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gen_example.py
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gen_example.py
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import os
import sys
import numpy as np
from pymatgen.core.structure import Structure
assert len(sys.argv) == 6
openmx_path = sys.argv[1]
openmx_overlap_path = sys.argv[2]
pot_path = sys.argv[3]
python_interpreter = sys.argv[4]
julia_interpreter = sys.argv[5]
os.makedirs('../example', exist_ok=True)
os.makedirs('../example/1_DFT_calculation', exist_ok=True)
save_str = """Bi4
1.00000000000000
4.3664811210045835 -0.0000000000000001 0.0000000000000000
-2.1832405605022918 3.7814838764134606 0.0000000000000000
0.0000000000000000 0.0000000000000000 19.0000000000000000
Bi
4
Direct
0.3333330000000032 0.6666669999999968 0.2730790361311790
0.0000000000000000 0.0000000000000000 0.3637838957355797
0.3333330000000032 0.6666669999999968 0.5324481042644251
0.0000000000000000 0.0000000000000000 0.6231529638688187
"""
with open('../example/1_DFT_calculation/POSCAR', 'w') as save_f:
save_f.write(save_str)
os.makedirs('../example/work_dir/dataset/raw', exist_ok=True)
np.random.seed(42)
stru_unit = Structure.from_file('../example/1_DFT_calculation/POSCAR')
shifts_a, shifts_b = np.linspace(0, 1, 25)[0:-1], np.linspace(0, 1, 25)[:-1]
shift_list = [[shift_a, shift_b] for shift_a in shifts_a for shift_b in shifts_b]
for shift_index, shift in enumerate(shift_list):
unit_frac_coords = stru_unit.frac_coords
unit_frac_coords[2:, 0] += shift[0]
unit_frac_coords[2:, 1] += shift[1]
stru_shift = Structure(
stru_unit.lattice,
stru_unit.species,
unit_frac_coords,
coords_are_cartesian=False,
to_unit_cell=True)
stru_shift.make_supercell([[3, 0, 0], [0, 3, 0], [0, 0, 1]])
super_cart_coords = stru_shift.cart_coords
pert_x = (np.random.rand(len(stru_shift), 1) - 0.5) * (0.1 * 2)
pert_y = (np.random.rand(len(stru_shift), 1) - 0.5) * (0.1 * 2)
pert_z = (np.random.rand(len(stru_shift), 1) - 0.5) * (0.1 * 2)
super_cart_coords += np.concatenate([pert_x, pert_y, pert_z], axis=-1)
stru_shift_pert = Structure(
stru_shift.lattice,
stru_shift.species,
super_cart_coords,
coords_are_cartesian=True,
to_unit_cell=True)
frac_coords = stru_shift_pert.frac_coords
frac_coords_str = ''
for i in range(len(stru_shift)):
frac_coords_str += f' {i + 1} Bi {frac_coords[i, 0]} {frac_coords[i, 1]} {frac_coords[i, 2]} 7.5 7.5 0.0 0.0 0.0 0.0 0 '
if i != len(stru_shift) - 1:
frac_coords_str += '\n'
save_str = f"""System.Name openmx
DATA.PATH {pot_path}
HS.fileout on
Species.Number 1
<Definition.of.Atomic.Species
Bi Bi8.0-s3p2d2 Bi_PBE19
Definition.of.Atomic.Species>
