Merge pull request #94 from usnistgov/develop

Develop

.github/workflows/main.yml

@@ -33,14 +33,14 @@ jobs:
         export DGLBACKEND=pytorch
         export CUDA_VISIBLE_DEVICES="-1"
         #pip install dgl-cu111
-        pip install flake8 pytest pycodestyle pydocstyle codecov pytest-cov coverage 
+        pip install flake8 pytest pycodestyle pydocstyle codecov pytest-cov coverage
         #pip uninstall -y torch nvidia-cublas-cu11 nvidia-cuda-nvrtc-cu11 nvidia-cuda-runtime-cu11 nvidia-cudnn-cu11
         #conda install -y  pytorch-cpu
         #pip install attrs==22.1.0 certifi==2022.9.24 charset-normalizer==2.1.1 codecov==2.1.12 contourpy==1.0.5 coverage==6.5.0 cycler==0.11.0 dgl==0.9.1 flake8==5.0.4 fonttools==4.38.0 idna==3.4 iniconfig==1.1.1 jarvis-tools==2022.9.16 joblib==1.2.0 kiwisolver==1.4.4 matplotlib==3.6.1 mccabe==0.7.0 networkx==3.0b1 numpy==1.23.4 packaging==21.3 pandas==1.5.1 Pillow==9.2.0 pluggy==1.0.0 psutil==5.9.3 py==1.11.0 pycodestyle==2.9.1 pydantic==1.10.2 pydocstyle==6.1.1 pyflakes==2.5.0 pyparsing==2.4.7 pytest==7.1.3 pytest-cov==4.0.0 python-dateutil==2.8.2 pytorch-ignite==0.5.0.dev20221024 pytz==2022.5 requests==2.28.1 scikit-learn==1.1.2 scipy==1.9.3 six==1.16.0 snowballstemmer==2.2.0 spglib==2.0.1 threadpoolctl==3.1.0 tomli==2.0.1 toolz==0.12.0 torch==1.12.1 tqdm==4.64.1 typing_extensions==4.4.0 urllib3==1.26.12 xmltodict==0.13.0
         echo 'PIP freeze'
         pip freeze
         coverage run -m pytest
-        coverage report -m
+        coverage report -m -i
         codecov
         codecov --token="85bd9c5d-9e55-4f6d-bd69-350ee5e3bb41"
         echo 'Train folder'

README.md

@@ -21,7 +21,7 @@
 * [Pre-trained models](#pretrained)
 * [Quick start using colab](#colab)
 * [JARVIS-ALIGNN webapp](#webapp)
-* [ALIGNN-FF](#alignnff)
+* [ALIGNN-FF & ASE Calculator](#alignnff)
 * [Peformances on a few datasets](#performances)
 * [Useful notes](#notes)
 * [References](#refs)
@@ -166,9 +166,44 @@ 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:
+
+```
+from alignn.ff.ff import AlignnAtomwiseCalculator,default_path
+model_path = default_path()
+calc = AlignnAtomwiseCalculator(path=model_path)
+
+from ase import Atom, Atoms
+import numpy as np
+import matplotlib.pyplot as plt
+
+lattice_params = np.linspace(3.5, 3.8)
+fcc_energies = []
+ready = True
+for a in lattice_params:
+    atoms = Atoms([Atom('Cu', (0, 0, 0))],
+                  cell=0.5 * a * np.array([[1.0, 1.0, 0.0],
+                                           [0.0, 1.0, 1.0],
+                                           [1.0, 0.0, 1.0]]),
+                 pbc=True)
+    
+    atoms.set_tags(np.ones(len(atoms)))
+    atoms.calc = calc
+    e = atoms.get_potential_energy()
+    fcc_energies.append(e)
+    
+import matplotlib.pyplot as plt
+%matplotlib inline
+plt.plot(lattice_params, fcc_energies)
+plt.title('1x1x1')
+plt.xlabel('Lattice constant ($\AA$)')
+plt.ylabel('Total energy (eV)')
+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).:
 
 ```
 train_folder_ff.py --root_dir "alignn/examples/sample_data_ff" --config "alignn/examples/sample_data_ff/config_example_atomwise.json" --output_dir=temp
@@ -189,6 +224,7 @@ run_alignn_ff.py -h
 ```
 
 
+
 <a name="performances"></a>
 
 Performances
@@ -307,7 +343,11 @@ coming soon!
 
