From ecae265f23c3b8013d677142891e07f00cbf7feb Mon Sep 17 00:00:00 2001 From: knc6 Date: Tue, 6 Jul 2021 13:04:11 -0400 Subject: [PATCH 1/3] Multi-output script, multiple updates. --- README.md | 7 +- alignn/__init__.py | 2 +- alignn/config.py | 11 ++ alignn/data.py | 44 ++++++- .../sample_data/generate_sample_data_reg.py | 20 ---- ...n_all_qm9_dgl.py => train_all_qm9_jctc.py} | 21 ++-- alignn/scripts/train_folder_multi_prop.py | 112 ++++++++++++++++++ alignn/train.py | 10 ++ alignn/train_props.py | 56 +++------ setup.py | 6 +- 10 files changed, 211 insertions(+), 78 deletions(-) delete mode 100644 alignn/examples/sample_data/generate_sample_data_reg.py rename alignn/scripts/{train_all_qm9_dgl.py => train_all_qm9_jctc.py} (83%) create mode 100644 alignn/scripts/train_folder_multi_prop.py diff --git a/README.md b/README.md index 719cf8fe..e573cb5a 100644 --- a/README.md +++ b/README.md @@ -26,5 +26,10 @@ Example ``` python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example_regrssion.json" ``` - +``` +python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example_classification.json" +``` +``` +python alignn/scripts/train_folder_multi_prop.py --root_dir "alignn/examples/sample_data_multi_prop" --config "alignn/examples/sample_data/config_example_regrssion.json" +``` You can also try multiple example scripts to run multiple dataset training. Look into the 'scripts' folder. diff --git a/alignn/__init__.py b/alignn/__init__.py index c39de043..040c2e1a 100644 --- a/alignn/__init__.py +++ b/alignn/__init__.py @@ -1,2 +1,2 @@ """Version number.""" -__version__ = "2021.06.20" +__version__ = "2021.07.05" diff --git a/alignn/config.py b/alignn/config.py index d8d1cca1..69b38745 100644 --- a/alignn/config.py +++ b/alignn/config.py @@ -72,6 +72,11 @@ "shear modulus", "elastic anisotropy", "U0", + "HOMO", + "LUMO", + "R2", + "ZPVE", + "omega1", "mu", "alpha", "homo", @@ -86,6 +91,7 @@ "A", "B", "C", + "all", "target", "max_efg", "avg_elec_mass", @@ -95,6 +101,9 @@ "_oqmd_stability", "edos_up", "pdos_elast", + "bandgap", + "energy_total", + "net_magmom", ] @@ -112,10 +121,12 @@ class TrainingConfig(BaseSettings): "mp_3d_2020", "qm9", "qm9_dgl", + "qm9_std_jctc", "user_data", "oqmd_3d_no_cfid", "edos_up", "edos_pdos", + "qmof", ] = "dft_3d" target: TARGET_ENUM = "formation_energy_peratom" atom_features: Literal["basic", "atomic_number", "cfid", "cgcnn"] = "cgcnn" diff --git a/alignn/data.py b/alignn/data.py index 6ca2f09e..d99e80ac 100644 --- a/alignn/data.py +++ b/alignn/data.py @@ -21,7 +21,8 @@ # from sklearn.pipeline import Pipeline import pickle as pk -from sklearn.decomposition import PCA # ,KernelPCA + +# from sklearn.decomposition import PCA # ,KernelPCA from sklearn.preprocessing import StandardScaler # use pandas progress_apply @@ -296,6 +297,29 @@ def get_train_val_loaders( ) print("Converting target data into 1 and 0.") all_targets = [] + + # TODO:make an all key in qm9_dgl + if dataset == "qm9_dgl" and target == "all": + print("Making all qm9_dgl") + tmp = [] + for ii in d: + ii["all"] = [ + ii["mu"], + ii["alpha"], + ii["homo"], + ii["lumo"], + ii["gap"], + ii["r2"], + ii["zpve"], + ii["U0"], + ii["U"], + ii["H"], + ii["G"], + ii["Cv"], + ] + tmp.append(ii) + print("Made all qm9_dgl") + d = tmp for i in d: if isinstance(i[target], list): # multioutput target all_targets.append(torch.tensor(i[target])) @@ -347,8 +371,20 @@ def get_train_val_loaders( if standard_scalar_and_pca: y_data = [i[target] for i in dataset_train] # pipe = Pipeline([('scale', StandardScaler())]) + if not isinstance(y_data[0], list): + print("Running StandardScalar") + y_data = np.array(y_data).reshape(-1, 1) sc = StandardScaler() + sc.fit(y_data) + print("Mean", sc.mean_) + print("Variance", sc.var_) + try: + print("New max", max(y_data)) + print("New min", min(y_data)) + except Exception as exp: + print(exp) + pass # pc = PCA(n_components=output_features) # pipe = Pipeline( # [ @@ -357,9 +393,9 @@ def get_train_val_loaders( # ] # ) pk.dump(sc, open("sc.pkl", "wb")) - pc = PCA(n_components=40) - pc.fit(y_data) - pk.dump(pc, open("pca.pkl", "wb")) + # pc = PCA(n_components=10) + # pc.fit(y_data) + # pk.dump(pc, open("pca.pkl", "wb")) if classification_threshold is None: try: diff --git a/alignn/examples/sample_data/generate_sample_data_reg.py b/alignn/examples/sample_data/generate_sample_data_reg.py deleted file mode 100644 index 78c50564..00000000 --- a/alignn/examples/sample_data/generate_sample_data_reg.py +++ /dev/null @@ -1,20 +0,0 @@ -from jarvis.db.figshare import data as jdata -from jarvis.core.atoms import Atoms - -dft_3d = jdata("dft_3d") -prop = "optb88vdw_bandgap" -max_samples = 50 -f = open("id_prop.csv", "w") -count = 0 -for i in dft_3d: - atoms = Atoms.from_dict(i["atoms"]) - jid = i["jid"] - poscar_name = "POSCAR-" + jid + ".vasp" - target = i[prop] - if target != "na": - atoms.write_poscar(poscar_name) - f.write("%s,%6f\n" % (poscar_name, target)) - count += 1 - if count == max_samples: - break -f.close() diff --git a/alignn/scripts/train_all_qm9_dgl.py b/alignn/scripts/train_all_qm9_jctc.py similarity index 83% rename from alignn/scripts/train_all_qm9_dgl.py rename to alignn/scripts/train_all_qm9_jctc.py index 4efb0f42..84202f5d 100644 --- a/alignn/scripts/train_all_qm9_dgl.py +++ b/alignn/scripts/train_all_qm9_jctc.py @@ -5,23 +5,20 @@ # d=data('dft_3d') props = [ + "U0", "mu", "alpha", - "homo", - "lumo", + "HOMO", + "LUMO", "gap", - "r2", - "zpve", - "U0", + "R2", + "ZPVE", "U", "H", "G", "Cv", - "A", - "B", - "C", + "omega1", ] -# props=['U0','mu'] cwd_home = os.getcwd() for i in props: model_name = "qm9_" + i + "_alignn" @@ -32,7 +29,7 @@ f = open("train.py", "w") tmp = ( "from alignn.train_props import " - + 'train_prop_model \ntrain_prop_model(dataset="qm9_dgl",prop="' + + 'train_prop_model \ntrain_prop_model(dataset="qm9_std_jctc",prop="' ) line = tmp + i + '")\n' @@ -48,9 +45,9 @@ directory=directory, cores=None, submit_cmd=submit_cmd, - memory="90G", + memory="94G", filename=model_name, - queue="singlegpu,general", + queue="singlegpu", walltime="150:00:00", pre_job_lines="#SBATCH --gres=gpu:1\n" # pre_job_lines='#SBATCH --gres=gpu:1\n. ~/.bashrc \n' diff --git a/alignn/scripts/train_folder_multi_prop.py b/alignn/scripts/train_folder_multi_prop.py new file mode 100644 index 00000000..7c11acf7 --- /dev/null +++ b/alignn/scripts/train_folder_multi_prop.py @@ -0,0 +1,112 @@ +import csv +import os +import sys +from jarvis.core.atoms import Atoms + +# from jarvis.core.graphs import Graph +# from alignn.models.alignn import ALIGNN +from alignn.data import get_train_val_loaders +from alignn.train import train_dgl +from alignn.config import TrainingConfig +from jarvis.db.jsonutils import loadjson +import argparse + +parser = argparse.ArgumentParser( + description="Atomistic Line Graph Neural Network" +) +parser.add_argument( + "--root_dir", + default="./", + help="Folder with id_multi_prop.csv, poscars/cifs and config*.json", +) +parser.add_argument( + "--config_name", + default="config_example_regrssion.json", + help="Name of the config file", +) + + +def train_for_folder_multi_prop( + root_dir="examples/sample_data_multi_prop", config_name="config.json" +): + # config_dat=os.path.join(root_dir,config_name) + id_prop_dat = os.path.join(root_dir, "id_multi_prop.csv") + config = loadjson(config_name) + if type(config) is dict: + try: + config = TrainingConfig(**config) + except Exception as exp: + print("Check", exp) + + with open(id_prop_dat, "r") as f: + reader = csv.reader(f) + data = [row for row in reader] + + dataset = [] + n_outputs = [] + for i in data: + info = {} + poscar_name = i[0] + poscar_path = os.path.join(root_dir, poscar_name) + atoms = Atoms.from_poscar(poscar_path) + info["atoms"] = atoms.to_dict() + info["jid"] = poscar_name + info["target"] = [float(j) for j in i[1:]] + n_outputs.append(info["target"]) + dataset.append(info) + lists_are_length_equaled = False not in [ + len(i) == len(n_outputs[0]) for i in n_outputs + ] + if lists_are_length_equaled: + config.model.output_features = len(n_outputs[0]) + else: + # TODO: Pad with NaN + raise ValueError("Make sure the outputs are of same size.") + + ( + train_loader, + val_loader, + test_loader, + prepare_batch, + ) = get_train_val_loaders( + dataset_array=dataset, + target=config.target, + n_train=config.n_train, + n_val=config.n_val, + n_test=config.n_test, + train_ratio=config.train_ratio, + val_ratio=config.val_ratio, + test_ratio=config.test_ratio, + batch_size=config.batch_size, + atom_features=config.atom_features, + neighbor_strategy=config.neighbor_strategy, + standardize=config.atom_features != "cgcnn", + id_tag=config.id_tag, + pin_memory=config.pin_memory, + workers=config.num_workers, + save_dataloader=config.save_dataloader, + use_canonize=config.use_canonize, + filename=config.filename, + cutoff=config.cutoff, + max_neighbors=config.max_neighbors, + classification_threshold=config.classification_threshold, + target_multiplication_factor=config.target_multiplication_factor, + ) + train_dgl( + config, + train_val_test_loaders=[ + train_loader, + val_loader, + test_loader, + prepare_batch, + ], + ) + + # train_data = get_torch_dataset( + + +if __name__ == "__main__": + args = parser.parse_args(sys.argv[1:]) + train_for_folder_multi_prop( + root_dir=args.root_dir, config_name=args.config_name + ) diff --git a/alignn/train.py b/alignn/train.py index 500c7efc..d474d9cb 100644 --- a/alignn/train.py +++ b/alignn/train.py @@ -470,6 +470,11 @@ def log_results(engine): g, lg, target = dat out_data = net([g.to(device), lg.to(device)]) out_data = out_data.cpu().numpy().tolist() + if config.standard_scalar_and_pca: + sc = pk.load(open("sc.pkl", "rb")) + out_data = list( + sc.transform(np.array(out_data).reshape(1, -1))[0] + ) # [0][0] target = target.cpu().numpy().flatten().tolist() info = {} info["id"] = id @@ -493,6 +498,11 @@ def log_results(engine): g, lg, target = dat out_data = net([g.to(device), lg.to(device)]) out_data = out_data.cpu().numpy().tolist() + if config.standard_scalar_and_pca: + sc = pk.load(open("sc.pkl", "rb")) + out_data = sc.transform(np.array(out_data).reshape(-1, 1))[ + 0 + ][0] target = target.cpu().numpy().flatten().tolist() if len(target) == 1: target = target[0] diff --git a/alignn/train_props.py b/alignn/train_props.py index ed52bc71..7f0bf485 100644 --- a/alignn/train_props.py +++ b/alignn/train_props.py @@ -8,41 +8,6 @@ # from sklearn.metrics import mean_absolute_error plt.switch_backend("agg") -jv_3d_props = [ - "formation_energy_peratom", - "optb88vdw_bandgap", - "bulk_modulus_kv", - "shear_modulus_gv", - "mbj_bandgap", - "slme", - "magmom_oszicar", - "spillage", - "kpoint_length_unit", - "encut", - "optb88vdw_total_energy", - "epsx", - "epsy", - "epsz", - "mepsx", - "mepsy", - "mepsz", - "max_ir_mode", - "min_ir_mode", - "n-Seebeck", - "p-Seebeck", - "n-powerfact", - "p-powerfact", - "ncond", - "pcond", - "nkappa", - "pkappa", - "ehull", - "exfoliation_energy", - "dfpt_piezo_max_dielectric", - "dfpt_piezo_max_eij", - "dfpt_piezo_max_dij", -] - def train_prop_model( prop="", @@ -58,6 +23,7 @@ def train_prop_model( batch_size=None, scheduler=None, n_epochs=None, + id_tag=None, ): """Train models for a dataset and a property.""" if scheduler is None: @@ -85,6 +51,8 @@ def train_prop_model( "name": name, }, } + if id_tag is not None: + config["id_tag"] = id_tag if train_ratio is not None: config["train_ratio"] = train_ratio if val_ratio is None: @@ -126,14 +94,28 @@ def train_prop_model( config["model"]["output_features"] = 200 else: raise ValueError("Target not available.") + if dataset == "qm9_std_jctc": + config["id_tag"] = "id" + config["n_train"] = 110000 + config["n_val"] = 10000 + config["n_test"] = 10829 + + # config["batch_size"] = 64 + config["cutoff"] = 5.0 + config["standard_scalar_and_pca"] = False + if dataset == "qm9_dgl": config["id_tag"] = "id" config["n_train"] = 110000 config["n_val"] = 10000 config["n_test"] = 10831 - config["batch_size"] = batch_size + config["standard_scalar_and_pca"] = False + config["batch_size"] = 64 config["cutoff"] = 5.0 - config["max_neighbors"] = 