+[ASE calculator](https://wiki.fysik.dtu.dk/ase/ase/calculators/calculators.html) provides interface to various codes. An example for ALIGNN-FF is give below. Note that there are multiple pretrained ALIGNN-FF models available, here we use the deafult_path model. As more accurate models are developed, they will be made available as well:
 
 ```
 from alignn.ff.ff import AlignnAtomwiseCalculator,default_path
@@ -224,7 +224,7 @@ plt.show()
 
 To train ALIGNN-FF use `train_folder_ff.py` script which uses `atomwise_alignn` model:
 
-AtomWise prediction example which looks for similar setup as before but unstead of `id_prop.csv`, it requires `id_prop.json` file (see example in the sample_data_ff directory).:
+AtomWise prediction example which looks for similar setup as before but unstead of `id_prop.csv`, it requires `id_prop.json` file (see example in the sample_data_ff directory). Note ALIGNN-FF requires energy stored as energy per atom:
 
 ```
 train_folder_ff.py --root_dir "alignn/examples/sample_data_ff" --config "alignn/examples/sample_data_ff/config_example_atomwise.json" --output_dir=temp

diff --git a/alignn/__init__.py b/alignn/__init__.py
@@ -1,2 +1,2 @@
 """Version number."""
-__version__ = "2023.06.20"
+__version__ = "2023.07.01"
diff --git a/alignn/config.py b/alignn/config.py
@@ -203,6 +203,7 @@ class TrainingConfig(BaseSettings):
     keep_data_order: bool = True
     normalize_graph_level_loss: bool = False
     distributed: bool = False
+    data_parallel: bool = False
     n_early_stopping: Optional[int] = None  # typically 50
     output_dir: str = os.path.abspath(".")  # typically 50
     # alignn_layers: int = 4

diff --git a/alignn/examples/sample_data_ff/config_example_atomwise.json b/alignn/examples/sample_data_ff/config_example_atomwise.json
@@ -35,18 +35,19 @@
     "num_workers": 0,
     "cutoff": 8.0,
     "max_neighbors": 12,
-    "keep_data_order": false,
+    "keep_data_order": true,
+    "distributed":true,
     "model": {
         "name": "alignn_atomwise",
         "atom_input_features": 92,
         "calculate_gradient":true,
-        "atomwise_output_features":3,
+        "atomwise_output_features":0,
         "alignn_layers":4,
         "gcn_layers":4,
         "output_features": 1,
         "graphwise_weight":0.85,
         "gradwise_weight":0.05,
-        "atomwise_weight":0.05,
+        "atomwise_weight":0.0,
         "stresswise_weight":0.05
 
     }

diff --git a/alignn/examples/sample_data_ff/mlearn_data/Cu/config.json b/alignn/examples/sample_data_ff/mlearn_data/Cu/config.json
@@ -34,27 +34,28 @@
     "use_canonize": false,
     "num_workers": 0,
     "cutoff": 8.0,
-    "max_neighbors": 12,
+    "max_neighbors": 13,
     "keep_data_order": true,
     "normalize_graph_level_loss": false,
     "distributed": false,
     "n_early_stopping": null,
     "output_dir": "./",
     "model": {
         "name": "alignn_atomwise",
-        "alignn_layers": 4,
+        "alignn_layers": 2,
         "gcn_layers": 4,
         "atom_input_features": 92,
         "edge_input_features": 80,
         "triplet_input_features": 40,
-        "embedding_features": 64,
+        "embedding_features": 164,
         "hidden_features": 256,
         "output_features": 1,
+        "force_mult_natoms": true,
         "grad_multiplier": -1,
         "calculate_gradient": true,
         "atomwise_output_features": 0,
         "graphwise_weight": 1.0,
-        "gradwise_weight": 10.0,
+        "gradwise_weight": 50.00,
         "stresswise_weight": 0.0,
         "atomwise_weight": 0.0,
         "link": "identity",

diff --git a/alignn/examples/sample_data_ff/mlearn_data/Ge/config.json b/alignn/examples/sample_data_ff/mlearn_data/Ge/config.json
@@ -58,6 +58,7 @@
         "stresswise_weight": 0.0,
         "atomwise_weight": 0.0,
         "link": "identity",
+        "force_mult_natoms": true,
         "zero_inflated": false,
         "classification": false
     }

