Esempio n. 1
0
    def full_static_screened_interaction(self):
        """Calcuate W_GG(q)"""
        W_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)

        t0 = time()
        for iq in range(self.nibzq):
            q = self.ibzq_qc[iq]
            optical_limit = False
            if np.abs(q).sum() < 1e-8:
                q = self.q_c.copy()
                optical_limit = True
            df = DF(calc=self.calc,
                    q=q,
                    w=(0., ),
                    optical_limit=optical_limit,
                    nbands=self.nbands,
                    hilbert_trans=False,
                    eta=0.0001,
                    ecut=self.ecut * Hartree,
                    xc='RPA',
                    txt='df.out')
            df.initialize()
            df.calculate()

            if optical_limit:
                K_GG = self.V_qGG[iq].copy()
                K0 = calculate_Kc(q,
                                  self.Gvec_Gc,
                                  self.acell_cv,
                                  self.bcell_cv,
                                  self.pbc,
                                  vcut=self.vcut)[0, 0]

                for iG in range(1, self.npw):
                    K_GG[0, iG] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
                    K_GG[iG, 0] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
                K_GG[0, 0] = K0
                df_GG = np.eye(self.npw, self.npw) - K_GG * df.chi0_wGG[0]
            else:
                df_GG = np.eye(self.npw,
                               self.npw) - self.V_qGG[iq] * df.chi0_wGG[0]
            dfinv_GG = np.linalg.inv(df_GG)

            if optical_limit:
                eps = 1 / dfinv_GG[0, 0]
                self.printtxt(
                    '    RPA macroscopic dielectric constant is: %3.3f' %
                    eps.real)
            W_qGG[iq] = dfinv_GG * self.V_qGG[iq]
            self.timing(iq, t0, self.nibzq, 'iq')

        if rank == 0:
            if self.kernel_file is not None:
                data = {'W_qGG': W_qGG}
                name = self.kernel_file + '.pckl'
                pickle.dump(data, open(name, 'w'), -1)

        return W_qGG
Esempio n. 2
0
    def full_static_screened_interaction(self):
        """Calcuate W_GG(q)"""
        W_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)

        t0 = time()
        for iq in range(self.nibzq):
            q = self.ibzq_qc[iq]
            optical_limit = False
            if np.abs(q).sum() < 1e-8:
                q = self.q_c.copy()
                optical_limit = True
            df = DF(calc=self.calc,
                    q=q,
                    w=(0.,),
                    optical_limit=optical_limit,
                    nbands=self.nbands,
                    hilbert_trans=False,
                    eta=0.0001,
                    ecut=self.ecut*Hartree,
                    xc='RPA',
                    txt='df.out')
            df.initialize()
            df.calculate()

            if optical_limit:
                K_GG = self.V_qGG[iq].copy()
                q_v = np.dot(q, self.bcell_cv)
                K0 = calculate_Kc(q,
                                  self.Gvec_Gc,
                                  self.acell_cv,
                                  self.bcell_cv,
                                  self.pbc,
                                  vcut=self.vcut)[0,0]

                for iG in range(1,self.npw):
                    K_GG[0, iG] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
                    K_GG[iG, 0] = self.V_qGG[iq, iG, iG]**0.5 * K0**0.5
                K_GG[0,0] = K0
                df_GG = np.eye(self.npw, self.npw) - K_GG*df.chi0_wGG[0]
            else:
                df_GG = np.eye(self.npw, self.npw) - self.V_qGG[iq]*df.chi0_wGG[0]
            dfinv_GG = np.linalg.inv(df_GG)
            
            if optical_limit:
                eps = 1/dfinv_GG[0,0]
                self.printtxt('    RPA macroscopic dielectric constant is: %3.3f' %  eps.real)
            W_qGG[iq] = dfinv_GG * self.V_qGG[iq]
            self.timing(iq, t0, self.nibzq, 'iq')
            
        if rank == 0:
            if self.kernel_file is not None:
                data = {'W_qGG': W_qGG}
                name = self.kernel_file+'.pckl'
                pickle.dump(data, open(name, 'w'), -1)
        
        return W_qGG
Esempio n. 3
0
    def full_bare_interaction(self):
        """Calculate V_GG(q)"""