Atoms.Number 36
Atoms.SpeciesAndCoordinates.Unit FRAC
<Atoms.SpeciesAndCoordinates
{frac_coords_str}
Atoms.SpeciesAndCoordinates>
Atoms.UnitVectors.Unit Ang
<Atoms.UnitVectors
13.0994433630137515 -0.0000000000000003 0.0000000000000000
-6.5497216815068757 11.3444516292403819 0.0000000000000000
0.0000000000000000 0.0000000000000000 19.0000000000000000
Atoms.UnitVectors>
scf.XcType GGA-PBE # LDA/LSDA-CA/LSDA-PW/GGA-PBE
scf.ElectronicTemperature 300.0 # default=300 (K) SIGMA in VASP
scf.energycutoff 300 # default=150 (Ry = 13.6eV)
scf.maxIter 2000
scf.EigenvalueSolver Band # DC/DC-LNO/Krylov/ON2/Cluster/Band
scf.Kgrid 5 5 1
scf.criterion 4e-08 # (Hartree = 27.21eV)
scf.partialCoreCorrection on
scf.SpinPolarization nc
scf.SpinOrbit.Coupling on
scf.Mixing.Type RMM-DIISK
scf.Init.Mixing.Weight 0.3
scf.Mixing.History 30
scf.Mixing.StartPulay 6
scf.Mixing.EveryPulay 1
1DFFT.EnergyCutoff 3600
1DFFT.NumGridK 900
1DFFT.NumGridR 900
scf.ProExpn.VNA off
MD.Type Nomd # Nomd (SCF) / NVT_NH (MD)
### END ###
"""
os.makedirs(f'../example/work_dir/dataset/raw/{shift_index}', exist_ok=True)
with open(f'../example/work_dir/dataset/raw/{shift_index}/openmx_in.dat', 'w') as save_f:
save_f.write(save_str)
stru_shift_pert.to(fmt='poscar', filename=f'../example/work_dir/dataset/raw/{shift_index}/POSCAR')
save_str = """cd ../work_dir/dataset/raw/0
for i in {0..575}; do
cd ../$i
mpirun -np 64 %s openmx_in.dat > openmx.std
cat openmx.out >> openmx.scfout
rm -r openmx_rst *.cube
done
""" % openmx_path
with open('../example/1_DFT_calculation/run.sh', 'w') as save_f:
save_f.write(save_str)
os.makedirs('../example/2_preprocess', exist_ok=True)
save_str = f"""[basic]
raw_dir = {os.path.abspath("../example/work_dir/dataset/raw")}
processed_dir = {os.path.abspath("../example/work_dir/dataset/processed")}
target = hamiltonian
interface = openmx
[interpreter]
python_interpreter = {python_interpreter}
julia_interpreter = {julia_interpreter}
[graph]
radius = 9.0
create_from_DFT = True
"""
with open('../example/2_preprocess/preprocess.ini', 'w') as save_f:
save_f.write(save_str)
save_str = """deeph-preprocess --config preprocess.ini
"""
with open('../example/2_preprocess/run.sh', 'w') as save_f:
save_f.write(save_str)
os.makedirs('../example/3_train', exist_ok=True)
save_str = """[basic]
graph_dir = %s
save_dir = %s
raw_dir = %s
dataset_name = Bi_soc
device = cuda:0
num_threads = 64
save_to_time_folder = False
save_csv = False
seed = 42
orbital = [{"83 83": [0, 0]}, {"83 83": [0, 1]}, {"83 83": [0, 2]}, {"83 83": [0, 3]}, {"83 83": [0, 4]}, {"83 83": [0, 5]}, {"83 83": [0, 6]}, {"83 83": [0, 7]}, {"83 83": [0, 8]}, {"83 83": [0, 9]}, {"83 83": [0, 10]}, {"83 83": [0, 11]}, {"83 83": [0, 12]}, {"83 83": [0, 13]}, {"83 83": [0, 14]}, {"83 83": [0, 15]}, {"83 83": [0, 16]}, {"83 83": [0, 17]}, {"83 83": [0, 18]}, {"83 83": [1, 0]}, {"83 83": [1, 1]}, {"83 83": [1, 2]}, {"83 83": [1, 3]}, {"83 83": [1, 4]}, {"83 83": [1, 5]}, {"83 83": [1, 6]}, {"83 83": [1, 7]}, {"83 83": [1, 8]}, {"83 83": [1, 9]}, {"83 83": [1, 10]}, {"83 83": [1, 11]}, {"83 83": [1, 12]}, {"83 83": [1, 13]}, {"83 83": [1, 14]}, {"83 83": [1, 15]}, {"83 83": [1, 16]}, {"83 83": [1, 17]}, {"83 83": [1, 18]}, {"83 83": [2, 0]}, {"83 83": [2, 1]}, {"83 83": [2, 2]}, {"83 83": [2, 3]}, {"83 83": [2, 4]}, {"83 83": [2, 5]}, {"83 83": [2, 6]}, {"83 83": [2, 7]}, {"83 83": [2, 8]}, {"83 83": [2, 9]}, {"83 83": [2, 10]}, {"83 83": [2, 11]}, {"83 83": [2, 12]}, {"83 83": [2, 13]}, {"83 83": [2, 14]}, {"83 83": [2, 15]}, {"83 83": [2, 16]}, {"83 83": [2, 17]}, {"83 83": [2, 18]}, {"83 83": [3, 0]}, {"83 83": [3, 1]}, {"83 83": [3, 2]}, {"83 83": [3, 3]}, {"83 83": [3, 4]}, {"83 83": [3, 5]}, {"83 83": [3, 6]}, {"83 83": [3, 7]}, {"83 83": [3, 8]}, {"83 83": [3, 9]}, {"83 83": [3, 10]}, {"83 83": [3, 11]}, {"83 83": [3, 12]}, {"83 83": [3, 13]}, {"83 83": [3, 14]}, {"83 83": [3, 15]}, {"83 83": [3, 16]}, {"83 83": [3, 17]}, {"83 83": [3, 18]}, {"83 83": [4, 0]}, {"83 83": [4, 1]}, {"83 83": [4, 2]}, {"83 83": [4, 3]}, {"83 83": [4, 4]}, {"83 83": [4, 5]}, {"83 83": [4, 6]}, {"83 83": [4, 7]}, {"83 83": [4, 8]}, {"83 83": [4, 9]}, {"83 83": [4, 10]}, {"83 83": [4, 11]}, {"83 83": [4, 12]}, {"83 83": [4, 13]}, {"83 83": [4, 14]}, {"83 83": [4, 15]}, {"83 83": [4, 16]}, {"83 83": [4, 17]}, {"83 83": [4, 18]}, {"83 83": [5, 0]}, {"83 83": [5, 1]}, {"83 83": [5, 2]}, {"83 83": [5, 3]}, {"83 83": [5, 4]}, {"83 83": [5, 5]}, {"83 83": [5, 6]}, {"83 83": [5, 7]}, {"83 83": [5, 8]}, {"83 83": [5, 9]}, {"83 83": [5, 10]}, {"83 83": [5, 11]}, {"83 83": [5, 12]}, {"83 83": [5, 13]}, {"83 83": [5, 14]}, {"83 83": [5, 15]}, {"83 83": [5, 16]}, {"83 83": [5, 17]}, {"83 83": [5, 18]}, {"83 83": [6, 0]}, {"83 83": [6, 1]}, {"83 83": [6, 2]}, {"83 83": [6, 3]}, {"83 83": [6, 4]}, {"83 83": [6, 5]}, {"83 83": [6, 6]}, {"83 83": [6, 7]}, {"83 83": [6, 8]}, {"83 83": [6, 9]}, {"83 83": [6, 10]}, {"83 83": [6, 11]}, {"83 83": [6, 12]}, {"83 83": [6, 13]}, {"83 83": [6, 14]}, {"83 83": [6, 15]}, {"83 83": [6, 16]}, {"83 83": [6, 17]}, {"83 83": [6, 18]}, {"83 83": [7, 0]}, {"83 83": [7, 1]}, {"83 83": [7, 2]}, {"83 83": [7, 3]}, {"83 83": [7, 4]}, {"83 83": [7, 5]}, {"83 83": [7, 6]}, {"83 83": [7, 7]}, {"83 83": [7, 8]}, {"83 83": [7, 9]}, {"83 83": [7, 