9 + if config["target"] == "all": + config["model"]["output_features"] = 12 + + # config["max_neighbors"] = 9 if dataset == "qm9": config["id_tag"] = "id" diff --git a/setup.py b/setup.py index 2a967745..7105ddde 100644 --- a/setup.py +++ b/setup.py @@ -10,14 +10,14 @@ setuptools.setup( name="alignn", # Replace with your own username - version="2021.06.20", + version="2021.07.05", author="Kamal Choudhary, Brian DeCost", author_email="kamal.choudhary@nist.gov", description="alignn", install_requires=[ - "numpy>=1.20.2", + "numpy>=1.19.5", "scipy>=1.6.3", - "jarvis-tools>=2021.06.18", + "jarvis-tools>=2021.07.05", "torch>=1.7.1", "dgl-cu101>=0.6.0", "dgl>=0.6.0", From 117d6a6a122453cb0346c4f1af4061609d7c8fb3 Mon Sep 17 00:00:00 2001 From: Kamal Choudhary Date: Tue, 6 Jul 2021 16:09:16 -0400 Subject: [PATCH 2/3] Update README.md --- README.md | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e573cb5a..b28cffe3 100644 --- a/README.md +++ b/README.md @@ -7,29 +7,49 @@ Installation ------------------------- First create a conda environment: Install miniconda environment from https://conda.io/miniconda.html +Based on your system requirements, you'll get a file something like 'Miniconda3-latest-XYZ'. + +Now, + ``` bash Miniconda3-latest-Linux-x86_64.sh (for linux) bash Miniconda3-latest-MacOSX-x86_64.sh (for Mac) +``` Download 32/64 bit python 3.6 miniconda exe and install (for windows) Now, let's make a conda environment, say "version", choose other name as you like:: +``` conda create --name version python=3.8 source activate version ``` -Now, let's install the package + +Now, let's install the package: ``` git clone https://github.com/usnistgov/alignn.git cd alignn python setup.py develop ``` -Example +Examples --------- +Users can keep their structure files in POSCAR, .cif, or .xyz files in a directory. In the examples below we will use POSCAR format files. In the same directory, there should be id_prop.csv file. +In this id_prop.csv, the filenames, and correponding target values are kept in comma separated values (csv) format. +Here is an example of training OptB88vdw bandgaps of 50 materials from JARVIS-DFT. The example is created using the script provided in the script folder. +Users can modify the script more than 50 data, or make their own dataset in this format. The dataset in split in 80:10:10 as training-validation-test set. +With the configuration parameters given in config_example_regrssion.json, the model is trained. + ``` python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example_regrssion.json" ``` +While the above example is for regression, the follwoing example shows a classification task for metal/non-metal based on the above bandgap values. We transform the dataset +into 1 or 0 based on a threshold of 0.01 eV (controlled by the parameter, 'classification_threshold') and train a similar classification model. ``` python alignn/scripts/train_folder.py --root_dir "alignn/examples/sample_data" --config "alignn/examples/sample_data/config_example_classification.json" ``` +While the above example regression was for single-output values, we can train multi-output regression models as well. +An example is given below for training formation energy per atom, bandgap and total energy per atom simulataneously. The script to generate the example data is provided in the script folder of the sample_data_multi_prop. +Another example of training electron and phonon density of states is provided also. ``` python alignn/scripts/train_folder_multi_prop.py --root_dir "alignn/examples/sample_data_multi_prop" --config "alignn/examples/sample_data/config_example_regrssion.json" ``` -You can also try multiple example scripts to run multiple dataset training. Look into the 'scripts' folder. + +You can also try multiple example scripts to run multiple dataset training. Look into the 'scripts' folder. +These scripts automatically download datasets from jarvis.db.fighshare module in JARVIS-Tools and train several models. Make sure you specify your specific queuing system