def __init__(self, basis='qz', xc='BH'): label = 'H2O' #TODO: for now, change later if xc == 'REVPBE': xc = 'revPBE' fdf_arguments = { 'DM.MixingWeight': 0.3, 'DM.NumberPulay': 3, 'ElectronicTemperature': 5e-3, 'WriteMullikenPop': 0, 'MaxSCFIterations': 40 } if basis == 'uf': super().__init__(label=label, xc='PBE', mesh_cutoff=100 * Ry, energy_shift=0.02 * Ry, basis_set='SZ') dmtol = 5e-4 elif not 'custom' in basis.lower(): super().__init__(label=label, xc=xc, mesh_cutoff=200 * Ry, energy_shift=0.02 * Ry, basis_set=basis.upper()) dmtol = 5e-4 else: species_o = Species(symbol='O', basis_set=PAOBasisBlock( basis_sets['o_basis_{}'.format(basis)])) species_h = Species(symbol='H', basis_set=PAOBasisBlock( basis_sets['h_basis_{}'.format(basis)])) super().__init__(label='H2O', xc=xc, mesh_cutoff=200 * Ry, basis_set='DZP', species=[species_o, species_h], energy_shift=0.02 * Ry) dmtol = 5e-4 fdf_arguments['DM.UseSaveDM'] = 'True' fdf_arguments['SCF.MustConverge'] = 'False' fdf_arguments['DM.Tolerance'] = dmtol fdf_arguments['MLCF.Use'] = False fdf_arguments['SaveDeltaRho'] = True allowed_keys = self.allowed_fdf_keywords allowed_keys['SaveDeltaRho'] = False allowed_keys['SaveRhoXC'] = False allowed_keys['MLCF.Use'] = False allowed_keys['SCF.MustConverge'] = False self.allowed_keywords = allowed_keys self.set_fdf_arguments(fdf_arguments)
def __init__(self, atoms=None, command=None, xc='LDA', spin='non-polarized', ghosts=[], **kwargs): if atoms is not None: finder = DojoFinder() elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys()) elem_dict = dict(zip(elems, range(1, len(elems) + 1))) symbols = atoms.get_chemical_symbols() # ghosts ghost_symbols = [symbols[i] for i in ghosts] ghost_elems = list(dict.fromkeys(ghost_symbols).keys()) tags = [1 if i in ghosts else 0 for i in range(len(atoms))] atoms.set_tags(tags) pseudo_path = finder.get_pp_path(xc=xc) if spin == 'spin-orbit': rel = 'fr' else: rel = 'sr' species = [ Species(symbol=elem, pseudopotential=finder.get_pp_fname(elem, xc=xc, rel=rel), ghost=False) for elem in elem_dict.keys() ] for elem in ghost_elems: species.append( Species(symbol=elem, pseudopotential=finder.get_pp_fname(elem, xc=xc, rel=rel), tag=1, ghost=True)) Siesta.__init__(self, xc=xc, spin=spin, atoms=atoms, pseudo_path=pseudo_path, species=species, **kwargs)
def get_species(atoms, xc, rel='sr'): finder = DojoFinder() elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys()) elem_dict = dict(zip(elems, range(1, len(elems) + 1))) pseudo_path = finder.get_pp_path(xc=xc) species = [ Species(symbol=elem, pseudopotential=finder.get_pp_fname(elem, xc=xc, rel=rel), ghost=False) for elem in elem_dict.keys() ] return pseudo_path, species
def species(self, atoms): """Find all relevant species depending on the atoms object and species input. Parameters : - atoms : An Atoms object. """ # For each element use default species from the species input, or set # up a default species from the general default parameters. symbols = np.array(atoms.get_chemical_symbols()) tags = atoms.get_tags() species = list(self['species']) default_species = [ s for s in species if (s['tag'] is None) and s['symbol'] in symbols] default_symbols = [s['symbol'] for s in default_species] for symbol in symbols: if symbol not in default_symbols: spec = Species(symbol=symbol, basis_set=self['basis_set'], tag=None) default_species.append(spec) default_symbols.append(symbol) assert len(default_species) == len(np.unique(symbols)) # Set default species as the first species. species_numbers = np.zeros(len(atoms), int) i = 1 for spec in default_species: mask = symbols == spec['symbol'] species_numbers[mask] = i i += 1 # Set up the non-default species. non_default_species = [s for s in species if not s['tag'] is None] for spec in non_default_species: mask1 = (tags == spec['tag']) mask2 = (symbols == spec['symbol']) mask = np.logical_and(mask1, mask2) if sum(mask) > 0: species_numbers[mask] = i i += 1 all_species = default_species + non_default_species return all_species, species_numbers
