Interoperability with Atomic Simulation Environment

[View with IPython nbviewer]

  • quippy now uses the standard ase.atoms.Atoms class to represent Atomic structures

  • quippy Potential objects can be used as ASE calculators, and vice-versa

  • Can use standard ASE tools, plus communicate with other packages using ASE as lingua franca

Example: vacancy formation energy

  • Generate structure with ASE lattice tools

  • Stillinger-Weber potential implementation from QUIP

  • Elastic constant fitting routine from matscipy, internal relaxations with ASE FIRE minimiser

[7]:
%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
[ ]: