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default_parameters.py
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default_parameters.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Jun 26 23:07:24 2023
@author: ZHANG Jun
"""
'''
.. py:data:: config
Configurations for database construction, training process, and prediction behaviors.
'''
import os
import torch.nn as nn
default_elements = ['Ac', 'Ag', 'Al', 'Am', 'Ar', 'As', 'At', 'Au', 'B', 'Ba',
'Be', 'Bh', 'Bi', 'Bk', 'Br', 'C', 'Ca', 'Cd', 'Ce', 'Cf',
'Cl', 'Cm', 'Cn', 'Co', 'Cr', 'Cs', 'Cu', 'Db', 'Ds', 'Dy',
'Er', 'Es', 'Eu', 'F', 'Fe', 'Fl', 'Fm', 'Fr', 'Ga', 'Gd',
'Ge', 'H', 'He', 'Hf', 'Hg', 'Ho', 'Hs', 'I', 'In', 'Ir',
'K', 'Kr', 'La', 'Li', 'Lr', 'Lu', 'Lv', 'Mc', 'Md', 'Mg',
'Mn', 'Mo', 'Mt', 'N', 'Na', 'Nb', 'Nd', 'Ne', 'Nh', 'Ni',
'No', 'Np', 'O', 'Og', 'Os', 'P', 'Pa', 'Pb', 'Pd', 'Pm',
'Po', 'Pr', 'Pt', 'Pu', 'Ra', 'Rb', 'Re', 'Rf', 'Rg', 'Rh',
'Rn', 'Ru', 'S', 'Sb', 'Sc', 'Se', 'Sg', 'Si', 'Sm', 'Sn',
'Sr', 'Ta', 'Tb', 'Tc', 'Te', 'Th', 'Ti', 'Tl', 'Tm', 'Ts',
'U', 'V', 'W', 'Xe', 'Y', 'Yb', 'Zn', 'Zr']
default_build_properties = {'energy': True,
'forces': True,
'cell': True,
'cart_coords': True,
'frac_coords': True,
'constraints': True,
'stress': True,
'distance': True,
'direction': True,
'path': False}
default_data_config = {
'species': default_elements,
'path_file': 'paths.log', # A file of absolute paths where OUTCAR and XDATCAR files exist.
'build_properties': default_build_properties, # Properties needed to be built into graph.
'topology_only': False,
'dataset_path': 'dataset', # Path where the collected data to save.
'mode_of_NN': 'ase_natural_cutoffs', # How to identify connections between atoms. 'ase_natural_cutoffs', 'pymatgen_dist', 'ase_dist', 'voronoi'. Note that pymatgen is much faster than ase.
'cutoff': 5.0, # Cutoff distance to identify connections between atoms. Deprecated if ``mode_of_NN`` is ``'ase_natural_cutoffs'``
'load_from_binary': False, # Read graphs from binary graphs that are constructed before. If this variable is ``True``, these above variables will be depressed.
'num_of_cores': 8,
'super_cell': False,
'has_adsorbate': False,
'keep_readable_structural_files': False,
'mask_similar_frames': False,
'mask_reversed_magnetic_moments': False, # or -0.5 # Frames with atomic magnetic moments lower than this value will be masked.
'energy_stride': 0.05,
'scale_prop': False
}
FIX_VALUE = [1,3,6]
default_train_config = {
'verbose': 1, # `0`: no train and validation output; `1`: Validation and test output; `2`: train, validation, and test output.
