def Get_ElasConst(self, filepath):
outcar = Outcar(filepath + "OUTCAR")
outcar.read_elastic_tensor()
self.tensor = np.array(outcar.data["elastic_tensor"]) / 10 #unit in GPa
def elastic_analysis():
filename = 'OUTCAR'
step_count = 1
check_file(filename)
proc_str = "Reading Data From " + filename + " File ..."
procs(proc_str, step_count, sp='-->>')
outcar = Outcar(filename)
outcar.read_elastic_tensor()
cij_tensor = np.array(outcar.data['elastic_tensor'])
filename = 'vasprun.xml'
step_count += 1
check_file(filename)
proc_str = "Reading Data From " + filename + " File ..."
procs(proc_str, step_count, sp='-->>')
vsr = Vasprun(filename)
struct0 = vsr.structures[0]
natom = struct0.num_sites
weight = struct0.composition.weight
volume = struct0.lattice.volume
## converting the units
volume *= 1.0e-30 ## from Angstrom to meters
weight *= weight * 1.0e-3 ## from gram to kg
density = weight / (volume * Avogadro)
asa = analyzer.SpacegroupAnalyzer(struct0)
# lat_type=asa.get_crystal_system()
crys_type = asa.get_lattice_type()
## Redefining the Cij matrix into the correct Voigt notation since VASP's OUTCAR has a different order
## In VASP: Columns and rows are listed as: 1, 2, 3, 6, 4, 5
## In this format OUTCAR's C44 values would be actually C66, C55 would be C44, and C66 would be C55.
## OUTCAR follows the below order:
## [C11 C12 C13 C16 C14 C15]
## [C21 C22 C23 C26 C24 C25]
## [C31 C32 C33 C36 C34 C35]
## [C61 C62 C63 C66 C64 C65]
## [C41 C42 C43 C46 C44 C45]
## [C51 C52 C53 C56 C54 C55]
cnew = np.zeros((6, 6))
snew = np.zeros((6, 6))
cnew = np.copy(cij_tensor)
for j in range(0, 6):
cnew[3][j] = cij_tensor[4][j]
cnew[4][j] = cij_tensor[5][j]
cnew[5][j] = cij_tensor[3][j]
ctemp = np.zeros((6, 6))
ctemp = np.copy(cnew)
for i in range(0, 6):
cnew[i][3] = cnew[i][4]
cnew[i][4] = cnew[i][5]
cnew[i][5] = ctemp[i][3]
# Change the units of Cij from kBar to GPa
cnew = cnew / 10.0
proc_str = "\n Modified elastic tensor in correct order (in GPa units)"
print(proc_str)
fmt = "%7.3f " * 6
for i in range(6):
print(fmt % tuple(cnew[i, :]))
def check_symmetric(a, tol=1e-8):
return np.allclose(a, a.T, atol=tol)
print(
'\n Checking if the elastic tensor is symmetric: i.e. Cij = Cji: %5s'
% check_symmetric(cnew))
print("\n Eigen Values of the elastic tensor:")
evals = LA.eigvals(cnew)
print(fmt % tuple(evals))
if np.all(evals) > 0.0:
print("\n All eigen values are positive indicating elastic stability.")
else:
print(
"\n ATTENTION: One or more eigen values are negative indicating elastic instability."
)
calc_elastic_prop(cnew, snew, crys_type, density, weight, natom)