details in the scripts. From 7ef12f2bc96a01e64086e63b5b41f8f7aed12d84 Mon Sep 17 00:00:00 2001 From: knc6 Date: Tue, 6 Jul 2021 16:11:29 -0400 Subject: [PATCH 3/3] Example folder added. --- .../scripts/generate_sample_data_reg.py | 20 ++++++ .../POSCAR-JVASP-10.vasp | 11 +++ .../POSCAR-JVASP-107772.vasp | 12 ++++ .../POSCAR-JVASP-1372.vasp | 10 +++ .../POSCAR-JVASP-14014.vasp | 20 ++++++ .../POSCAR-JVASP-14873.vasp | 15 ++++ .../POSCAR-JVASP-15345.vasp | 12 ++++ .../POSCAR-JVASP-1996.vasp | 10 +++ .../POSCAR-JVASP-21210.vasp | 9 +++ .../POSCAR-JVASP-22556.vasp | 13 ++++ .../POSCAR-JVASP-28397.vasp | 16 +++++ .../POSCAR-JVASP-28565.vasp | 17 +++++ .../POSCAR-JVASP-28634.vasp | 20 ++++++ .../POSCAR-JVASP-28704.vasp | 20 ++++++ .../POSCAR-JVASP-42300.vasp | 30 ++++++++ .../POSCAR-JVASP-48166.vasp | 22 ++++++ .../POSCAR-JVASP-50332.vasp | 13 ++++ .../POSCAR-JVASP-60702.vasp | 52 ++++++++++++++ .../POSCAR-JVASP-63912.vasp | 14 ++++ .../POSCAR-JVASP-64003.vasp | 14 ++++ .../POSCAR-JVASP-64045.vasp | 14 ++++ .../POSCAR-JVASP-64240.vasp | 14 ++++ .../POSCAR-JVASP-64377.vasp | 14 ++++ .../POSCAR-JVASP-64584.vasp | 14 ++++ .../POSCAR-JVASP-64664.vasp | 14 ++++ .../POSCAR-JVASP-64719.vasp | 14 ++++ .../POSCAR-JVASP-64906.vasp | 12 ++++ .../POSCAR-JVASP-65062.vasp | 14 ++++ .../POSCAR-JVASP-65101.vasp | 14 ++++ .../POSCAR-JVASP-86097.vasp | 15 ++++ .../POSCAR-JVASP-86205.vasp | 25 +++++++ .../POSCAR-JVASP-86436.vasp | 16 +++++ .../POSCAR-JVASP-86726.vasp | 14 ++++ .../POSCAR-JVASP-86968.vasp | 17 +++++ .../POSCAR-JVASP-89025.vasp | 44 ++++++++++++ .../POSCAR-JVASP-89265.vasp | 36 ++++++++++ .../POSCAR-JVASP-90228.vasp | 17 +++++ .../POSCAR-JVASP-90532.vasp | 17 +++++ .../POSCAR-JVASP-90856.vasp | 16 +++++ .../POSCAR-JVASP-97378.vasp | 44 ++++++++++++ .../POSCAR-JVASP-97499.vasp | 56 +++++++++++++++ .../POSCAR-JVASP-97570.vasp | 30 ++++++++ .../POSCAR-JVASP-97677.vasp | 72 +++++++++++++++++++ .../POSCAR-JVASP-97799.vasp | 32 +++++++++ .../POSCAR-JVASP-97915.vasp | 43 +++++++++++ .../POSCAR-JVASP-97984.vasp | 20 ++++++ .../POSCAR-JVASP-98167.vasp | 38 ++++++++++ .../POSCAR-JVASP-98224.vasp | 18 +++++ .../POSCAR-JVASP-98225.vasp | 40 +++++++++++ .../POSCAR-JVASP-98284.vasp | 35 +++++++++ .../POSCAR-JVASP-98550.vasp | 28 ++++++++ .../sample_data_multi_prop/id_multi_prop.csv | 50 +++++++++++++ .../scripts/generate_sample_data_edos.py | 22 ++++++ .../scripts/generate_sample_data_reg.py | 29 ++++++++ 54 files changed, 1248 insertions(+) create mode 100644 alignn/examples/sample_data/scripts/generate_sample_data_reg.py create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-10.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-107772.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1372.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14014.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14873.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-15345.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1996.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-21210.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-22556.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28397.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28565.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28634.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28704.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-42300.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-48166.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-50332.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-60702.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-63912.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64003.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64045.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64240.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64377.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64584.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64664.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64719.