10]}, {"83 83": [7, 11]}, {"83 83": [7, 12]}, {"83 83": [7, 13]}, {"83 83": [7, 14]}, {"83 83": [7, 15]}, {"83 83": [7, 16]}, {"83 83": [7, 17]}, {"83 83": [7, 18]}, {"83 83": [8, 0]}, {"83 83": [8, 1]}, {"83 83": [8, 2]}, {"83 83": [8, 3]}, {"83 83": [8, 4]}, {"83 83": [8, 5]}, {"83 83": [8, 6]}, {"83 83": [8, 7]}, {"83 83": [8, 8]}, {"83 83": [8, 9]}, {"83 83": [8, 10]}, {"83 83": [8, 11]}, {"83 83": [8, 12]}, {"83 83": [8, 13]}, {"83 83": [8, 14]}, {"83 83": [8, 15]}, {"83 83": [8, 16]}, {"83 83": [8, 17]}, {"83 83": [8, 18]}, {"83 83": [9, 0]}, {"83 83": [9, 1]}, {"83 83": [9, 2]}, {"83 83": [9, 3]}, {"83 83": [9, 4]}, {"83 83": [9, 5]}, {"83 83": [9, 6]}, {"83 83": [9, 7]}, {"83 83": [9, 8]}, {"83 83": [9, 9]}, {"83 83": [9, 10]}, {"83 83": [9, 11]}, {"83 83": [9, 12]}, {"83 83": [9, 13]}, {"83 83": [9, 14]}, {"83 83": [9, 15]}, {"83 83": [9, 16]}, {"83 83": [9, 17]}, {"83 83": [9, 18]}, {"83 83": [10, 0]}, {"83 83": [10, 1]}, {"83 83": [10, 2]}, {"83 83": [10, 3]}, {"83 83": [10, 4]}, {"83 83": [10, 5]}, {"83 83": [10, 6]}, {"83 83": [10, 7]}, {"83 83": [10, 8]}, {"83 83": [10, 9]}, {"83 83": [10, 10]}, {"83 83": [10, 11]}, {"83 83": [10, 12]}, {"83 83": [10, 13]}, {"83 83": [10, 14]}, {"83 83": [10, 15]}, {"83 83": [10, 16]}, {"83 83": [10, 17]}, {"83 83": [10, 18]}, {"83 83": [11, 0]}, {"83 83": [11, 1]}, {"83 83": [11, 2]}, {"83 83": [11, 3]}, {"83 83": [11, 4]}, {"83 83": [11, 5]}, {"83 83": [11, 6]}, {"83 83": [11, 7]}, {"83 83": [11, 8]}, {"83 83": [11, 9]}, {"83 83": [11, 10]}, {"83 83": [11, 11]}, {"83 83": [11, 12]}, {"83 83": [11, 13]}, {"83 83": [11, 14]}, {"83 83": [11, 15]}, {"83 83": [11, 16]}, {"83 83": [11, 17]}, {"83 83": [11, 18]}, {"83 83": [12, 0]}, {"83 83": [12, 1]}, {"83 83": [12, 2]}, {"83 83": [12, 3]}, {"83 83": [12, 4]}, {"83 83": [12, 5]}, {"83 83": [12, 6]}, {"83 83": [12, 7]}, {"83 83": [12, 8]}, {"83 83": [12, 9]}, {"83 83": [12, 10]}, {"83 83": [12, 11]}, {"83 83": [12, 12]}, {"83 83": [12, 13]}, {"83 83": [12, 14]}, {"83 83": [12, 15]}, {"83 83": [12, 16]}, {"83 83": [12, 17]}, {"83 83": [12, 18]}, {"83 83": [13, 0]}, {"83 83": [13, 1]}, {"83 83": [13, 2]}, {"83 83": [13, 3]}, {"83 83": [13, 4]}, {"83 83": [13, 5]}, {"83 83": [13, 6]}, {"83 83": [13, 7]}, {"83 83": [13, 8]}, {"83 83": [13, 9]}, {"83 83": [13, 10]}, {"83 83": [13, 11]}, {"83 83": [13, 12]}, {"83 83": [13, 13]}, {"83 83": [13, 14]}, {"83 83": [13, 15]}, {"83 83": [13, 16]}, {"83 83": [13, 17]}, {"83 83": [13, 18]}, {"83 83": [14, 0]}, {"83 83": [14, 1]}, {"83 83": [14, 2]}, {"83 83": [14, 3]}, {"83 83": [14, 4]}, {"83 83": [14, 5]}, {"83 83": [14, 6]}, {"83 83": [14, 7]}, {"83 