Chimie Théorique » QMX

"""

This module is the EMBED module for ASE

implemented by T. Kerber

Torsten Kerber, ENS LYON: 2011, 07, 11

This work is supported by Award No. UK-C0017, made by King Abdullah

University of Science and Technology (KAUST)

"""

import math

from ase import Atom, Atoms

from ase.data import covalent_radii, atomic_numbers

import numpy as np

class Embed(Atoms):

    #--- constructor of the Embed class ---

    def __init__(self, system, cluster, cell_cluster = "Auto", cluster_pos = True, linkType = 'H'):

        super(Embed, self).__init__()

        # define the atom map

        self.atom_map_sys_cl = []

        self.atom_map_cl_sys = []

        self.linkatoms = []

        # cluster dimensions

        self.xyz_cl_min = None

        self.xyz_cl_max = None

        # set the search radius for link atoms

        self.d = 10

        # define the systems for calculations

        if system is None or cluster is None:

            raise RuntimeError("Embed: system or cluster is not definied")

        self.set_system(system)

        if cluster_pos:

            self.set_cluster(cluster)

        else:

            self.set_cluster_by_numbers(cluster)

        # set the cell of the system

        self.set_cell(system.get_cell())

        self.cell_cluster = cell_cluster

        self.embed(linkType)

        return

    #--- set the cluster ---

    def set_cluster(self, atoms):

        import copy

        # set the min/max cluster dimensions

        self.xyz_cl_min = atoms[0].get_position()

        self.xyz_cl_max = atoms[0].get_position()

        for atom in atoms:

            # assign the label "Cluster (10)" in atom.TAG

            atom.set_tag(10)

            xyz=atom.get_position()

            for i in xrange(3):

                # set the min/max cluster dimensions

                if xyz[i] < self.xyz_cl_min[i]:

                    self.xyz_cl_min[i] = xyz[i]

                if xyz[i] > self.xyz_cl_max[i]:

                    self.xyz_cl_max[i] = xyz[i]

        # add self.d around min/max cluster dimensions

        self.xyz_cl_min -= [self.d, self.d, self.d]

        self.xyz_cl_max += [self.d, self.d, self.d]

        # set the cluster for low and high level calculation

        self.atoms_cluster = atoms

        return

    #--- set cluster by atom numbers ---

    def set_cluster_by_numbers(self, numbers):

        cluster = Atoms()

        nat = len(self.atoms_system)

        for number in numbers:

            if nat > numbers:

                raise RuntimeError("QMX: The number of the cluster atom ", number, "is bigger than the number of atoms")

            cluster.append(self.atoms_system[number-1])

        self.set_cluster(cluster)

    #--- set the system ---

    def set_system(self, atoms):

        self.atoms_system = atoms

        # assign the label "Cluster (10)" in atom.TAG

        for atom in atoms:

            atom.set_tag(0)

        # update search radius for link atoms

        dx = 0

        for atom in atoms:

            r = covalent_radii[atom.get_atomic_number()]

            if (r > dx):

                dx = r

        self.d = dx * 2.1

        return

    #--- return cluster ---

    def get_cluster(self):

        return self.atoms_cluster

    def get_system(self):

        return self.atoms_system

    #--- Embedding ---

    def embed(self, linkType):

        # is the cluster and the host system definied ?

        if self.atoms_cluster is None or self.atoms_system is None:

            return

        self.find_cluster()

        self.set_linkatoms(linkType)

        print "link atoms found: ", len(self.linkatoms)

        if self.cell_cluster == "System":

            self.atoms_cluster.set_cell(self.atoms_system.get_cell())

        elif self.cell_cluster == "Auto":

            positions = self.atoms_cluster.get_positions()

            # build cell

            cell = np.zeros((3, 3),  float)

            #for all directions

            for idir in xrange(3):

                #find the biggest dimensions of the cluster in x,y,z direction

                l = positions[:, idir].max() - positions[:, idir].min()

