def cons_max_volume(self, sites, max_volume, min_volume=1, dimension=3, symprec=1e-5):
"""
parameters:
pcell: Cell object, The primitive cell to be extended
sites: disorderd sites infomation.
yield:
Cell object, a list of non-redundant configurations to max volume supercells.
"""
# 该函数产生所有构型用于确定基态相图
for volume in range(min_volume, max_volume + 1):
hnfs = non_dup_hnfs(self._pcell, volume, dimension, symprec)
dict_trans = {} # 记录已经产生过的snf，相同snf的平移操作相同。
for h in hnfs:
hfpg = PermutationGroup(self._pcell, h)
# 此处的平移操作不必每次重新计算，因为相同snf平移操作相同
# 可以用字典来标记查询。若没有这样的操作，那么就没有snf带来的效率提升。
# quotient = hfpg.get_quotient()
# if not quotient in dict_trans:
# dict_trans[quotient] = hfpg.get_pure_translations(symprec)
# trans = dict_trans[quotient]
# rots = hfpg.get_pure_rotations(symprec)
perms = hfpg.get_symmetry_perms(symprec)
supercell = self._pcell.extend(h)
_sites = numpy.repeat(sites, volume, axis=0)
for mol, _ in remove_redundant(supercell.positions, _sites, perms):
c = Cell(supercell.lattice, mol[0], mol[1])
if c.is_primitive(symprec):
yield c
def test_is_primitive(self):
bcc_latt = [0.5, 0.5, -0.5,
-0.5, 0.5, 0.5,
0.5, -0.5, 0.5]
bcc_pos = [(0, 0, 0)]
bcc_atoms = [0]
bcc_pcell = Cell(bcc_latt, bcc_pos, bcc_atoms)
self.assertTrue(bcc_pcell.is_primitive())
def get_max_volume(pcell, sites, max_volume, min_volume=1, dimension=3, symprec=1e-5):
for volume in range(min_volume, max_volume + 1):
hnfs = non_dup_hnfs(pcell, volume, dimension, symprec)
dict_trans = {} # 记录已经产生过的snf，相同snf的平移操作相同。
for h in hnfs:
hfpg = PG(pcell, h)
perms = hfpg.get_symmetry_perms(symprec)
if dimension == 2:
supercell = pcell.extend(h)._get_niggli_2D()
else:
supercell = pcell.extend(h)._get_niggli_3D()
_sites = np.repeat(sites, volume, axis=0)
for mol, _ in remove_redundant(supercell.positions, _sites, perms):
c = Cell(supercell.lattice, mol[0], mol[1])
if c.is_primitive(symprec):
yield c