Add support for La3Ni2O7

moldga/config.py

@@ -88,6 +88,7 @@ def __init__(self):
         self.previous_sc_path: str = "./"
         self.use_lambda_correction: bool = False
         self.restrict_chi_phys: bool = False
+        self.anderson_prev_res: float | None = None
 
 
 class EliashbergConfig:
@@ -148,6 +149,8 @@ def __init__(self):
         self.mu: float = 0.0
         self.n: float = 0.0
         self.n_bands: int = 1
+        self.nd_bands: int = 1
+        self.np_bands: int = 0
         self.occ: np.ndarray = np.ndarray(0)
         self.occ_k: np.ndarray = np.ndarray(0)
         self.occ_dmft: np.ndarray = np.ndarray(0)

moldga/dga_io.py

@@ -31,9 +31,13 @@ def uniquify_path(path: str = None):
     return path
 
 
-def load_from_w2dyn_file_and_update_config() -> tuple[GreensFunction, SelfEnergy, LocalFourPoint, LocalFourPoint]:
+def load_from_w2dyn_file_and_update_config(
+    combine_two_atoms_to_one_obj: bool = False,
+) -> tuple[GreensFunction, SelfEnergy, LocalFourPoint, LocalFourPoint]:
     """
     Loads data from the w2dyn file and updates the config file.
+    If combine_atoms_to_one_obj is True, we are doubling the orbital space and putting the data from the
+    inequivalent atoms into the diagonal blocks after we average over them.
     """
     file = w2dyn_aux.W2dynFile(fname=str(os.path.join(config.dmft.input_path, config.dmft.fname_1p)))
 
@@ -51,36 +55,106 @@ def load_from_w2dyn_file_and_update_config() -> tuple[GreensFunction, SelfEnergy
     config.lattice.interaction.vpp = file.get_vpp()
 
     config.sys.mu = file.get_mu()
-    config.sys.n_bands = file.get_nd() + file.get_np()
+    config.sys.nd_bands = file.get_nd()
+    config.sys.np_bands = file.get_np()
+    config.sys.n_bands = config.sys.nd_bands + config.sys.np_bands
     config.sys.n = file.get_totdens()
     config.sys.occ_dmft = 2 * np.mean(file.get_rho1(), axis=(1, 3))
 
+    if combine_two_atoms_to_one_obj:
+        config.sys.n_bands *= 2
+        config.sys.nd_bands *= 2
+        config.sys.np_bands *= 2
+        config.sys.n *= 2
+
+        config.sys.occ_dmft = np.zeros((config.sys.n_bands, config.sys.n_bands))
+        rho_1_mean = 0.5 * (np.mean(file.get_rho1(), axis=(1, 3)) + np.mean(file.get_rho1(ineq=2), axis=(1, 3)))
+        config.sys.occ_dmft[0:2, 0:2] = 2 * rho_1_mean
+        config.sys.occ_dmft[2:4, 2:4] = 2 * rho_1_mean
+
     if config.sys.n == 0:
         config.sys.n = 2 * np.sum(config.sys.occ_dmft)
 
     file2 = w2dyn_aux.W2dynG4iwFile(fname=str(os.path.join(config.dmft.input_path, config.dmft.fname_2p)))
-    g2_dens = LocalFourPoint(file2.read_g2_full_multiband(config.sys.n_bands, name="dens"), channel=SpinChannel.DENS)
-    g2_magn = LocalFourPoint(file2.read_g2_full_multiband(config.sys.n_bands, name="magn"), channel=SpinChannel.MAGN)
+    g2_dens = LocalFourPoint(
+        file2.read_g2_full_multiband(file.get_nd() + file.get_np(), name="dens"), channel=SpinChannel.DENS
+    )
+    g2_magn = LocalFourPoint(
+        file2.read_g2_full_multiband(file.get_nd() + file.get_np(), name="magn"), channel=SpinChannel.MAGN
+    )
+
+    if combine_two_atoms_to_one_obj:
+        g2_dens_2 = LocalFourPoint(
+            file2.read_g2_full_multiband(file.get_nd() + file.get_np(), ineq=2, name="dens"), channel=SpinChannel.DENS
+        )
+        g2_magn_2 = LocalFourPoint(
+            file2.read_g2_full_multiband(file.get_nd() + file.get_np(), ineq=2, name="magn"), channel=SpinChannel.MAGN
+        )
+
+        def construct_large_g2(g2_1: LocalFourPoint, g2_2: LocalFourPoint) -> LocalFourPoint:
+            g2_mean = 0.5 * (g2_1.mat + g2_2.mat)
+            del g2_2
+            g2_1.mat = np.zeros((4, 4, 4, 4, *g2_mean.shape[4:]))
+            g2_1.mat[0:2, 0:2, 0:2, 0:2] = g2_mean
+            g2_1.mat[2:4, 2:4, 2:4, 2:4] = g2_mean
+            del g2_mean
+            return g2_1
+
+        g2_dens = construct_large_g2(g2_dens, g2_dens_2)
+        g2_magn = construct_large_g2(g2_magn, g2_magn_2)
+
     file2.close()
 