        V_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)
        
        for iq in range(self.nibzq):
            q = self.ibzq_qc[iq]

            Vq_G = np.diag(calculate_Kc(q,
                                        self.Gvec_Gc,
                                        self.acell_cv,
                                        self.bcell_cv,
                                        self.pbc,
                                        integrate_gamma=True,
                                        N_k=self.kd.N_c,
                                        vcut=self.vcut))**0.5
            
            for i, iG in enumerate(self.integrate_coulomb):
                Vq_G[iG] = self.vint_Gq[i][iq]**0.5

            V_qGG[iq] = np.outer(Vq_G, Vq_G)

        return V_qGG
Esempio n. 4
0
    def full_bare_interaction(self):
        """Calculate V_GG(q)"""

        V_qGG = np.zeros((self.nibzq, self.npw, self.npw), dtype=complex)

        for iq in range(self.nibzq):
            q = self.ibzq_qc[iq]

            Vq_G = np.diag(
                calculate_Kc(q,
                             self.Gvec_Gc,
                             self.acell_cv,
                             self.bcell_cv,
                             self.pbc,
                             integrate_gamma=True,
                             N_k=self.kd.N_c,
                             vcut=self.vcut))**0.5

            for i, iG in enumerate(self.integrate_coulomb):
                Vq_G[iG] = self.vint_Gq[i][iq]**0.5

            V_qGG[iq] = np.outer(Vq_G, Vq_G)

        return V_qGG
Esempio n. 5
0
    def screened_interaction_kernel(self, iq):
        """Calcuate W_GG(w) for a given q.
        if static: return W_GG(w=0)
        is not static: return W_GG(q,w) - Vc_GG
        """

        q = self.ibzq_qc[iq]
        w = self.w_w.copy() * Hartree

        optical_limit = False
        if np.abs(q).sum() < 1e-8:
            q = np.array([1e-12, 0,
                          0])  # arbitrary q, not really need to be calculated
            optical_limit = True

        hilbert_trans = True
        if self.ppa or self.static:
            hilbert_trans = False

        df = DF(calc=self.calc,
                q=q.copy(),
                w=w,
                nbands=self.nbands,
                eshift=None,
                optical_limit=optical_limit,
                hilbert_trans=hilbert_trans,
                xc='RPA',
                time_ordered=True,
                rpad=self.rpad,
                vcut=self.vcut,
                G_plus_q=True,
                eta=self.eta * Hartree,
                ecut=self.ecut.copy() * Hartree,
                txt='df.out',
                comm=self.dfcomm,
                kcommsize=self.kcommsize)

        df.initialize()
        df.e_skn = self.e_skn.copy()
        df.calculate()

        dfinv_wGG = df.get_inverse_dielectric_matrix(xc='RPA')
        assert df.ecut[0] == self.ecut[0]
        if not self.static and not self.ppa:
            assert df.eta == self.eta
            assert df.Nw == self.Nw
            assert df.dw == self.dw

        # calculate Coulomb kernel and use truncation in 2D
        delta_GG = np.eye(df.npw)
        Vq_G = np.diag(
            calculate_Kc(q,
                         df.Gvec_Gc,
                         self.acell_cv,
                         self.bcell_cv,
                         self.pbc,
                         integrate_gamma=True,
                         N_k=self.kd.N_c,
                         vcut=self.vcut))**0.5
        if (self.vcut == '2D' and df.optical_limit) or self.numint:
            for iG in range(len(df.Gvec_Gc)):
                if df.Gvec_Gc[iG, 0] == 0 and df.Gvec_Gc[iG, 1] == 0:
                    v_q, v0_q = calculate_Kc_q(self.acell_cv,
                                               self.bcell_cv,
                                               self.pbc,
                                               self.kd.N_c,
                                               vcut=self.vcut,
                                               q_qc=np.array([q]),
                                               Gvec_c=df.Gvec_Gc[iG])
                    Vq_G[iG] = v_q[0]**0.5
        self.Kc_GG = np.outer(Vq_G, Vq_G)