83": [14, 8]}, {"83 83": [14, 9]}, {"83 83": [14, 10]}, {"83 83": [14, 11]}, {"83 83": [14, 12]}, {"83 83": [14, 13]}, {"83 83": [14, 14]}, {"83 83": [14, 15]}, {"83 83": [14, 16]}, {"83 83": [14, 17]}, {"83 83": [14, 18]}, {"83 83": [15, 0]}, {"83 83": [15, 1]}, {"83 83": [15, 2]}, {"83 83": [15, 3]}, {"83 83": [15, 4]}, {"83 83": [15, 5]}, {"83 83": [15, 6]}, {"83 83": [15, 7]}, {"83 83": [15, 8]}, {"83 83": [15, 9]}, {"83 83": [15, 10]}, {"83 83": [15, 11]}, {"83 83": [15, 12]}, {"83 83": [15, 13]}, {"83 83": [15, 14]}, {"83 83": [15, 15]}, {"83 83": [15, 16]}, {"83 83": [15, 17]}, {"83 83": [15, 18]}, {"83 83": [16, 0]}, {"83 83": [16, 1]}, {"83 83": [16, 2]}, {"83 83": [16, 3]}, {"83 83": [16, 4]}, {"83 83": [16, 5]}, {"83 83": [16, 6]}, {"83 83": [16, 7]}, {"83 83": [16, 8]}, {"83 83": [16, 9]}, {"83 83": [16, 10]}, {"83 83": [16, 11]}, {"83 83": [16, 12]}, {"83 83": [16, 13]}, {"83 83": [16, 14]}, {"83 83": [16, 15]}, {"83 83": [16, 16]}, {"83 83": [16, 17]}, {"83 83": [16, 18]}, {"83 83": [17, 0]}, {"83 83": [17, 1]}, {"83 83": [17, 2]}, {"83 83": [17, 3]}, {"83 83": [17, 4]}, {"83 83": [17, 5]}, {"83 83": [17, 6]}, {"83 83": [17, 7]}, {"83 83": [17, 8]}, {"83 83": [17, 9]}, {"83 83": [17, 10]}, {"83 83": [17, 11]}, {"83 83": [17, 12]}, {"83 83": [17, 13]}, {"83 83": [17, 14]}, {"83 83": [17, 15]}, {"83 83": [17, 16]}, {"83 83": [17, 17]}, {"83 83": [17, 18]}, {"83 83": [18, 0]}, {"83 83": [18, 1]}, {"83 83": [18, 2]}, {"83 83": [18, 3]}, {"83 83": [18, 4]}, {"83 83": [18, 5]}, {"83 83": [18, 6]}, {"83 83": [18, 7]}, {"83 83": [18, 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[graph]
create_from_DFT = True
[train]
epochs = 10000
train_ratio = 0.6
val_ratio = 0.2
test_ratio = 0.2
revert_then_decay = True
revert_decay_epoch = [250, 2500]
revert_decay_gamma = [0.2, 0.5]
[hyperparameter]
batch_size = 1
learning_rate = 0.001
[network]
gauss_stop = 8.5
""" % (os.path.abspath("../example/work_dir/dataset/graph"), os.path.abspath("../example/work_dir/trained_model"), os.path.abspath("../example/work_dir/dataset/processed"))
with open('../example/3_train/train.ini', 'w') as save_f:
save_f.write(save_str)
save_str = """deeph-train --config train.ini
"""
with open('../example/3_train/run.sh', 'w') as save_f:
save_f.write(save_str)
os.makedirs('../example/4_compute_overlap', exist_ok=True)
os.makedirs('../example/work_dir/olp/5_4', exist_ok=True)
save_str = """Bi244
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"""
with open('../example/work_dir/olp/5_4/POSCAR', 'w') as save_f:
save_f.write(save_str)
save_str = f"""System.Name openmx