     update_frequency_boxes(g2_dens.niw, g2_dens.niv)
 
     def extend_orbital(arr: np.ndarray) -> np.ndarray:
-        return np.einsum("i...,ij->ij...", arr, np.eye(config.sys.n_bands))
+        return np.einsum("i...,ij->ij...", arr, np.eye(arr.shape[0]))
 
     giw_spin_mean = np.mean(file.get_giw(), axis=1)  # [band,spin,niv]
     niv_dmft = giw_spin_mean.shape[-1] // 2
     niv_cut = config.box.niw_core + config.box.niv_full + 10
     giw_spin_mean = giw_spin_mean[..., niv_dmft - niv_cut : niv_dmft + niv_cut]
     g_dmft = GreensFunction(extend_orbital(giw_spin_mean))
 
+    if combine_two_atoms_to_one_obj:
+        giw_spin_mean_2 = np.mean(file.get_giw(ineq=2), axis=1)[..., niv_dmft - niv_cut : niv_dmft + niv_cut]
+        giw_spin_mean = 0.5 * (giw_spin_mean + giw_spin_mean_2)
+        del giw_spin_mean_2
+        giw_spin_mean_large = np.zeros((2 * giw_spin_mean.shape[0], *giw_spin_mean.shape[1:]))
+        giw_spin_mean_large[0:2] = giw_spin_mean
+        giw_spin_mean_large[2:4] = giw_spin_mean
+        g_dmft = GreensFunction(extend_orbital(giw_spin_mean_large))
+        del giw_spin_mean_large
+
     siw_spin_mean = np.mean(file.get_siw(), axis=1)  # [band,spin,niv]
     siw_spin_mean = extend_orbital(siw_spin_mean)[None, None, None, ...]
     siw_dc_spin_mean = np.mean(file.get_dc(), axis=-1)  # [band,spin]
     siw_dc_spin_mean = extend_orbital(siw_dc_spin_mean)[None, None, None, ..., None]
     siw_spin_mean = siw_spin_mean[..., niv_dmft - niv_cut : niv_dmft + niv_cut]
     sigma_dmft = SelfEnergy(siw_spin_mean, estimate_niv_core=True) + siw_dc_spin_mean
 
+    if combine_two_atoms_to_one_obj:
+        siw_spin_mean_2 = np.mean(file.get_siw(ineq=2), axis=1)
+        siw_spin_mean_2 = extend_orbital(siw_spin_mean_2)[None, None, None, ...]
+        siw_dc_spin_mean_2 = np.mean(file.get_dc(ineq=2), axis=-1)
+        siw_dc_spin_mean_2 = extend_orbital(siw_dc_spin_mean_2)[None, None, None, ..., None]
+        siw_spin_mean_2 = siw_spin_mean_2[..., niv_dmft - niv_cut : niv_dmft + niv_cut]
+        siw_spin_mean = 0.5 * (siw_spin_mean + siw_spin_mean_2)
+        del siw_spin_mean_2
+        siw_dc_spin_mean = 0.5 * (siw_dc_spin_mean + siw_dc_spin_mean_2)
+        del siw_dc_spin_mean_2
+
+        siw_spin_mean_large = np.zeros(
+            (1, 1, 1, 2 * siw_spin_mean.shape[3], 2 * siw_spin_mean.shape[3], siw_spin_mean.shape[-1])
+        )
+        siw_spin_mean_large[:, :, :, 0:2, 0:2, ...] = siw_spin_mean
+        siw_spin_mean_large[:, :, :, 2:4, 2:4, ...] = siw_spin_mean
+        siw_dc_spin_mean_large = np.zeros_like(siw_spin_mean_large)
+        siw_dc_spin_mean_large[:, :, :, 0:2, 0:2, ...] = siw_dc_spin_mean
+        siw_dc_spin_mean_large[:, :, :, 2:4, 2:4, ...] = siw_dc_spin_mean
+        sigma_dmft = SelfEnergy(siw_spin_mean_large, estimate_niv_core=True) + siw_dc_spin_mean_large
+        del siw_spin_mean_large, siw_dc_spin_mean_large
+
     del giw_spin_mean, siw_spin_mean, siw_dc_spin_mean
 
