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""" 

Module for generating rattled structures. Rattle refers to displacing atoms 

with a normal distribution with zero mean and some standard deviation. 

""" 

 

import numpy as np 

from scipy.special import erf 

from ase.neighborlist import NeighborList 

 

 

def generate_rattled_structures(atoms, n_structures, rattle_std, seed=42): 

""" Return list of rattled configurations. 

 

Displacements are drawn from normal distributions in x,y,z directions for 

each atom independently. 

 

Warning 

------- 

Repeatedly calling this function *without* providing different seeds will 

yield identical or correlated results to avoid this please specify a 

different seed for each call in such a case. 

 

Parameters 

---------- 

atoms : ASE Atoms object 

prototype ase atoms object 

n_structures : int 

number of structures to generate 

rattle_std : float 

rattle amplitude (standard deviation in normal distribution) 

seed : int 

seed for setting up NumPy random state from which random numbers are 

generated 

""" 

rs = np.random.RandomState(seed) 

N = len(atoms) 

atoms_list = [] 

for _ in range(n_structures): 

atoms_tmp = atoms.copy() 

displacements = rs.normal(0.0, rattle_std, (N, 3)) 

atoms_tmp.positions += displacements 

atoms_list.append(atoms_tmp) 

return atoms_list 

 

 

def generate_mc_rattled_structures(atoms, n_configs, rattle_std, d_min, 

seed=42, **kwargs): 

""" Return list of Monte Carlo rattled configurations. 

 

Rattling atom `i` is a Monte Carlo move and is accepted with a probability 

that is determined from the minimum interatomic distance `d_ij`. 

If `min(d_ij)` is smaller than `d_min` the move is only accepted with a low 

probability. 

 

This process is repeated for each atom a number of times meaning the 

magnitude of the final displacements are not connected to `rattle_std`. 

 

Warning 

------- 

Repeatedly calling this function *without* providing different seeds will 

yield identical or correlated results to avoid this please specify a 

different seed for each call in such a case. 

 

Notes 

------ 

Note that this might not generate a symmetric distribution for the 

displacements 

kwargs will be forwarded to mc_rattle (see doc for this function for 

detailed explanation) 

 

Parameters 

---------- 

atoms : ASE Atoms object 

prototype ase atoms object 

n_structures : int 

number of structures to generate 

rattle_std : float 

rattle amplitude (standard deviation in normal distribution). Note this 

value is not connected to the final average displacement for the 

structures. 

d_min : float 

interatomic distance used for computing the probability for each rattle 

move. 

seed : int 

seed for setting up NumPy random state from which random numbers are 

generated 

""" 

rs = np.random.RandomState(seed) 

atoms_list = [] 

for _ in range(n_configs): 

atoms_tmp = atoms.copy() 

seed = rs.randint(1, 1000000000) 

displacements = mc_rattle(atoms_tmp, rattle_std, d_min, seed=seed, 

**kwargs) 

atoms_tmp.positions += displacements 

atoms_list.append(atoms_tmp) 

return atoms_list 

 

 

def _probability_mc_rattle(d, d_min, width): 

""" Monte Carlo probability function as an error function. 

 

Parameters 

---------- 

d_min : float 

center value for the error function 

width : float 

width of error function 

""" 

 

return (erf((d-d_min)/width) + 1.0) / 2 

 

 

def mc_rattle(atoms, rattle_std, d_min, width=0.1, N_iter=10, 

max_attempts=5000, max_disp=2.0, active_atoms=None, seed=42): 

""" Generate displacements using the Monte Carlo rattle method 

 

Parameters 

---------- 

atoms : ASE Atoms object 

prototype ase atoms object 

rattle_std : float 

rattle amplitude (standard deviation in normal distribution) 

d_min : float 

interatomic distance used for computing the probability for each rattle 

move. Center position of the error function 

width : float 

width of the error function 

N_iter : int 

Number of Monte Carlo cycle 

max_disp : float 

rattle moves that yields a displacement larger than max_disp will 

always be rejected. This rarley occurs and is more used as a safety net 

for not generating structures where two or more have swapped positions. 

max_attempts : int 

limit for how many attempted rattle moves are allowed a single atom. 

If this limit is reached an `Exception` is raised. 

active_atoms : list 

list of which atomic indices should undergo Monte Carlo rattling 

seed : int 

seed for setting up NumPy random state from which random numbers are 

generated 

""" 

rs = np.random.RandomState(seed) 

 

146 ↛ 149line 146 didn't jump to line 149, because the condition on line 146 was never false if active_atoms is None: 

active_atoms = range(len(atoms)) 

 

atoms_rattle = atoms.copy() 

reference_positions = atoms_rattle.get_positions() 

nbr_list = NeighborList([d_min]*len(atoms_rattle), skin=0.0, 

self_interaction=False, bothways=True) 

nbr_list.update(atoms_rattle) 

 

# run Monte Carlo 

for _ in range(N_iter): 

for i in active_atoms: 

i_nbrs = np.setdiff1d(nbr_list.get_neighbors(i)[0], [i]) 

159 ↛ 173line 159 didn't jump to line 173, because the loop on line 159 didn't complete for n in range(max_attempts): 

delta_disp = rs.normal(0.0, rattle_std, 3) 

atoms_rattle.positions[i] += delta_disp 

disp_i = atoms_rattle.positions[i] - reference_positions[i] 

163 ↛ 164line 163 didn't jump to line 164, because the condition on line 163 was never true if np.linalg.norm(disp_i) > max_disp: 

continue 

min_distance = np.min(atoms_rattle.get_distances(i, i_nbrs, 

mic=True)) 

167 ↛ 171line 167 didn't jump to line 171, because the condition on line 167 was never false if _probability_mc_rattle(min_distance, d_min, width) > \ 

rs.rand(): # accept disp_i 

break 

else: # revert disp_i 

atoms_rattle[i].position -= delta_disp 

else: 

raise Exception('Maxmium attempts for atom {}'.format(i)) 

displacements = atoms_rattle.positions - reference_positions 

return displacements