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Atoms.UnitVectors.Unit Ang
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28.3821289999999991 18.9074199999999983 0.0000000000000000
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Atoms.UnitVectors>
scf.XcType GGA-PBE # LDA/LSDA-CA/LSDA-PW/GGA-PBE
scf.ElectronicTemperature 300.0 # default=300 (K) SIGMA in VASP
scf.energycutoff 300 # default=150 (Ry = 13.6eV)
scf.maxIter 2000
scf.EigenvalueSolver Band # DC/DC-LNO/Krylov/ON2/Cluster/Band
scf.Kgrid 2 2 1
scf.criterion 4e-08 # (Hartree = 27.21eV)
scf.partialCoreCorrection on
scf.SpinPolarization nc
scf.SpinOrbit.Coupling on
scf.Mixing.Type RMM-DIISK
scf.Init.Mixing.Weight 0.3
scf.Mixing.History 30
scf.Mixing.StartPulay 6
scf.Mixing.EveryPulay 1
1DFFT.EnergyCutoff 3600
1DFFT.NumGridK 900
1DFFT.NumGridR 900
scf.ProExpn.VNA off
MD.Type Nomd # Nomd (SCF) / NVT_NH (MD)
Band.dispersion on
Band.Nkpath 3
<Band.kpath
20 0.666666667 0.333333333 0.000 0.000 0.000 0.000 K \Gamma
20 0.000 0.000 0.000 0.500 0.000 0.000 \Gamma M
15 0.500 0.000 0.000 0.333333333 -0.333333333 0.000 M K'
Band.kpath>
### END ###
"""
with open('../example/work_dir/olp/5_4/openmx_in.dat', 'w') as save_f:
save_f.write(save_str)
save_str = f"""cd "{os.path.abspath("../example/work_dir/olp/5_4")}"
mkdir output
mpirun -np 16 {openmx_overlap_path} openmx_in.dat > openmx.std
"""
with open('../example/4_compute_overlap/run.sh', 'w') as save_f:
save_f.write(save_str)
os.makedirs('../example/5_inference', exist_ok=True)
os.makedirs('../example/work_dir/inference/5_4', exist_ok=True)
save_str = f"""[basic]
OLP_dir = {os.path.abspath("../example/work_dir/olp/5_4/")}
work_dir = {os.path.abspath("../example/work_dir/inference/5_4/")}
structure_file_name = POSCAR
task = [1, 2, 3, 4, 5]
sparse_calc_config = {os.path.abspath("../example/work_dir/inference/5_4/band.json")}
trained_model_dir = {os.path.abspath("../example/work_dir/trained_model")}
restore_blocks_py = True
[interpreter]
julia_interpreter = {julia_interpreter}
[graph]
radius = 9.0
create_from_DFT = True
"""
with open('../example/5_inference/inference.ini', 'w') as save_f:
save_f.write(save_str)
save_str = """{
"calc_job": "band",
"which_k": 0,
"fermi_level": -3.594117550337986,
"max_iter": 300,
"num_band": 50,
"k_data": ["20 0.3333333333333333 0.6666666666666667 0 0 0 0 K Γ", "20 0 0 0 0.5 0.5 0 Γ M", "15 0.5 0.5 0 0.6666666666666667 0.3333333333333333 0 M K'"]
}"""
with open('../example/work_dir/inference/5_4/band.json', 'w') as save_f:
save_f.write(save_str)
save_str = f"""deeph-inference --config inference.ini
"""
with open('../example/5_inference/run.sh', 'w') as save_f:
save_f.write(save_str)