     file.close()
@@ -198,11 +272,17 @@ def set_hamiltonian(er_type: str, er_input: str | list, int_type: str, int_input
     if int_type == "one_band_from_dmft" or int_type == "" or int_type is None:
         return ham.single_band_interaction(config.lattice.interaction.udd)
     elif int_type == "kanamori_from_dmft":
-        return ham.kanamori_interaction(
-            config.sys.n_bands,
+        return ham.kanamori_interaction_dp(
+            config.sys.nd_bands,
+            config.sys.np_bands,
             config.lattice.interaction.udd,
+            config.lattice.interaction.upp,
+            config.lattice.interaction.udp,
             config.lattice.interaction.jdd,
+            config.lattice.interaction.jpp,
+            config.lattice.interaction.jdp,
             config.lattice.interaction.vdd,
+            config.lattice.interaction.vpp,
         )
     elif int_type == "custom":
         if not isinstance(int_input, str):

moldga/hamiltonian.py

@@ -107,26 +107,81 @@ def interaction_orbital_diagonal(self, u: float, n_bands: int = 1) -> "Hamiltoni
 
         return self._add_interaction_term(interaction_elements)
 
-    def kanamori_interaction(self, n_bands: int, udd: float, jdd: float, vdd: float = None) -> "Hamiltonian":
+    def kanamori_interaction_d(self, n_bands: int, udd: float, jdd: float, vdd: float = None) -> "Hamiltonian":
         """
-        Adds the Kanamori interaction terms to the Hamiltonian. The interaction terms are defined by the Hubbard 'udd' (U),
-        the exchange interaction 'jdd' (J) and the pair hopping 'vdd' (V or sometimes U'). 'vdd' is an optional parameter, if left empty,
-        it is set to U-2J.
+        Adds the Kanamori interaction terms ONLY for d orbitals to the Hamiltonian.
+        The interaction terms are defined by the Hubbard 'udd' (U),
+        the exchange interaction 'jdd' (J) and the pair hopping 'vdd' (V or sometimes U').
+        'vdd' is an optional parameter, if left empty, it is set to V=U-2J.
         """
+        return self.kanamori_interaction_dp(nd_bands=n_bands, udd=udd, jdd=jdd, vdd=vdd)
+
+    def kanamori_interaction_p(self, n_bands: int, upp: float, jpp: float, vpp: float = None) -> "Hamiltonian":
+        """
+        Adds the Kanamori interaction terms ONLY for p orbitals to the Hamiltonian.
+        The interaction terms are defined by the Hubbard 'udd' (U),
+        the exchange interaction 'jpp' (J) and the pair hopping 'vpp' (V or sometimes U').
+        'vpp' is an optional parameter, if left empty, it is set to V=U-2J.
+        """
+        return self.kanamori_interaction_dp(np_bands=n_bands, upp=upp, jpp=jpp, vpp=vpp)
+
+    def kanamori_interaction_dp(
+        self,
+        nd_bands: int = 0,
+        np_bands: int = 0,
+        udd: float = 0.0,
+        upp: float = 0.0,
+        udp: float = 0.0,
+        jdd: float = 0.0,
+        jpp: float = 0.0,
+        jdp: float = 0.0,
+        vdd: float = None,
+        vpp: float = None,
+    ) -> "Hamiltonian":
+        """
+        Adds the full Kanamori interaction terms for d and p orbitals to the Hamiltonian.
+        The interaction terms are defined by the local interaction Hubbard U,
+        the exchange interaction J and the pair hopping V or sometimes U'.
+        vdd (vpp) (vdp) are optional parameters, if left empty, they are set to V=U-2J.
+        """
+
+        # Default V=U-2J
         if vdd is None:
             vdd = udd - 2 * jdd
+        if vpp is None:
+            vpp = upp - 2 * jpp
+
+        def orb_type(i: int) -> str:
+            return "d" if i < nd_bands else "p"  # d bands come first
+
+        def get_params(o1: int, o2: int) -> tuple[float, float, float]:
+            # Return correct (U, J, V) depending on orbital types of o1, o2
+            t1, t2 = orb_type(o1), orb_type(o2)
+
+            if t1 == "d" and t2 == "d":
+                return udd, jdd, vdd
+            elif t1 == "p" and t2 == "p":
+                return upp, jpp, vpp
+            else:
+                return 0, jdp, udp  # udp is used as "Vdp", since there is no Udp possible that fits U_llll
 