                # calculate the box parameters (2*cluster + 60 pm)

                l = (math.floor((2*l+6)/2.5)+1)*2.5

                # apply cell parameters

                cell[idir, idir] = l

            # set parameters to cluster

            self.atoms_cluster.set_cell(cell)

            # print information on the screen

            print "dimension of box surrounding the cluster: ",

            for idir in xrange(3):

                print cell[idir, idir]*10,

            print " pm"

        else:

            self.atoms_cluster.set_cell(self.cell_cluster)

    def find_cluster(self):

        # set tolerance

        d = 0.001

        #atoms

        xyzs_cl=[]

        for atom_cl in self.atoms_cluster:

            xyzs_cl.append(atom_cl.get_position())

        xyzs_sys=[]

        for atom_sys in self.atoms_system:

            xyzs_sys.append(atom_sys.get_position())

        self.atom_map_sys_cl = np.zeros(len(self.atoms_system), int)

        self.atom_map_cl_sys = np.zeros(len(self.atoms_cluster), int)

        # loop over cluster atoms atom_sys

        for iat_sys in xrange(len(self.atoms_system)):

            # get the coordinates of the system atom atom_sys

            xyz_sys = xyzs_sys[iat_sys]

            # bSysOnly: no identical atom has been found

            bSysOnly = True

            # loop over system atoms atom_cl

            for iat_cl in xrange(len(self.atoms_cluster)):

                # difference vector between both atoms

                xyz_diff = np.abs(xyzs_sys[iat_sys]-xyzs_cl[iat_cl])

                # identical atoms

                if xyz_diff[0] < d and xyz_diff[1] < d and xyz_diff[2] < d:

                    # set tag (CLUSTER+HOST: 10) to atom_sys

                    self.atoms_system[iat_sys].set_tag(10)

                    # map the atom in the atom list

                    self.atom_map_sys_cl[iat_sys] = iat_cl

                    self.atom_map_cl_sys[iat_cl] = iat_sys

                    # atom has been identified

                    bSysOnly = False

                    break

            if bSysOnly:

                self.atom_map_sys_cl[iat_sys] = -1

    def set_linkatoms(self, linkType, tol=15.):

        # local copies of xyz coordinates to avoid massive copying of xyz objects

        xyzs_cl=[]

        for atom_cl in self.atoms_cluster:

            xyzs_cl.append(atom_cl.get_position())

        xyzs_sys=[]

        for atom_sys in self.atoms_system:

            xyzs_sys.append(atom_sys.get_position())

        # number of atoms in the cluster and the system

        nat_cl=len(self.atoms_cluster)

        nat_sys=len(self.atoms_system)

        # system has pbc?

        pbc = self.get_pbc()

        # set the bond table

        bonds = []

        # set the 27 cell_vec, starting with the (0,0,0) vector for the unit cell

        cells_L = [(0.0,  0.0,  0.0)]

        # get the cell vectors of the host system and build up a 3 by 3 supercell to search for neighbors in the surrounding

        cell = self.atoms_system.get_cell()

        if self.atoms_system.get_pbc().any():

            for ix in xrange(-1, 2):

                for iy in xrange(-1, 2):

                    for iz in xrange(-1, 2):

                        if ix == 0 and iy == 0 and iz == 0:

                            continue

                        cells_L.append(np.dot([ix, iy, iz], cell))

        # save the radius of system atoms

        rs_sys = []

        for atom in self.atoms_system:

            rs_sys.append(covalent_radii[atom.get_atomic_number()])

        # sum over cluster atoms (iat_cl)

        for iat_cl in xrange(nat_cl):

            # get the cluster atom

            atom_cl=self.atoms_cluster[iat_cl]

            # ignore link atoms

            if atom_cl.get_tag() == 50:

                continue

            # xyz coordinates and covalent radius of the cluster atom iat_cl

            xyz_cl = xyzs_cl[iat_cl]

            r_cl = covalent_radii[atom_cl.get_atomic_number()]