'dataset_path': os.path.join('dataset', 'all_graphs.bin'),
'model_save_dir': 'agat_model',
'epochs': 1000,
'output_files': 'out_file',
'device': 'cuda:0',
'validation_size': 0.15,
'test_size': 0.15,
'early_stop': True,
'stop_patience': 300,
'head_list': ['mul', 'div', 'free'],
'gat_node_dim_list': [len(default_elements), 100, 100, 100],
'energy_readout_node_list': [300, 100, 50, 30, 10, 3, FIX_VALUE[0]], # the first value should be: len(head_list)*gat_node_dim_list[-1]
'force_readout_node_list': [300, 100, 50, 30, 10, FIX_VALUE[1]], # the first value should be: len(head_list)*gat_node_dim_list[-1]
'stress_readout_node_list': [300, 100, 50, 30, 10, FIX_VALUE[2]], # the first value should be: len(head_list)*gat_node_dim_list[-1]
'bias': True,
'negative_slope': 0.2,
'criterion': nn.MSELoss(),
'a': 1.0,
'b': 1.0,
'c': 0.0,
# 'optimizer': 'adam',
'learning_rate': 0.0001,
'weight_decay': 0.0, # weight decay (L2 penalty)
'batch_size': 64,
'val_batch_size': 400,
'transfer_learning': False,
'trainable_layers': -4,
'mask_fixed': False,
'tail_readout_no_act': [3,3,3],
# 'adsorbate': False, # or not when building graphs.
'adsorbate_coeff': 20.0, # indentify and specify the importance of adsorbate atoms with respective to surface atoms. zero for equal importance.
'transfer_learning': False}
default_ase_calculator_config = {'fmax' : 0.1,
'steps' : 200,
'maxstep' : 0.05,
'restart' : None,
'restart_steps' : 0,
'perturb_steps' : 0,
'perturb_amplitude': 0.05,
'out' : None}
default_high_throughput_config = {
'model_save_dir': 'agat_model',
'opt_config': default_ase_calculator_config,
'calculation_index' : '0', # sys.argv[1],
'fix_all_surface_atom' : False,
'remove_bottom_atoms' : False,
'save_trajectory' : False,
'partial_fix_adsorbate': True,
'adsorbates' : ['H'],
'sites' : ['ontop'],
'dist_from_surf' : 1.7,
'using_template_bulk_structure': False,
'graph_build_scheme_dir': os.path.join('dataset'),
'device': 'cuda' # in our test results, the A6000 is about * times faster than EPYC 7763.
}
default_predict_config = {}
default_active_learning_config = {}
default_hp_dft_config = {'INCAR_static': '''
SYSTEM = ML
Start parameter for this Run:
ISTART = 0
ICHARG = 2
INIWAV = 1
Electronic Relaxation:
ENCUT = 500
PREC = Accurate
ALGO = Fast
NELM = 300
NELMIN = 4
EDIFF = 1E-06
GGA = PE
LREAL = A
Ionic Relaxation:
EDIFFG = -0.05
NSW = 300
IBRION = 2
ISIF = 3
POTIM = 0.5
DOS related values:
SIGMA = 0.1
ISMEAR = 1
Spin polarized:
ISPIN = 2
MAGMOM = placeholder
File writing
LWAVE = .FALSE.
LCHARG = .FALSE.
Calculation of DOS
NPAR = 8
LORBIT = 11
NCORE = 1
IVDW = 11
''',
'INCAR_aimd': '''
SYSTEM = ML
Start parameter for this Run:
ISTART = 0
ICHARG = 2
INIWAV = 1
Electronic Relaxation:
ENCUT = 500
PREC = Accurate
ALGO = Fast
NELM = 300
NELMIN = 4
EDIFF = 1E-06
GGA = PE
LREAL = A
Ionic Relaxation:
NSW = 100
IBRION = 0
ISIF = 3
POTIM = 2
DOS related values:
SIGMA = 0.1
ISMEAR = 1
Spin polarized:
ISPIN = 2
MAGMOM = placeholder
File writing
LWAVE = .FALSE.
LCHARG = .FALSE.
Calculation of DOS
NPAR = 8
LORBIT = 11
IVDW = 11
MDALGO = 3
SMASS = 0
TEBEG = 300
TEEND = 300
''',
"KPOINTS": '''Automatic mesh
0
Gamma
1 1 1
0.0 0.0 0.0
''',
'calculation_index' : '0', # sys.argv[1],
'adsorbates' : ['H'],
'sites' : ['bridge'],
'dist_from_surf' : 1.7,
'include_bulk_static': True, # This should be true for other calculations.
'include_surface_static': True, # This should be true for other calculations.
'include_adsorption_static': True,
'include_bulk_aimd': True,
'include_surface_aimd': True,
'include_adsorption_aimd': True,
'random_samples': 1, # number of surfaces
'vasp_bash_path': 'vasp_run.sh'
}