         r_loc = [0, 0, 0]
+        n_tot = nd_bands + np_bands
 
         interaction_elements = []
-        for a, b, c, d in it.product(range(n_bands), repeat=4):
+
+        for a, b, c, d in it.product(range(n_tot), repeat=4):
             bands = [a + 1, b + 1, c + 1, d + 1]
+
+            # choose parameters based on (a,b) pair (equivalently (c,d))
+            u, j, v = get_params(a, b)
+
             if a == b == c == d:  # U_{llll}
-                interaction_elements.append(InteractionElement(r_loc, bands, udd))
-            elif (a == d and b == c) or (a == b and c == d):  # U_{lmml} or U_{llmm}
-                interaction_elements.append(InteractionElement(r_loc, bands, jdd))
+                interaction_elements.append(InteractionElement(r_loc, bands, u))
+            elif (a == d and b == c) or (a == b and c == d):  # U_{lmml}, U_{llmm}
+                interaction_elements.append(InteractionElement(r_loc, bands, j))
             elif a == c and b == d:  # U_{lmlm}
-                interaction_elements.append(InteractionElement(r_loc, bands, vdd))
+                interaction_elements.append(InteractionElement(r_loc, bands, v))
 
         return self._add_interaction_term(interaction_elements)
 

moldga/nonlocal_sde.py

@@ -496,9 +496,7 @@ def read_last_n_sigmas_from_files(n: int, output_path: str = "./", previous_sc_p
     last_iterations = sorted(
         [(int(match.group(1)), f) for f in files if (match := re.search(r"sigma_dga_iteration_(\d+)\.npy$", f))],
         key=lambda x: x[0],
-        reverse=True,
-    )
-    last_iterations = last_iterations[:n] if len(last_iterations) >= n else last_iterations
+    )[-n:]
     return [np.load(file) for _, file in last_iterations]
 
 
@@ -536,7 +534,7 @@ def calculate_self_energy_q(
     sigma_old, starting_iter = get_starting_sigma(config.self_consistency.previous_sc_path, sigma_dmft)
     if starting_iter > 0:
         logger.info(
-            f"Using previous calculation and starting the self-consistency loop at iteration {starting_iter+1}."
+            f"Using previous calculation and starting the self-consistency loop at iteration {starting_iter + 1}."
         )
 
     sigma_old = sigma_old.cut_niv(config.box.niw_core + config.box.niv_full + 10)
@@ -637,7 +635,7 @@ def calculate_self_energy_q(
             config.sys.mu = update_mu(
                 old_mu, config.sys.n, giwk_full.ek, sigma_new.mat, config.sys.beta, sigma_new.fit_smom()[0]
             )
-            # linear mixing of mu to ensure more stable mu convergence
+
             config.sys.mu = (
                 config.self_consistency.mixing * config.sys.mu + (1 - config.self_consistency.mixing) * old_mu
             )
@@ -771,9 +769,96 @@ def get_result(idx: int):
         sigma_new.mat[..., niv - niv_core : niv + niv_core] = get_proposal(-1).reshape(shape) + update.reshape(shape)
 
         logger.info(
-            f"Pulay mixing applied with {n_hist} previous iterations and a mixing parameter of {config.self_consistency.mixing}."
+            f"Pulay mixing applied with {n_hist} previous iterations and "
+            f"a mixing parameter of {config.self_consistency.mixing}."
         )
 
