Source code for ase.cluster.wulff

import numpy as np


delta = 1e-10


[docs]def wulff_construction(symbol, surfaces, energies, size, structure, rounding='closest', latticeconstant=None, debug=False, maxiter=100): """Create a cluster using the Wulff construction. A cluster is created with approximately the number of atoms specified, following the Wulff construction, i.e. minimizing the surface energy of the cluster. Parameters ---------- symbol : str or int The chemical symbol (or atomic number) of the desired element. surfaces : list A list of surfaces. Each surface is an (h, k, l) tuple or list of integers. energies : list A list of surface energies for the surfaces. size : int The desired number of atoms. structure : {'fcc', bcc', 'sc'} The desired crystal structure. rounding : {'closest', 'above', 'below'} Specifies what should be done if no Wulff construction corresponds to exactly the requested number of atoms. 'above', 'below', and 'closest' mean that the nearest cluster above or below - or the closest one - is created instead. latticeconstant : float (optional) The lattice constant. If not given, extracted from `ase.data`. debug : bool, default False If True, information about the iteration towards the right cluster size is printed. """ if debug: print('Wulff: Aiming for cluster with %i atoms (%s)' % (size, rounding)) if rounding not in ['above', 'below', 'closest']: raise ValueError('Invalid rounding: %s' % rounding) # Interpret structure, if it is a string. if isinstance(structure, str): if structure == 'fcc': from ase.cluster.cubic import FaceCenteredCubic as structure elif structure == 'bcc': from ase.cluster.cubic import BodyCenteredCubic as structure elif structure == 'sc': from ase.cluster.cubic import SimpleCubic as structure elif structure == 'hcp': from ase.cluster.hexagonal import \ HexagonalClosedPacked as structure elif structure == 'graphite': from ase.cluster.hexagonal import Graphite as structure else: error = 'Crystal structure %s is not supported.' % structure raise NotImplementedError(error) # Check number of surfaces nsurf = len(surfaces) if len(energies) != nsurf: raise ValueError('The energies array should contain %d values.' % (nsurf,)) # Copy energies array so it is safe to modify it energies = np.array(energies) # We should check that for each direction, the surface energy plus # the energy in the opposite direction is positive. But this is # very difficult in the general case! # Before starting, make a fake cluster just to extract the # interlayer distances in the relevant directions, and use these # to "renormalize" the surface energies such that they can be used # to convert to number of layers instead of to distances. atoms = structure(symbol, surfaces, 5 * np.ones(len(surfaces), int), latticeconstant=latticeconstant) for i, s in enumerate(surfaces): d = atoms.get_layer_distance(s) energies[i] /= d # First guess a size that is not too large. wanted_size = size ** (1.0 / 3.0) max_e = max(energies) factor = wanted_size / max_e atoms, layers = make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant) if len(atoms) == 0: # Probably the cluster is very flat if debug: print('First try made an empty cluster, trying again.') factor = 1 / energies.min() atoms, layers = make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant) if len(atoms) == 0: raise RuntimeError('Failed to create a finite cluster.') # Second guess: scale to get closer. old_factor = factor old_layers = layers old_atoms = atoms factor *= (size / len(atoms))**(1.0 / 3.0) atoms, layers = make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant) if len(atoms) == 0: print('Second guess gave an empty cluster, discarding it.') atoms = old_atoms factor = old_factor layers = old_layers else: del old_atoms # Find if the cluster is too small or too large (both means perfect!) below = above = None if len(atoms) <= size: below = atoms if len(atoms) >= size: above = atoms # Now iterate towards the right cluster iter = 0 while (below is None or above is None): if len(atoms) < size: # Find a larger cluster if debug: print('Making a larger cluster.') factor = ((layers + 0.5 + delta) / energies).min() atoms, new_layers = make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant) assert (new_layers - layers).max() == 1 assert (new_layers - layers).min() >= 0 layers = new_layers else: # Find a smaller cluster if debug: print('Making a smaller cluster.') factor = ((layers - 0.5 - delta) / energies).max() atoms, new_layers = make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant) assert (new_layers - layers).max() <= 0 assert (new_layers - layers).min() == -1 layers = new_layers if len(atoms) <= size: below = atoms if len(atoms) >= size: above = atoms iter += 1 if iter == maxiter: raise RuntimeError('Runaway iteration.') if rounding == 'below': if debug: print('Choosing smaller cluster with %i atoms' % len(below)) return below elif rounding == 'above': if debug: print('Choosing larger cluster with %i atoms' % len(above)) return above else: assert rounding == 'closest' if (len(above) - size) < (size - len(below)): atoms = above else: atoms = below if debug: print('Choosing closest cluster with %i atoms' % len(atoms)) return atoms
def make_atoms(symbol, surfaces, energies, factor, structure, latticeconstant): layers1 = factor * np.array(energies) layers = np.round(layers1).astype(int) atoms = structure(symbol, surfaces, layers, latticeconstant=latticeconstant) return (atoms, layers)