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-64906.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-65062.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-65101.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-86097.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-86205.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-86436.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-86726.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-86968.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-89025.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-89265.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-90228.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-90532.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-90856.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97378.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97499.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97570.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97677.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97799.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97915.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-97984.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-98167.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-98224.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-98225.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-98284.vasp create mode 100644 alignn/examples/sample_data_multi_prop/POSCAR-JVASP-98550.vasp create mode 100644 alignn/examples/sample_data_multi_prop/id_multi_prop.csv create mode 100644 alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_edos.py create mode 100644 alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_reg.py diff --git a/alignn/examples/sample_data/scripts/generate_sample_data_reg.py b/alignn/examples/sample_data/scripts/generate_sample_data_reg.py new file mode 100644 index 00000000..78c50564 --- /dev/null +++ b/alignn/examples/sample_data/scripts/generate_sample_data_reg.py @@ -0,0 +1,20 @@ +from jarvis.db.figshare import data as jdata +from jarvis.core.atoms import Atoms + +dft_3d = jdata("dft_3d") +prop = "optb88vdw_bandgap" +max_samples = 50 +f = open("id_prop.csv", "w") +count = 0 +for i in dft_3d: + atoms = Atoms.from_dict(i["atoms"]) + jid = i["jid"] + poscar_name = "POSCAR-" + jid + ".vasp" + target = i[prop] + if target != "na": + atoms.write_poscar(poscar_name) + f.write("%s,%6f\n" % (poscar_name, target)) + count += 1 + if count == max_samples: + break +f.close() diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-10.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-10.vasp new file mode 100644 index 00000000..85127db6 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-10.vasp @@ -0,0 +1,11 @@ +System +1.0 +1.6777483798834445 -2.9059452409270157 -1.1e-15 +1.6777483798834438 2.9059452409270126 -7e-16 +-6.5e-15 -8e-16 6.220805465667012 +V Se +1 2 +direct +0.0 0.0 0.0 +0.6666669999999968 0.3333330000000032 0.7479606991085345 +0.3333330000000032 0.6666669999999968 0.252039300891465 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-107772.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-107772.vasp new file mode 100644 index 00000000..c82d4658 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-107772.vasp @@ -0,0 +1,12 @@ +System +1.0 +4.376835486482439 0.0086562096165887 7.148251977291244 +2.0211490103296166 3.8822313794698684 7.148251977291244 +0.0142338540946976 0.008656214510917 8.38176620441039 +Bi Sb +3 1 +direct +0.11687114695010013 0.11687114695010009 0.11687114695134818 +0.885057350916569 0.8850573509165686 0.8850573509144881 +0.3806761740317465 0.3806761740317465 0.3806761740305207 +0.6173953281015849 0.6173953281015848 0.6173953281036431 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1372.