 ### 10) On OpenCatalyst dataset 
 
-coming soon!
+[On 10k dataset](https://github.com/Open-Catalyst-Project/ocp/blob/main/MODELS.md#is2re-models):
+
+CGCNN MAE: 0.988
+
+ALIGNN MAE: 0.61
 
 
 <a name="notes"></a>

alignn/__init__.py

@@ -1,2 +1,2 @@
 """Version number."""
-__version__ = "2023.01.10"
+__version__ = "2023.04.01"

alignn/pretrained.py

@@ -102,6 +102,18 @@
         "https://figshare.com/ndownloader/files/38789199",
         1,
     ],
+    "jv_supercon_edos_alignn": [
+        "https://figshare.com/ndownloader/files/39946300",
+        1,
+    ],
+    "jv_supercon_debye_alignn": [
+        "https://figshare.com/ndownloader/files/39946297",
+        1,
+    ],
+    "jv_supercon_a2F_alignn": [
+        "https://figshare.com/ndownloader/files/38801886",
+        100,
+    ],
     "mp_e_form_alignnn": [
         "https://figshare.com/ndownloader/files/31458811",
         1,

alignn/train.py

@@ -1012,26 +1012,25 @@ def es_score(engine):
             mean_absolute_error(np.array(targets), np.array(predictions)),
         )
         if config.store_outputs and not classification:
-            x = []
-            y = []
-            for i in history["EOS"]:
-                x.append(i[0].cpu().numpy().tolist())
-                y.append(i[1].cpu().numpy().tolist())
-            x = np.array(x, dtype="float").flatten()
-            y = np.array(y, dtype="float").flatten()
-            f = open(
-                os.path.join(
-                    config.output_dir, "prediction_results_train_set.csv"
-                ),
-                "w",
-            )
+            # save training targets and predictions here
             # TODO: Add IDs
-            f.write("target,prediction\n")
-            for i, j in zip(x, y):
-                f.write("%6f, %6f\n" % (j, i))
-                line = str(i) + "," + str(j) + "\n"
-                f.write(line)
-            f.close()
+            resultsfile = os.path.join(
+                config.output_dir, "prediction_results_train_set.csv"
+            )
+
+            target_vals, predictions = [], []
+
+            for tgt, pred in history["trainEOS"]:
+                target_vals.append(tgt.cpu().numpy().tolist())
+                predictions.append(pred.cpu().numpy().tolist())
+
+            target_vals = np.array(target_vals, dtype="float").flatten()
+            predictions = np.array(predictions, dtype="float").flatten()
+
+            with open(resultsfile, "w") as f:
+                print("target,prediction", file=f)
+                for target_val, predicted_val in zip(target_vals, predictions):
+                    print(f"{target_val}, {predicted_val}", file=f)
 
     # TODO: Fix IDs for train loader
     """

setup.py

@@ -10,21 +10,21 @@
 
 setuptools.setup(
     name="alignn",
-    version="2023.01.10",
+    version="2023.04.01",
     author="Kamal Choudhary, Brian DeCost",
     author_email="kamal.choudhary@nist.gov",
     description="alignn",
     install_requires=[
         "numpy>=1.19.5",
         "scipy>=1.6.1",
         "jarvis-tools>=2021.07.19",
-        "torch==1.12.0",
+        "torch>=1.8",
         "dgl>=0.6.0",
         "scikit-learn>=0.22.2",
         "matplotlib>=3.4.1",
         "tqdm>=4.60.0",
         "pandas>=1.2.3",
-        "pytorch-ignite==0.5.0.dev20221024",
+        "pytorch-ignite>=0.5.0.dev20221024",
         "pydantic>=1.8.1",
         "flake8>=3.9.1",
         "pycodestyle>=2.7.0",