Source code for matador.orm.spectral.dispersion

# coding: utf-8
# Distributed under the terms of the MIT license.

""" This file implements classes to store and manipulate electronic and
vibrational bandstructures, with or without projection data.


import numpy as np
from matador.orm.spectral.spectral import Spectral
from matador.utils.chem_utils import INVERSE_CM_TO_EV, KELVIN_TO_EV

EPS = 1e-4

[docs]class Dispersion(Spectral): """ Parent class for continuous spectra in reciprocal space, i.e. electronic and vibrational bandstructures. Note: This class speaks of "k-points" as general reciprocal space points used to display the dispersion curves; these correspond to CASTEP's phonon_kpoints or spectral_kpoints, and not the k-points used to generate the underlying wavefunction or dynamical matrix. """
[docs] def find_full_kpt_branch(self): """ Find all branch indices from branch start indices. """ branch_inds = [] for ind, start_ind in enumerate(self.kpoint_branch_start[:-1]): branch_inds.append(list(range(start_ind, self.kpoint_branch_start[ind+1]))) branch_inds.append(list(range(self.kpoint_branch_start[-1], self.num_kpoints))) if not sum([len(branch) for branch in branch_inds]) == self.num_kpoints: raise RuntimeError('Error parsing kpoints: number of kpoints does ' 'not match number in branches') return branch_inds
[docs] def set_branches_and_spacing(self): """ Set the relevant kpoint spacing and branch attributes. """ branch_start, spacing = self.find_kpoint_branches() self._data['kpoint_path_spacing'] = spacing self._data['kpoint_branch_start'] = branch_start
[docs] def find_kpoint_branches(self): """ Separate a kpoint path into discontinuous branches, Returns: list[list[int]]: list of lists containing the indices of the discontinuous kpoint branches. float: estimated k-point spacing from median of their separations. """ kpt_diffs = np.linalg.norm(np.diff(self.kpoint_path_cartesian, axis=0), axis=1) spacing = np.median(kpt_diffs) # add 0 as its the start of the first path, then add all indices # have to add 1 to the where to get the start rather than end of the branch branch_start = [0] + (np.where(kpt_diffs > 3*spacing)[0] + 1).tolist() return branch_start, spacing
[docs] def linearise_path(self, preserve_kspace_distance=False): """ For a given k-point path, normalise the spacing between points, mapping it onto [0, 1]. Keyword arguments: preserve_kspace_distance (bool): if True, point separation will be determined by their actual separation in reciprocal space, otherwise they will be evenly spaced. Returns: np.ndarray: 3xN array containing k-points mapped onto [0, 1]. """ path = [0] for branch in self.kpoint_branches: for ind, kpt in enumerate(self.kpoint_path[branch]): if ind != len(branch) - 1: if preserve_kspace_distance: diff = np.sqrt(np.sum((kpt - self.kpoint_path[branch[ind + 1]])**2)) else: diff = 1. path.append(path[-1] + diff) path = np.asarray(path) path /= np.max(path) if len(path) != self.num_kpoints - len(self.kpoint_branches) + 1: raise RuntimeError('Linearised kpoint path has wrong number of kpoints!') return path
[docs] def reorder_bands(self): """ Reorder the bands of this Dispersion object directly. """ self._data['eigs_s_k'] = self.get_band_reordering(self.eigs, self.kpoint_branches)
[docs] @staticmethod def get_band_reordering(eigs, kpoint_branches): """ Recursively reorder eigenvalues such that bands join up correctly, based on local gradients. Parameters: dispersion (numpy.ndarray): array containing eigenvalues as a function of q/k branches (:obj:`list` of :obj:`int`): list containing branches of k/q-point path Returns: numpy.ndarray: reordered branches. """ sorted_eigs = np.array(eigs, copy=True) num_bands = np.shape(sorted_eigs)[1] for channel_ind, channel in enumerate(eigs): eigs = channel for branch_ind, branch in enumerate(kpoint_branches): eigs_branch = channel[:, branch] converged = False counter = 0 i_cached = 0 while not converged and counter < len(branch): counter += 1 for i in range(i_cached+1, len(branch) - 1): guess = (2 * eigs_branch[:, i] - eigs_branch[:, i-1]) argsort_guess = np.argsort(guess) if