Interoperability with Atomic Simulation Environment
quippy uses the standard
ase.atoms.Atomsclass to represent Atomic structuresquippy
Potentialobjects can be used as ASE calculators, and vice-versaCan use standard ASE tools, plus communicate with other packages using ASE as lingua franca
Example: vacancy formation energy
Generate structure with
ASElattice toolsStillinger-Weber potential implementation from
QUIPElastic constant fitting routine from
matscipy, internal relaxations withASEFIRE minimiser
%pylab inline
from ase.build import bulk
from ase.optimize import BFGS
from ase.optimize.precon import PreconLBFGS
from quippy.potential import Potential
si = bulk('Si', a=5.44, cubic=True)
sw_pot = Potential('IP SW',
param_filename='../../share/Parameters/ip.parms.SW.xml')
si.set_calculator(sw_pot)
e_bulk_per_atom = si.get_potential_energy()/len(si)
# call general purpose elastic constants calculator
# using ASE Atoms and QUIP Potential
from matscipy.elasticity import fit_elastic_constants
Cij = fit_elastic_constants(si, optimizer=BFGS,
symmetry='cubic', logfile='-')
vac1 = si.copy()
vac1 *= (3, 3, 3)
half_cell = np.diag(vac1.cell)/2.
vac_atom = ((vac1.positions - half_cell)**2).sum(axis=1).argmin()
del vac1[vac_atom]
vac1.set_calculator(sw_pot)
vac1.rattle(0.01)
opt = PreconLBFGS(vac1) # big cell, use preconditioned minimiser
opt.run(fmax=1e-6)
e_vac = vac1.get_potential_energy() - e_bulk_per_atom*len(vac1)
print('SW vacancy formation energy', e_vac, 'eV')
Populating the interactive namespace from numpy and matplotlib
Step Time Energy fmax
BFGS: 0 21:22:10 -34.635777 0.3247
BFGS: 1 21:22:10 -34.644590 0.1504
BFGS: 2 21:22:10 -34.646997 0.0001
Step Time Energy fmax
BFGS: 0 21:22:10 -34.670667 0.1584
BFGS: 1 21:22:10 -34.672777 0.0749
BFGS: 2 21:22:10 -34.673385 0.0000
Step Time Energy fmax
BFGS: 0 21:22:10 -34.678737 0.0000
Step Time Energy fmax
BFGS: 0 21:22:10 -34.660845 0.1508
BFGS: 1 21:22:10 -34.662784 0.0742
BFGS: 2 21:22:10 -34.663403 0.0000
Step Time Energy fmax
BFGS: 0 21:22:10 -34.617822 0.2945
BFGS: 1 21:22:10 -34.625262 0.1477
BFGS: 2 21:22:10 -34.627763 0.0001
Fitting C_11
Strain array([-0.02, -0.01, 0. , 0.01, 0.02])
Stress array([-2.56044687, -1.01247671, 0.5027424 , 1.98366491, 3.42893711]) GPa
Cij (gradient) / GPa : 149.74909575669915
Error in Cij / GPa : 1.1696996170085603
Correlation coefficient : 0.9999084935045536
Setting C11 (1) to 0.934660 +/- 0.007301
Fitting C_21
Strain array([-0.02, -0.01, 0. , 0.01, 0.02])
Stress array([-1.07663577, -0.26643655, 0.5027424 , 1.23345414, 1.92818198]) GPa
Cij (gradient) / GPa : 75.0952617697293
Error in Cij / GPa : 1.3149075235415504
Correlation coefficient : 0.9995404242109733
Setting C21 (7) to 0.468708 +/- 0.008207
Fitting C_31
Strain array([-0.02, -0.01, 0. , 0.01, 0.02])
Stress array([-1.07663577, -0.26643655, 0.5027424 , 1.23345414, 1.92818198]) GPa
Cij (gradient) / GPa : 75.09526176972929
Error in Cij / GPa : 1.31490752354155
Correlation coefficient : 0.9995404242109733
Updating C31 (7) with value 0.468708 +/- 0.008207
Fitting C_44
Strain array([-0.02, -0.01, 0. , 0.01, 0.02])
Stress array([-1.13572340e+00, -5.65842409e-01, -9.46072689e-15, 5.60142655e-01,
1.11304586e+00]) GPa
Cij (gradient) / GPa : 56.23523568430933
Error in Cij / GPa : 0.19437884854805132
Correlation coefficient : 0.9999820790695022
Setting C44 (4) to 0.350993 +/- 0.001213
[[b C11 b C12 b C12 b b b ]
[b C12 b C11 b C12 b b b ]
[b C12 b C12 b C11 b b b ]
[b b b b C44 b b ]
[b b b b b C44 b ]
[b b b b b b C44]]
=
[[149.75 75.1 75.1 0. 0. 0. ]
[ 75.1 149.75 75.1 0. 0. 0. ]
[ 75.1 75.1 149.75 0. 0. 0. ]
[ 0. 0. 0. 56.24 0. 0. ]
[ 0. 0. 0. 0. 56.24 0. ]
[ 0. 0. 0. 0. 0. 56.24]]
C_11 = 149.75 +/- 1.17 GPa
C_12 = 75.10 +/- 1.31 GPa
C_44 = 56.24 +/- 0.19 GPa
PreconLBFGS: 0 21:22:10 -927.087471 0.8332
estimate_mu(): mu=2.3315998829549316, mu_c=1.0
PreconLBFGS: 1 21:22:10 -927.611735 0.1487
PreconLBFGS: 2 21:22:11 -927.643943 0.0642
PreconLBFGS: 3 21:22:11 -927.652380 0.0393
PreconLBFGS: 4 21:22:11 -927.655827 0.0210
PreconLBFGS: 5 21:22:11 -927.656988 0.0092
PreconLBFGS: 6 21:22:11 -927.657131 0.0052
PreconLBFGS: 7 21:22:11 -927.657193 0.0030
PreconLBFGS: 8 21:22:11 -927.657220 0.0011
PreconLBFGS: 9 21:22:11 -927.657224 0.0009
PreconLBFGS: 10 21:22:11 -927.657226 0.0004
PreconLBFGS: 11 21:22:11 -927.657226 0.0002
PreconLBFGS: 12 21:22:11 -927.657226 0.0001
PreconLBFGS: 13 21:22:11 -927.657226 0.0001
PreconLBFGS: 14 21:22:11 -927.657226 0.0000
PreconLBFGS: 15 21:22:11 -927.657226 0.0000
PreconLBFGS: 16 21:22:11 -927.657226 0.0000
PreconLBFGS: 17 21:22:11 -927.657226 0.0000
PreconLBFGS: 18 21:22:12 -927.657226 0.0000
PreconLBFGS: 19 21:22:12 -927.657226 0.0000
PreconLBFGS: 20 21:22:12 -927.657226 0.0000
PreconLBFGS: 21 21:22:12 -927.657226 0.0000
SW vacancy formation energy 4.33384020178346 eV