diff --git a/alignn/examples/sample_data_ff/mlearn_data/Li/config.json b/alignn/examples/sample_data_ff/mlearn_data/Li/config.json
@@ -56,6 +56,7 @@
         "graphwise_weight": 1.0,
         "gradwise_weight": 10.0,
         "stresswise_weight": 0.0,
+        "force_mult_natoms": true,
         "atomwise_weight": 0.0,
         "link": "identity",
         "zero_inflated": false,

diff --git a/alignn/examples/sample_data_ff/mlearn_data/Mo/config.json b/alignn/examples/sample_data_ff/mlearn_data/Mo/config.json
@@ -59,6 +59,7 @@
         "atomwise_weight": 0.0,
         "link": "identity",
         "zero_inflated": false,
+        "force_mult_natoms": true,
         "classification": false
     }
 }
diff --git a/alignn/examples/sample_data_ff/mlearn_data/Ni/config.json b/alignn/examples/sample_data_ff/mlearn_data/Ni/config.json
@@ -51,10 +51,11 @@
         "hidden_features": 256,
         "output_features": 1,
         "grad_multiplier": -1,
+        "force_mult_natoms": true,
         "calculate_gradient": true,
         "atomwise_output_features": 0,
         "graphwise_weight": 1.0,
-        "gradwise_weight": 10.0,
+        "gradwise_weight": 1.0,
         "stresswise_weight": 0.0,
         "atomwise_weight": 0.0,
         "link": "identity",

diff --git a/alignn/examples/sample_data_ff/mlearn_data/Si/config.json b/alignn/examples/sample_data_ff/mlearn_data/Si/config.json
@@ -54,9 +54,10 @@
         "calculate_gradient": true,
         "atomwise_output_features": 0,
         "graphwise_weight": 1.0,
-        "gradwise_weight": 10.0,
+        "gradwise_weight": 1.0,
         "stresswise_weight": 0.0,
         "atomwise_weight": 0.0,
+        "force_mult_natoms": true,
         "link": "identity",
         "zero_inflated": false,
         "classification": false,

diff --git a/alignn/examples/sample_data_ff/mlearn_data/all/config_example.json b/alignn/examples/sample_data_ff/mlearn_data/all/config_example.json
@@ -51,16 +51,17 @@
         "hidden_features": 256,
         "output_features": 1,
         "grad_multiplier": -1,
+        "force_mult_natoms": true,
         "calculate_gradient": true,
         "atomwise_output_features": 0,
         "graphwise_weight": 1.0,
-        "gradwise_weight": 1.0,
-        "stresswise_weight": 0.01,
+        "gradwise_weight": 50.0,
+        "stresswise_weight": 0.0,
         "atomwise_weight": 0.0,
         "link": "identity",
         "zero_inflated": false,
         "use_cutoff_function": true,
-        "energy_mult_natoms": true,
+        "energy_mult_natoms": false,
         "classification": false,
         "stress_multiplier":1
     }