np.any(np.argsort(guess) != np.argsort(eigs_branch[:, i+1])): tmp_copy = np.array(channel, copy=True) for ind, mode in enumerate(np.argsort(eigs_branch[:, i]).tolist()): eigs_branch[mode, i+1:] = tmp_copy[:, branch][argsort_guess[ind], i+1:] for other_branch in kpoint_branches[branch_ind:]: eigs_other_branch = channel[:, other_branch] for ind, mode in enumerate(np.argsort(channel[:, i]).tolist()): eigs_other_branch[mode] = tmp_copy[:, other_branch][argsort_guess[ind]] channel[:, other_branch] = eigs_other_branch channel[:, branch] = eigs_branch i_cached = i break else: converged = True sorted_eigs[channel_ind] = channel.reshape(1, num_bands, len(channel[0])) return sorted_eigs
[docs] def plot_dispersion(self, **kwargs): """ Make a plot of the band structure, with projections, if found. """ from matador.plotting.spectral_plotting import plot_spectral _kwargs = { "plot_dos": False, "plot_bandstructure": True, "plot_pdis": "projector_weights" in self, "phonons": "Vibrational" in self.__class__.__name__ } _kwargs.update(kwargs) plot_spectral( self, **_kwargs )
def _reshaped_eigs(self, eigs, shape): """ Attempts to reshape the eigenvalues into the desired shape. Parameters: eigs (np.ndarray): the eigs to reshape. shape (tuple): the desired shape. Returns: np.ndarray: the reshaped eigs. """ raise NotImplementedError( 'Wrong eigenvalue shape passed, and reshape function is not yet implemented. ' 'Eigs should have shape {}, not {}'.format(shape, np.shape(eigs)) )
[docs]class ElectronicDispersion(Dispersion): """ Class that stores electronic dispersion data. Attributes are all implemented as properties based on underlying raw data. Attributes: source (list): list of source files. num_kpoints (int): number of kpoints. num_spins (int): number of spin channels. num_bands (int): number of bands. num_electrons (int): number of bands. eigs_s_k (numpy.ndarray): eigenvalue array of shape (num_spins, num_bands, num_kpoints). kpoint_path (numpy.ndarray): array of shape (num_kpoints, 3) containing the k-point path in fractional coordinates. kpoint_path_cartesian (numpy.ndarray): as above, in Cartesian coordinates. fermi_energy (float): Fermi energy in eV (takes average of spin channels if more than one is present). spin_fermi_energy (list[float]): Fermi energy for each spin channel in eV. band_gap (float): smallest band gap across spin channels/momenta. spin_band_gap (list[float]): band gap per spin channel. projectors (list[tuple]): list of projector labels in format (`element`, `l-channel`). projector_weights (numpy.ndarray): array of projector_weights with shape (num_kpoints, num_bands, num_projectors) Note: projector_weights will only work with one spin channel. """ required_keys = [ 'num_kpoints', 'num_spins', 'num_bands', 'num_electrons', 'eigs_s_k', 'kpoint_path', 'lattice_cart', 'fermi_energy', ] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) if kwargs.get('projection_data') is not None: projection_data = kwargs.get('projection_data') self._data['projectors'] = projection_data['projectors'] self._data['projector_weights'] = projection_data['projector_weights'] else: self._data['projectors'] = self._data.get("projectors") self._data['projector_weights'] = self._data.get("projector_weights") if self._data.get("projectors") is not None: if self.num_spins != 1: raise NotImplementedError('Projected dispersion not implemented' ' for multiple spin channels') # only want to take projectors and projector_weights from this data proj_shape = np.shape(self._data["projector_weights"]) expected_shape = (self.num_kpoints, self.num_bands, self.num_projectors) if proj_shape != expected_shape: raise RuntimeError(f"Incompatible shape of projector weights: {proj_shape}, was expecting {expected_shape}") shape = (self.num_spins, self.num_bands, self.num_kpoints) if np.shape(self._data['eigs_s_k']) != shape: self._data['eigs_s_k'] = self._reshaped_eigs(self._data['eigs_s_k'], shape) @property def num_spins(self): """ Number of spin channels in spectrum. """ return self._data['num_spins'] @property def num_electrons(self): """ Number of electrons. """ return self._data['num_electrons'] @property