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1372.vasp new file mode 100644 index 00000000..65873672 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1372.vasp @@ -0,0 +1,10 @@ +System +1.0 +3.5058938597621094 -3.081249e-10 2.0241289627124215 +1.1686312862968669 3.3053879820023613 2.0241289627124215 +-8.715088e-10 -6.162497e-10 4.048256928443838 +Al As +1 1 +direct +0.0 0.0 0.0 +0.24999999999999997 0.25 0.24999999999999997 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14014.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14014.vasp new file mode 100644 index 00000000..40909ae7 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14014.vasp @@ -0,0 +1,20 @@ +System +1.0 +4.157436115454804 -0.0 0.0 +-0.0 7.038494037846648 0.0 +0.0 0.0 7.411178065479046 +Tb Mn Si +4 4 4 +direct +0.25 0.5014870121743014 0.1863876024079978 +0.75 0.49851298782569875 0.8136123975920022 +0.25 0.0014870121743013 0.3136123975920026 +0.75 0.9985129878256985 0.6863876024079979 +0.75 0.8608859093979077 0.06027225572734521 +0.25 0.1391140906020922 0.939727744272655 +0.75 0.3608859093979077 0.43972774427265476 +0.25 0.6391140906020923 0.5602722557273451 +0.25 0.792634730215007 0.8919146343653592 +0.75 0.207365269784993 0.10808536563464112 +0.25 0.2926347302150071 0.6080853656346409 +0.75 0.707365269784993 0.3919146343653588 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14873.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14873.vasp new file mode 100644 index 00000000..cb44176f --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-14873.vasp @@ -0,0 +1,15 @@ +System +1.0 +4.191262576674699 0.0 -0.0 +-0.0 4.191262576674699 0.0 +-0.0 0.0 4.191262576674699 +Sr B +1 6 +direct +0.0 0.0 0.0 +0.2028453684309125 0.5 0.5 +0.5 0.5 0.7971546315690875 +0.5 0.5 0.2028453684309125 +0.5 0.2028453684309125 0.5 +0.5 0.7971546315690875 0.5 +0.7971546315690875 0.5 0.5 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-15345.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-15345.vasp new file mode 100644 index 00000000..8ef52b85 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-15345.vasp @@ -0,0 +1,12 @@ +System +1.0 +3.5666343258756448 0.0 0.0 +0.0 3.60492483256326 -1.0516480500920402 +0.0 0.0043800721536433 3.7551864245512623 +Y Co C +1 1 2 +direct +0.0 0.9970518040927455 0.00294819590736377 +0.5 0.6150401254054609 0.3849598745944179 +0.5 0.15192770422861318 0.5440196337270639 +0.5 0.4559803662731792 0.8480722957711558 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1996.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1996.vasp new file mode 100644 index 00000000..2d521309 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-1996.vasp @@ -0,0 +1,10 @@ +System +1.0 +3.93712543178282 0.0 2.273100275741533 +1.3123751439276066 3.7119571065192623 2.273100275741533 +0.0 0.0 4.546200551483066 +Na I +1 1 +direct +0.0 0.0 0.0 +0.5 0.5 0.5 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-21210.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-21210.vasp new file mode 100644 index 00000000..b9909e2b --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-21210.vasp @@ -0,0 +1,9 @@ +System +1.0 +1.6712283e-08 -2.508029669761222 3.5458136263853106 +-2.172017276374766 1.254014874098203 3.545813646368687 +-2.17201803290572 -1.254014795663004 -3.5458136064019246 +Xe +1 +direct +0.0 0.0 0.0 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-22556.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-22556.vasp new file mode 100644 index 00000000..a6c11dfd --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-22556.vasp @@ -0,0 +1,13 @@ +System +1.0 +3.790914410660539 -0.0 0.0 +0.0 3.790914410660539 0.0 +-0.0 -0.0 3.790914410660539 +Sr Fe O +1 1 3 +direct +0.4999990000000025 0.4999990000000025 0.4999990000000025 +0.0 0.0 0.0 +0.4999990000000025 0.0 0.0 +0.0 0.4999990000000025 0.0 +0.0 0.0 0.4999990000000025 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28397.