np.array([[0.000000, 0.000000, 0.100000], [0.682793, 0.682793, 0.682793], [-0.682793, -0.682793, 0.68279], [-0.682793, 0.682793, -0.682793], [0.682793, -0.682793, -0.682793]]), cell=[10, 10, 10]) c_basis = """2 nodes 1.00 0 1 S 0.20 P 1 0.20 6.00 5.00 1.00 1 2 S 0.20 P 1 E 0.20 6.00 6.00 5.00 1.00 0.95""" species = Species(symbol='C', basis_set=PAOBasisBlock(c_basis)) calc = Siesta( label='ch4', basis_set='SZ', xc='LYP', mesh_cutoff=300 * Ry, species=[species], restart='ch4.XV', fdf_arguments={ 'DM.Tolerance': 1E-5, 'DM.MixingWeight': 0.15, 'DM.NumberPulay': 3, 'MaxSCFIterations': 200, 'ElectronicTemperature': (0.02585, 'eV'), # 300 K 'SaveElectrostaticPotential': True })
def __init__(self, atoms=None, command=None, xc='LDA', spin='non-polarized', basis_set='DZP', ghosts=[], input_basis_set={}, pseudo_path=None, input_pp={}, pp_accuracy='standard', **kwargs): # non-perturnbative polarized orbital. self.npt_elems = set() if atoms is not None: finder = DojoFinder() elems = list(dict.fromkeys(atoms.get_chemical_symbols()).keys()) self.elem_dict = dict(zip(elems, range(1, len(elems) + 1))) symbols = atoms.get_chemical_symbols() # ghosts ghost_symbols = [symbols[i] for i in ghosts] ghost_elems = list(dict.fromkeys(ghost_symbols).keys()) tags = [1 if i in ghosts else 0 for i in range(len(atoms))] atoms.set_tags(tags) if pseudo_path is None: pseudo_path = finder.get_pp_path(xc=xc, accuracy=pp_accuracy) if spin == 'spin-orbit': rel = 'fr' else: rel = 'sr' species = [] for elem, index in self.elem_dict.items(): if elem not in input_basis_set: bselem = basis_set if elem in ['Li', 'Be', 'Na', 'Mg']: self.npt_elems.add(f"{elem}.{index}") else: bselem = PAOBasisBlock(input_basis_set[elem]) if elem not in input_pp: pseudopotential = finder.get_pp_fname( elem, xc=xc, rel=rel, accuracy=pp_accuracy) else: pseudopotential = os.path.join( pseudo_path, input_pp[elem]) species.append(Species(symbol=elem, pseudopotential=pseudopotential, basis_set=bselem, ghost=False)) for elem in ghost_elems: species.append( Species(symbol=elem, pseudopotential=finder.get_pp_fname( elem, xc=xc, rel=rel, accuracy=pp_accuracy), tag=1, ghost=True)) Siesta.__init__(self, xc=xc, spin=spin, atoms=atoms, pseudo_path=pseudo_path, species=species, **kwargs) self.set_npt_elements()
# Test setting fdf-arguments after initiation. siesta.set_fdf_arguments( {'DM.Tolerance': 1e-2, 'ON.eta': (2, 'Ry')}) siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() assert 'MeshCutoff\t3000\teV\n' in lines assert 'DM.Tolerance\t0.01\n' in lines assert 'ON.eta\t2\tRy\n' in lines # Test initiation using Species. atoms = ch4.copy() species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) siesta = Siesta(species=[Species(symbol='C', tag=1)]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) atoms.set_tags([0, 0, 0, 1, 0]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) siesta = Siesta(species=[Species(symbol='H', tag=1, basis_set='SZ')]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 3, 2])) siesta = Siesta(label='test_label', species=species) siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() lines = [line.split() for line in lines] assert ['1', '6', 'C.lda.1'] in lines assert ['2', '1', 'H.lda.2'] in lines
# In this script the Virtual Crystal approximation is used to model # a stronger affinity for positive charge on the H atoms. # This could model interaction with other molecules not explicitly # handled. import numpy as np from ase.calculators.siesta import Siesta from ase.calculators.siesta.parameters import Species from ase.optimize import QuasiNewton from ase import Atoms atoms = Atoms('CH4', np.array([ [0.000000, 0.000000, 0.000000], [0.682793, 0.682793, 0.682793], [-0.682793, -0.682793, 0.682790], [-0.682793, 0.682793, -0.682793], [0.682793, -0.682793, -0.682793]])) siesta = Siesta( species=[ Species(symbol='H', excess_charge=0.1)]) atoms.calc = siesta dyn = QuasiNewton(atoms, trajectory='h.traj') dyn.run(fmax=0.02)
def test_siesta(siesta_factory): # Setup test structures. h = Atoms('H', [(0.0, 0.0, 0.0)]) ch4 = Atoms( 'CH4', np.array([[0.000000, 0.000000, 0.000000], [0.682793, 0.682793, 0.682793], [-0.682793, -0.682793, 0.682790], [-0.682793, 0.682793, -0.682793], [0.682793, -0.682793, -0.682793]])) # Test the initialization. siesta = siesta_factory.calc() assert isinstance(siesta, FileIOCalculator) assert isinstance(siesta.implemented_properties, tuple) assert isinstance(siesta.default_parameters, dict) assert isinstance(siesta.name, str) assert isinstance(siesta.default_parameters, dict) # Test simple fdf-argument case. atoms = h.copy() siesta = siesta_factory.calc(label='test_label', fdf_arguments={'DM.Tolerance': 1e-3}) atoms.calc = siesta siesta.write_input(atoms, properties=['energy']) atoms = h.copy() atoms.calc = siesta siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as fd: lines = fd.readlines() assert any([line.split() == ['DM.Tolerance', '0.001'] for line in lines]) # Test (slightly) more complex case of setting fdf-arguments. siesta = siesta_factory.calc(label='test_label', mesh_cutoff=3000, fdf_arguments={ 'DM.Tolerance': 1e-3, 'ON.eta': (5, 'Ry') }) atoms.calc = siesta siesta.write_input(atoms, properties=['energy']) atoms = h.copy() atoms.calc = siesta siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() assert 'MeshCutoff\t3000\teV\n' in lines assert 'DM.Tolerance\t0.001\n' in lines assert 'ON.eta\t5\tRy\n' in lines # Test setting fdf-arguments after initiation. siesta.set_fdf_arguments({'DM.Tolerance': 1e-2, 'ON.eta': (2, 'Ry')}) siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() assert 'MeshCutoff\t3000\teV\n' in lines assert 'DM.Tolerance\t0.01\n' in lines assert 'ON.eta\t2\tRy\n' in lines # Test initiation using Species. atoms = ch4.copy() species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) siesta = siesta_factory.calc(species=[Species(symbol='C', tag=1)]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) atoms.set_tags([0, 0, 0, 1, 0]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 2, 2])) siesta = siesta_factory.calc( species=[Species(symbol='H', tag=1, basis_set='SZ')]) species, numbers = siesta.species(atoms) assert all(numbers == np.array([1, 2, 2, 3, 2])) siesta = siesta_factory.calc(label='test_label', species=species) siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() lines = [line.split() for line in lines] assert ['1', '6', 'C.lda.1'] in lines assert ['2', '1', 'H.lda.2'] in lines assert ['3', '1', 'H.lda.3'] in lines assert ['C.lda.1', 'DZP'] in lines assert ['H.lda.2', 'DZP'] in lines assert ['H.lda.3', 'SZ'] in lines # Test if PAO block can be given as species. c_basis = """2 nodes 1.00 0 1 S 0.20 P 1 0.20 6.00 5.00 1.00 1 2 S 0.20 P 1 E 0.20 6.00 6.00 5.00 1.00 0.95""" basis_set = PAOBasisBlock(c_basis) species = Species(symbol='C', basis_set=basis_set) siesta = siesta_factory.calc(label='test_label', species=[species]) siesta.write_input(atoms, properties=['energy']) with open('test_label.fdf', 'r') as f: lines = f.readlines() lines = [line.split() for line in lines] assert ['%block', 'PAO.Basis'] in lines assert ['%endblock', 'PAO.Basis'] in lines
from ase.calculators.siesta import Siesta from ase.calculators.siesta.parameters import Species, PAOBasisBlock from ase.optimize import QuasiNewton from ase import Atoms atoms = Atoms('3H', [(0.0, 0.0, 0.0), (0.0, 0.0, 0.5), (0.0, 0.0, 1.0)], cell=[10, 10, 10]) basis_set = PAOBasisBlock("""1 0 2 S 0.2 0.0 0.0""") atoms.set_tags([0, 1, 0]) siesta = Siesta(species=[ Species(symbol='H', tag=None, basis_set='SZ'), Species(symbol='H', tag=1, basis_set=basis_set, ghost=True) ]) atoms.set_calculator(siesta) dyn = QuasiNewton(atoms, trajectory='h.traj') dyn.run(fmax=0.02)