            # sum over system atoms (iat_sys)

            for iat_sys in xrange(nat_sys):

                # avoid cluster atoms

                if self.atoms_system[iat_sys].get_tag()==10:

                    continue

                # sum over all cell_L

                for cell_L in cells_L:

                    # xyz coordinates and covalent radius of the system atom iat_sys

                    xyz_sys = xyzs_sys[iat_sys]+cell_L

                    # go only in distance self.d around the cluster

                    lcont = True

                    for i in xrange(3):

                        if (xyz_sys[i] < self.xyz_cl_min[i] or

                            xyz_sys[i] > self.xyz_cl_max[i]):

                            lcont = False

                            break

                    if not lcont:

                        continue

                    # xyz coordinates and covalent radius of the system atom iat_sys

                    r_sys = rs_sys[iat_sys]

                    # diff vector

                    xyz_diff = xyz_sys - xyz_cl

                    # distance between the atoms

                    r = np.sqrt(np.dot(xyz_diff, xyz_diff))

                    # ratio of the distance to the sum of covalent radius

                    f = r / (r_cl + r_sys)

                    if f <= (1+tol/100.) and f >= (1-2*tol/100.):

                        s = cell_L, self.atom_map_cl_sys[iat_cl], iat_sys, r_cl

                        bonds.append(s)

                        break

                    if f <= (1-2*tol/100.):

                        raise RuntimeError("QMX: The cluster atom", iat_cl, " and the system atom", iat_sys, "came too close")

        r_h = covalent_radii[atomic_numbers[linkType]]

        for bond in bonds:

            cell_L, iat_cl_sys, iat_sys, r_cl = bond

            # assign the tags for the border atoms

            atom_sys.set_tag(1)

            atom_cl.set_tag(11)

            #difference vector for the link atom, scaling

            xyz_diff = xyzs_sys[iat_sys] + cell_L - xyzs_sys[iat_cl_sys]

            r = (r_cl + r_h)

            xyz_diff *=  r / np.sqrt(np.dot(xyz_diff, xyz_diff))

            # determine position of the link atom

            xyz_diff += xyzs_sys[iat_cl_sys]

            # create link atom

            atom = Atom(symbol=linkType, position=xyz_diff, tag=50)

            # add atom to cluster

            self.atoms_cluster.append(atom)

            # add atom to the linkatoms

            s = cell_L, iat_cl_sys, iat_sys, r, len(self.atoms_cluster)-1

            self.linkatoms.append(s)

        return

    def set_positions(self, positions_new):

        # number of atoms

        nat_sys=len(self.atoms_system)

        # go over all pairs of atoms

        for iat_sys in xrange(nat_sys):

            xyz = positions_new[iat_sys]

            self.atoms_system[iat_sys].set_position(xyz)

            iat_cl = self.atom_map_sys_cl[iat_sys]

            if iat_cl > -1:

                self.atoms_cluster[iat_cl].set_position(xyz)

        for cell_L, iat_cl_sys, iat_sys, r, iat in self.linkatoms:

            # determine position of the link atom

            xyz_cl = positions_new[iat_cl_sys]

            xyz = positions_new[iat_sys] - xyz_cl + cell_L

            xyz *= r / np.sqrt(np.dot(xyz, xyz))

            xyz += xyz_cl

            # update xyz coordinates of the cluster

            self.atoms_cluster[iat].set_position(xyz)

    def __getitem__(self, i):

        return self.atoms_system.__getitem__(i)

    def get_positions(self):

        return self.atoms_system.get_positions()

    def __add__(self, other):

        return self.atoms_system.__add__(other)

    def __delitem__(self, i):

        return self.atoms_system.__delitem__(i)

    def __len__(self):

        return self.atoms_system.__len__()

    def get_chemical_symbols(self, reduce=False):

        return self.atoms_system.get_chemical_symbols(reduce)