xtal_lego

Crystal Generator with the desired local coordination environment.

Initialization

from pyxtal import pyxtal
from xtal_lego.builder import mof_builder

# Set reference graphite sp2 structure
xtal = pyxtal()
xtal.from_spg_wps_rep(194, ['2c', '2b'], [2.46, 6.70], ['C', 'C'])
cif_file = xtal.to_pymatgen()

builder = mof_builder(['C'], [1], db_file='mof_sp2.db')
builder.set_descriptor_calculator(mykwargs={'rcut': 2.0})
builder.set_reference_enviroments(cif_file)
builder.set_criteria(CN={'C': [3]})
print(builder)

------MOF Builder------
System: C1
Database: mof_sp2.db
Log_file: mof.log
Descriptor: SO3 descriptor with Cutoff:  1.800 lmax: 4, nmax: 2, alpha: 1.500
neighborlist: ase
Reference enviroments (1, 15)
Criterion_CN: {'C': 3}
Criterion_cutoff: None
Criterion_Dimension: 3
Criterion_exclude_ii: False

The above setup include the following important information:

• Reference enviroment: the reference atomic environment based on the input crystal
• Calculator: the way to compute local atomic environment
• database file: a database file to store the structures generated by this instance
• log file: a log file to store information all activities

Optimization of a single crystal

After the initialization, the mof_builder class can take an input crystal and then perform optimization to get the crystal with the target reference environment

# Generate a diamond structure and optimize its similarity
xtal0 = pyxtal()
xtal0.from_spg_wps_rep(227, ['8a'], [3.6], ['C'])
xtal0, sim = builder.optimize_xtal(xtal0)
print(xtal0.get_xtal_string(dicts={"Sim": float(sim)}, header='After Opt'))

# Try to optimize at the subgroup representation 227->210
sub = xtal0.subgroup_once(H=210)
sub0, sim = builder.optimize_xtal(sub)
print(sub0.get_xtal_string(dicts={"Sim": float(sim)}, header='After Opt'))

# Try to optimize at the subgroup representation 227->210->212
sub = sub.subgroup_once(H=212, group_type='t+k')
sub0, sim = builder.optimize_xtal(sub)
print(sub0.get_xtal_string(dicts={"Sim": float(sim)}, header='After Opt'))
sub0.to_file('opt.cif')

After Opt*  8   1  227 Fd-3m          4.03    13.386 8a
After Opt*  8   1  210 F4132          4.03    13.386 8b
After Opt*  8   2  212 P4332          2.46     0.000 8c
   0*  8   2  212 P4332          2.46  False        13.037 =>  0.000   8c

This example does not only show how to call optmize_xtal function via mof_builder, but also illustrate that optimization under different space group representation can lead to different results.

Post-analysis

You may need to run two external packages

• GULP: based on classical force field
• dftbplus: based on density functional tight-binding

Detailed instructions can be found at the subdiectory of Installation