def eigs_s_k(self): """ Array of electronic eigenvalues with shape (num_spins, num_bands, num_kpoints). """ return self._data['eigs_s_k'] @property def eigs(self): """ Alias for `self.eigs_s_k`. """ return self.eigs_s_k @property def fermi_energy(self): """ Return the Fermi energy as described in the raw data. """ return self._data['fermi_energy'] or np.mean(self._data.get('spin_fermi_energy')) @property def spin_fermi_energy(self): """ Return the Fermi energy as described in the raw data. """ return self._data.get('spin_fermi_energy') @property def band_gap(self): """ Return the band gap of the system. """ if not self._data.get('band_gap'): self.set_gap_data() return self._data['band_gap'] @property def band_gap_path_inds(self): """ Return the indices of the k-points that comprise the smallest band gap. """ if not self._data.get('band_gap_path_inds'): self.set_gap_data() return self._data['band_gap_path_inds'] @property def spin_band_gap(self): """ Return the band gap for each spin channel. """ if not self._data.get('spin_band_gap'): self.set_gap_data() return self._data['spin_band_gap'] @property def spin_band_gap_path_inds(self): """ Return the indices of the k-points that comprise the smallest band gap for each spin channel. """ if not self._data.get('spin_band_gap_path_inds'): self.set_gap_data() return self._data['spin_band_gap_path_inds']
[docs] def set_gap_data(self): """ Loop over bands to set the band gap, VBM, CBM, their positions and the smallest direct gap inside self._data, for each spin channel. Sets self.band_gap to be the smallest of the band gaps across all spin channels. """ spin_keys = ['spin_band_gap', 'spin_band_gap_path', 'spin_band_gap_path_inds', 'spin_valence_band_min', 'spin_conduction_band_max', 'spin_gap_momentum', 'spin_direct_gap', 'spin_direct_gap_path', 'spin_direct_gap_path_inds', 'spin_direct_valence_band_min', 'spin_direct_conduction_band_max'] for key in spin_keys: self._data[key] = self.num_spins * [None] for ispin in range(self.num_spins): vbm = -1e10 cbm = 1e10 cbm_pos = [] vbm_pos = [] # calculate indirect gap for _, branch in enumerate(self.kpoint_branches): for nb in range(self.num_bands): band = self.eigs_s_k[ispin][nb][branch] - self.spin_fermi_energy[ispin] argmin = np.argmin(band) argmax = np.argmax(band) if vbm + EPS < band[argmax] < 0: vbm = band[argmax] vbm_pos = [branch[argmax]] elif vbm - EPS <= band[argmax] < 0: vbm = band[argmax] vbm_pos.extend([branch[argmax]]) if cbm - EPS > band[argmin] > 0: cbm = band[argmin] cbm_pos = [branch[argmin]] elif cbm + EPS >= band[argmin] > 0: cbm = band[argmin] cbm_pos.extend([branch[argmin]]) if band[argmin] + EPS/2 < 0 < band[argmax] - EPS/2: vbm = 0 cbm = 0 vbm_pos = [0] cbm_pos = [0] break smallest_diff = 1e10 for _cbm_pos in cbm_pos: for _vbm_pos in vbm_pos: if abs(_vbm_pos - _cbm_pos) < smallest_diff: tmp_cbm_pos = _cbm_pos tmp_vbm_pos = _vbm_pos smallest_diff = abs(_vbm_pos - _cbm_pos) cbm_pos = tmp_cbm_pos vbm_pos = tmp_vbm_pos self._data['spin_valence_band_min'][ispin] = vbm + self.spin_fermi_energy[ispin] self._data['spin_conduction_band_max'][ispin] = cbm + self.spin_fermi_energy[ispin] self._data['spin_band_gap'][ispin] = cbm - vbm self._data['spin_band_gap_path'][ispin] = [self.kpoint_path[cbm_pos], self.kpoint_path[vbm_pos]] self._data['spin_band_gap_path_inds'][ispin] = [cbm_pos, vbm_pos] self._data['spin_gap_momentum'][ispin] = np.linalg.norm( self.kpoint_path_cartesian[cbm_pos] - self.kpoint_path_cartesian[vbm_pos] ) # calculate direct gap direct_gaps = np.zeros(self.num_kpoints) direct_cbms = np.zeros(self.num_kpoints) direct_vbms = np.zeros(self.num_kpoints) for ind, _ in enumerate(self.kpoint_path): direct_cbm = 1e10 direct_vbm = -1e10 for nb in range(self.num_bands): band_eig = self.eigs_s_k[ispin][nb][ind] - self.spin_fermi_energy[ispin] if direct_vbm <= band_eig < EPS: direct_vbm = band_eig if direct_cbm >= band_eig > EPS: direct_cbm = band_eig direct_gaps[ind] = direct_cbm - direct_vbm direct_cbms[ind] = direct_cbm direct_vbms[ind] = direct_vbm