diff --git a/alignn/ff/alignnff_fd/best_model.pt b/alignn/ff/alignnff_fd/best_model.pt
diff --git a/alignn/ff/alignnff_fd/config.json b/alignn/ff/alignnff_fd/config.json
@@ -0,0 +1,71 @@
+{
+    "version": "112bbedebdaecf59fb18e11c929080fb2f358246",
+    "dataset": "user_data",
+    "target": "target",
+    "atom_features": "cgcnn",
+    "neighbor_strategy": "k-nearest",
+    "id_tag": "jid",
+    "random_seed": 123,
+    "classification_threshold": null,
+    "n_val": null,
+    "n_test": null,
+    "n_train": null,
+    "train_ratio": 0.98,
+    "val_ratio": 0.01,
+    "test_ratio": 0.01,
+    "target_multiplication_factor": null,
+    "epochs": 50,
+    "batch_size": 10,
+    "weight_decay": 1e-05,
+    "learning_rate": 0.001,
+    "filename": "sample",
+    "warmup_steps": 2000,
+    "criterion": "l1",
+    "optimizer": "adamw",
+    "scheduler": "onecycle",
+    "pin_memory": false,
+    "save_dataloader": false,
+    "write_checkpoint": true,
+    "write_predictions": true,
+    "store_outputs": false,
+    "progress": true,
+    "log_tensorboard": false,
+    "standard_scalar_and_pca": false,
+    "use_canonize": false,
+    "num_workers": 0,
+    "cutoff": 8.0,
+    "max_neighbors": 12,
+    "keep_data_order": true,
+    "normalize_graph_level_loss": false,
+    "distributed": false,
+    "n_early_stopping": null,
+    "output_dir": "temp_new",
+    "model": {
+        "name": "alignn_atomwise",
+        "alignn_layers": 4,
+        "gcn_layers": 4,
+        "atom_input_features": 92,
+        "edge_input_features": 80,
+        "triplet_input_features": 40,
+        "embedding_features": 64,
+        "hidden_features": 256,
+        "output_features": 1,
+        "grad_multiplier": -1,
+        "calculate_gradient": true,
+        "atomwise_output_features": 0,
+        "graphwise_weight": 1.0,
+        "gradwise_weight": 10.0,
+        "stresswise_weight": 0.0,
+        "atomwise_weight": 0.0,
+        "link": "identity",
+        "zero_inflated": false,
+        "classification": false,
+        "force_mult_natoms": true,
+        "energy_mult_natoms": false,
+        "include_pos_deriv": false,
+        "use_cutoff_function": false,
+        "inner_cutoff": 6.0,
+        "stress_multiplier": 1.0,
+        "add_reverse_forces": false
+    }
+}
diff --git a/alignn/ff/alignnff_fmult/best_model.pt b/alignn/ff/alignnff_fmult/best_model.pt
diff --git a/alignn/ff/alignnff_fmult/config.json b/alignn/ff/alignnff_fmult/config.json
@@ -0,0 +1,72 @@
+{
+    "version": "112bbedebdaecf59fb18e11c929080fb2f358246",
+    "dataset": "user_data",
+    "target": "target",
+    "atom_features": "cgcnn",
+    "neighbor_strategy": "k-nearest",
+    "id_tag": "jid",
+    "random_seed": 123,
+    "classification_threshold": null,
+    "n_val": null,
+    "n_test": null,
+    "n_train": null,
+    "train_ratio": 0.9,
+    "val_ratio": 0.05,
+    "test_ratio": 0.05,
+    "target_multiplication_factor": null,
+    "epochs": 300,
+    "batch_size": 16,
+    "weight_decay": 1e-05,
+    "learning_rate": 0.001,
+    "filename": "sample",
+    "warmup_steps": 2000,
+    "criterion": "l1",
+    "optimizer": "adamw",
+    "scheduler": "onecycle",
+    "pin_memory": false,
+    "save_dataloader": false,
+    "write_checkpoint": true,
+    "write_predictions": true,
+    "store_outputs": false,
+    "progress": true,
+    "log_tensorboard": false,
+    "standard_scalar_and_pca": false,
+    "use_canonize": false,
+    "num_workers": 0,
+    "cutoff": 8.0,
+    "max_neighbors": 12,
+    "keep_data_order": false,
+    "normalize_graph_level_loss": false,
+    "distributed": false,
+    "data_parallel": false,
+    "n_early_stopping": null,
+    "output_dir": "temp_new",
+    "model": {
+        "name": "alignn_atomwise",
+        "alignn_layers": 2,
+        "gcn_layers": 4,
+        "atom_input_features": 92,
+        "edge_input_features": 80,
+        "triplet_input_features": 40,
+        "embedding_features": 64,
+        "hidden_features": 256,
+        "output_features": 1,
+        "grad_multiplier": -1,
+        "calculate_gradient": true,
+        "atomwise_output_features": 0,
+        "graphwise_weight": 0.8,
+        "gradwise_weight": 0.2,
+        "stresswise_weight": 0.0,
+        "atomwise_weight": 0.0,
+        "link": "identity",
+        "zero_inflated": false,
+        "classification": false,
+        "force_mult_natoms": true,
+        "energy_mult_natoms": false,
+        "include_pos_deriv": false,
+        "use_cutoff_function": false,
+        "inner_cutoff": 6.0,
+        "stress_multiplier": 1.0,
+        "add_reverse_forces": false
+    }
+}