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28397.vasp new file mode 100644 index 00000000..a6c2ed51 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28397.vasp @@ -0,0 +1,16 @@ +System +1.0 +0.0 -3.9587610833154616 0.0 +-6.655928089533787 0.0 0.0 +0.0 0.0 -23.94045079597872 +Si S +4 4 +direct +0.0 0.0 0.5217263648738928 +0.0 0.5 0.5217263648738928 +0.5 0.0 0.4661843287970869 +0.5 0.5 0.4661843287970869 +0.0524900550457348 0.7500000000000001 0.5792751627500841 +0.9475099449542649 0.25 0.5792751627500841 +0.4474867899417421 0.7500000000000001 0.40863414357892813 +0.5525132100582582 0.25 0.40863414357892813 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28565.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28565.vasp new file mode 100644 index 00000000..99358ee9 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28565.vasp @@ -0,0 +1,17 @@ +System +1.0 +3.3542337275744103 0.0 0.0 +-1.6771168637872051 2.904850045503021 0.0002624763854255 +0.0 0.0027191662067697 29.227006366170723 +Te Mo W Se S +2 2 1 2 2 +direct +0.3332869375319439 0.6665748750638851 0.40880470960271253 +0.3334050587801155 0.6668121175602401 0.2788667649202174 +0.3335186248083656 0.6670362496167267 0.11458088390572692 +0.6666801663591748 0.33335933271835083 0.3438741864905265 +0.3331138284834239 0.6662286569668452 0.5825261819744669 +0.6664959135073409 0.3329918270146898 0.5252624785410095 +0.6664002633646019 0.3327995267292049 0.639695963563112 +0.6669034856871763 0.33380597137434753 0.06276093238425431 +0.6667957214778616 0.3335914429557147 0.1664678986179713 diff --git a/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28634.vasp b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28634.vasp new file mode 100644 index 00000000..d5295f38 --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/POSCAR-JVASP-28634.vasp @@ -0,0 +1,20 @@ +System +1.0 +3.2250494729190726 2.216578e-10 2.163e-13 +-1.6125242360333125 2.7929740932668943 -8.4754501616e-06 +6.6135e-12 -9.7386036979e-05 34.14068378844537 +Mo W Se S +1 3 2 6 +direct +0.33331504683701274 0.6666300936741254 0.0934536896030271 +0.33331656497272827 0.6666341299456554 0.47208556491119213 +0.6666698924990063 0.33333978499784117 0.28247993950367123 +0.6666991220003533 0.3333972440007849 0.6548932577640771 +0.33334341592455036 0.6666888318491291 0.3328984548376194 +0.33332779768550075 0.6666565953711356 0.2320623862553914 +0.33337390316228616 0.6667488063245635 0.7006769649140224 +0.6666482437297062 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diff --git a/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_edos.py b/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_edos.py new file mode 100644 index 00000000..ad29486d --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_edos.py @@ -0,0 +1,22 @@ +from jarvis.db.figshare import data as jdata +from jarvis.core.atoms import Atoms + +dft_3d = jdata("edos_pdos") +max_samples = 50 +f = open("id_multi_prop.csv", "w") +count = 0 +for i in dft_3d: + atoms = Atoms.from_dict(i["atoms"]) + jid = i["jid"] + poscar_name = "POSCAR-" + jid + ".vasp" + # cif_name = jid + ".cif" + target = i["edos_up"] + if target != "na": + target = ",".join(map(str, target)) + atoms.write_poscar(poscar_name) + # atoms.write_cif(cif_name) + f.write("%s,%s\n" % (poscar_name, target)) + count += 1 + if count == max_samples: + break +f.close() diff --git a/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_reg.py b/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_reg.py new file mode 100644 index 00000000..2829b9ac --- /dev/null +++ b/alignn/examples/sample_data_multi_prop/scripts/generate_sample_data_reg.py @@ -0,0 +1,29 @@ +from jarvis.db.figshare import data as jdata +from jarvis.core.atoms import Atoms + +dft_3d = jdata("dft_3d") +props = [ + "optb88vdw_bandgap", + "formation_energy_peratom", + "optb88vdw_total_energy", +] +max_samples = 50 +f = open("id_multi_prop.csv", "w") +count = 0 +for i in dft_3d: + atoms = Atoms.from_dict(i["atoms"]) + jid = i["jid"] + poscar_name = "POSCAR-" + jid + ".vasp" + # cif_name = jid + ".cif" + target = "" + for j in props: + if i[j] != "na": + target += "," + str(i[j]) + if "na" not in target: + atoms.write_poscar(poscar_name) + # atoms.write_cif(cif_name) + f.write("%s%s\n" % (poscar_name, target)) + count += 1 + if count == max_samples: + break +f.close()