self._data['spin_direct_gap'][ispin] = np.min(direct_gaps) self._data['spin_direct_conduction_band_max'][ispin] = ( direct_cbms[np.argmin(direct_gaps)] + self.spin_fermi_energy[ispin] ) self._data['spin_direct_valence_band_min'][ispin] = ( direct_vbms[np.argmin(direct_gaps)] + self.spin_fermi_energy[ispin] ) self._data['spin_direct_gap'][ispin] = np.min(direct_gaps) self._data['spin_direct_gap_path'][ispin] = 2 * [self.kpoint_path[np.argmin(direct_gaps)]] self._data['spin_direct_gap_path_inds'][ispin] = 2 * [np.argmin(direct_gaps)] if np.abs(self._data['spin_direct_gap'][ispin] - self._data['spin_band_gap'][ispin]) < EPS: self._data['spin_valence_band_min'][ispin] = direct_vbm + self.spin_fermi_energy[ispin] self._data['spin_conduction_band_max'][ispin] = direct_cbm + self.spin_fermi_energy[ispin] self._data['spin_band_gap_path_inds'][ispin] = self._data['spin_direct_gap_path_inds'][ispin] cbm_pos = self._data['spin_direct_gap_path_inds'][ispin][0] vbm_pos = self._data['spin_direct_gap_path_inds'][ispin][1] self._data['spin_band_gap_path'][ispin] = self._data['spin_direct_gap_path'][ispin] self._data['spin_gap_momentum'][ispin] = ( np.linalg.norm(self.kpoint_path_cartesian[cbm_pos] - self.kpoint_path_cartesian[vbm_pos]) ) spin_gap_index = np.argmin(self._data['spin_band_gap']) # use smallest spin channel gap data for standard non-spin data access for key in spin_keys: self._data[key.replace('spin_', '')] = self._data[key][spin_gap_index]
[docs] def new_from_trimmed_path(self, k_start_ind=0, k_end_ind=None): """ Returns a new ElectronicDispersion object with the kpoint path trimmed by the provided indices. """ _new_data_dict = {} if k_end_ind is None: k_end_ind = len(self.kpoint_path) _new_data_dict['kpoint_path'] = self.kpoint_path[k_start_ind:k_end_ind] _new_data_dict['eigs_s_k'] = self.eigs[:, :, k_start_ind:k_end_ind] _new_data_dict['num_bands'] = self.num_bands _new_data_dict['num_electrons'] = self.num_electrons _new_data_dict['num_spins'] = self.num_spins _new_data_dict['num_kpoints'] = len(_new_data_dict['kpoint_path']) _new_data_dict['lattice_cart'] = self.lattice_cart _new_data_dict['fermi_energy'] = self.fermi_energy _new_data_dict['spin_fermi_energy'] = self.spin_fermi_energy return ElectronicDispersion(**_new_data_dict)
[docs]class VibrationalDispersion(Dispersion): """ Class that stores vibrational dispersion data. Attributes are all implemented as properties based on underlying raw data. Attributes: source (list): list of source files. num_kpoints (int): number of kpoints. num_atoms (int): number of atoms. num_modes (int): number of phonon modes. eigs (numpy.ndarray): eigenvalue array of shape (1, num_modes, num_kpoints), in frequency units below, with first index denoting the single "spin channel" for phonons. freq_unit (str): human-readable frequency unit used for eig array. kpoint_path (numpy.ndarray): array of shape (num_kpoints, 3) containing the k-point path in fractional coordinates. kpoint_path_cartesian (numpy.ndarray): as above, in Cartesian coordinates. """ required_keys = [ 'num_kpoints', 'num_modes', 'eigs_q', 'kpoint_path' ] def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) shape = (1, self.num_modes, self.num_qpoints) if np.shape(self._data['eigs_q']) != shape: self._data['eigs_q'] = self._reshaped_eigs(self._data['eigs_q'], shape) @property def num_atoms(self): """ Number of atoms in cell. """ return self._data['num_atoms'] @property def num_modes(self): """ Number of phonon modes. """ return self._data['num_modes'] @property def num_bands(self): """Alias for number of modes. """ return self._data['num_modes'] @property def eigs_q(self): """ Eigenvalues in frequency units `self.freq_unit`, with shape (1, num_modes, num_kpoints). """ return np.asarray(self._data['eigs_q']) @property def softest_mode_freq(self): """ The frequency of the softest mode in the calculation. Negative modes correspond to imaginary frequencies. """ return np.min(self.eigs) @property def debye_temperature(self): """ Returns the Debye temperature in K. """ return self.debye_freq * INVERSE_CM_TO_EV / KELVIN_TO_EV @property def debye_freq(self): """ Returns the Debye frequency in cm^-1. """ return np.max(self.eigs)