        if self.ppa:
            dfinv1_GG = dfinv_wGG[0] - delta_GG
            dfinv2_GG = dfinv_wGG[1] - delta_GG
            self.wt_GG = self.E0 * np.sqrt(dfinv2_GG / (dfinv1_GG - dfinv2_GG))
            self.R_GG = -self.wt_GG / 2 * dfinv1_GG
            del dfinv_wGG
            dfinv_wGG = np.array([1j * pi * self.R_GG + delta_GG])

        if self.static:
            assert len(dfinv_wGG) == 1
            W_GG = dfinv_wGG[0] * self.Kc_GG

            return df, W_GG
        else:
            Nw = np.shape(dfinv_wGG)[0]
            W_wGG = np.zeros_like(dfinv_wGG)
            for iw in range(Nw):
                dfinv_wGG[iw] -= delta_GG
                W_wGG[iw] = dfinv_wGG[iw] * self.Kc_GG

            return df, W_wGG
Esempio n. 6
0
    def screened_interaction_kernel(self, iq):
        """Calcuate W_GG(w) for a given q.
        if static: return W_GG(w=0)
        is not static: return W_GG(q,w) - Vc_GG
        """

        q = self.ibzq_qc[iq]
        w = self.w_w.copy()*Hartree

        optical_limit = False
        if np.abs(q).sum() < 1e-8:
            q = np.array([1e-12, 0, 0]) # arbitrary q, not really need to be calculated
            optical_limit = True

        hilbert_trans = True
        if self.ppa or self.static:
            hilbert_trans = False

        df = DF(calc=self.calc, q=q.copy(), w=w, nbands=self.nbands, eshift=None,
                optical_limit=optical_limit, hilbert_trans=hilbert_trans, xc='RPA', time_ordered=True,
                rpad=self.rpad, vcut=self.vcut, G_plus_q=True,
                eta=self.eta*Hartree, ecut=self.ecut.copy()*Hartree,
                txt='df.out', comm=self.dfcomm, kcommsize=self.kcommsize)

        df.initialize()
        df.e_skn = self.e_skn.copy()
        df.calculate()

        dfinv_wGG = df.get_inverse_dielectric_matrix(xc='RPA')
        assert df.ecut[0] == self.ecut[0]
        if not self.static and not self.ppa:
            assert df.eta == self.eta
            assert df.Nw == self.Nw
            assert df.dw == self.dw

        # calculate Coulomb kernel and use truncation in 2D
        delta_GG = np.eye(df.npw)
        Vq_G = np.diag(calculate_Kc(q,
                                    df.Gvec_Gc,
                                    self.acell_cv,
                                    self.bcell_cv,
                                    self.pbc,
                                    integrate_gamma=True,
                                    N_k=self.kd.N_c,
                                    vcut=self.vcut))**0.5
        if (self.vcut == '2D' and df.optical_limit) or self.numint:
            for iG in range(len(df.Gvec_Gc)):
                if df.Gvec_Gc[iG, 0] == 0 and df.Gvec_Gc[iG, 1] == 0:
                    v_q, v0_q = calculate_Kc_q(self.acell_cv,
                                               self.bcell_cv,
                                               self.pbc,
                                               self.kd.N_c,
                                               vcut=self.vcut,
                                               q_qc=np.array([q]),
                                               Gvec_c=df.Gvec_Gc[iG])
                    Vq_G[iG] = v_q[0]**0.5
        self.Kc_GG = np.outer(Vq_G, Vq_G)

        if self.ppa:
            dfinv1_GG = dfinv_wGG[0] - delta_GG
            dfinv2_GG = dfinv_wGG[1] - delta_GG
            self.wt_GG = self.E0 * np.sqrt(dfinv2_GG / (dfinv1_GG - dfinv2_GG))
            self.R_GG = - self.wt_GG / 2 * dfinv1_GG
            del dfinv_wGG
            dfinv_wGG = np.array([1j*pi*self.R_GG + delta_GG])

        if self.static:
            assert len(dfinv_wGG) == 1
            W_GG = dfinv_wGG[0] * self.Kc_GG

            return df, W_GG
        else:
            Nw = np.shape(dfinv_wGG)[0]
            W_wGG = np.zeros_like(dfinv_wGG)
            for iw in range(Nw):
                dfinv_wGG[iw] -= delta_GG
                W_wGG[iw] = dfinv_wGG[iw] * self.Kc_GG

            return df, W_wGG