+        return sigma_new
+    if (
+        config.self_consistency.mixing_strategy == "anderson"
+        and current_iter > n_hist
+        and config.self_consistency.save_iter
+        and config.output.save_quantities
+    ):
+        last_sigmas = read_last_n_sigmas_from_files(
+            n_hist, config.output.output_path, config.self_consistency.previous_sc_path
+        )
+
+        niv = sigma_new.current_shape[-1] // 2
+        niv_core = config.box.niv_core
+        sl = slice(niv - niv_core, niv + niv_core)
+
+        sigma_dmft_stacked = np.tile(sigma_dmft.mat, (config.lattice.k_grid.nk_tot, 1, 1, 1))
+
+        last_proposals = [sigma_dmft_stacked] + last_sigmas  # [dmft, s1, ..., s_{n-1}]
+        last_results = last_sigmas + [sigma_new.mat]  # [s1,  s2, ..., s_new]
+        last_proposals = [s[..., sl] for s in last_proposals]
+        last_results = [s[..., sl] for s in last_results]
+
+        shape = last_results[-1].shape
+        n_total = int(np.prod(shape))
+        flat = lambda x: x.reshape(-1)
+
+        # Current residual f_n = F(x_n) - x_n
+        f_curr = flat(last_results[-1]) - flat(last_proposals[-1])
+        f_vec = np.concatenate([f_curr.real, f_curr.imag])
+        norm_f = np.linalg.norm(f_vec)
+
+        # Build dX and dF matrices (n_hist columns)
+        # dX[:,i] = x_{n-i} - x_{n-i-1}  (proposal differences)
+        # dF[:,i] = f_{n-i} - f_{n-i-1}  (residual differences)
+        dx_cols = []
+        df_cols = []
+        for i in range(n_hist):
+            dx = flat(last_proposals[-1 - i]) - flat(last_proposals[-2 - i])
+            dx_cols.append(np.concatenate([dx.real, dx.imag]))
+
+            df_i = flat(last_results[-1 - i]) - flat(last_proposals[-1 - i])
+            df_im1 = flat(last_results[-2 - i]) - flat(last_proposals[-2 - i])
+            df = df_i - df_im1
+            df_cols.append(np.concatenate([df.real, df.imag]))
+
+        dx_matrix = np.column_stack(dx_cols)  # (2*n_total, n_hist)
+        df_matrix = np.column_stack(df_cols)  # (2*n_total, n_hist)
+
+        # Anderson: solve min ||f_curr - dF @ c||
+        try:
+            u, s, vh = np.linalg.svd(df_matrix, full_matrices=False)
+
+            s_max = s[0] if len(s) > 0 else 1.0
+            cutoff = 1e-5 * s_max
+            mask = s > cutoff
+
+            if not np.any(mask):
+                raise np.linalg.LinAlgError("All singular values below threshold.")
+
+            s_reg = s[mask] / (s[mask] ** 2 + cutoff**2)
+            coeffs = vh[mask].T @ (s_reg * (u[:, mask].T @ f_vec))
+
+        except np.linalg.LinAlgError:
+            logger.warning("Anderson SVD failed — falling back to linear mixing.")
+            return alpha * sigma_new + (1 - alpha) * sigma_old
+
+        # Undamped Anderson proposal: x_n + f_n - (dX + dF) @ c
+        x_n = flat(last_proposals[-1])
+        x_anderson = np.concatenate([x_n.real, x_n.imag]) + f_vec - (dx_matrix + df_matrix) @ coeffs
+        x_anderson = x_anderson[:n_total] + 1j * x_anderson[n_total:]
+
+        # Damp between old proposal and Anderson proposal
+        x_n_complex = x_n
+        candidate = (1 - alpha) * x_n_complex + alpha * x_anderson.reshape(-1)
+
+        # Safety clamp
+        update = candidate - x_n_complex
+        norm_u = np.linalg.norm(update)
+        if norm_f > 0 and norm_u > 3.0 * norm_f:
+            candidate = x_n_complex + update * (3.0 * norm_f / norm_u)
+            logger.warning(f"Anderson step clamped (norm_u={norm_u:.3e}, norm_f={norm_f:.3e}).")
+
+        sigma_new.mat[..., sl] = candidate.reshape(shape)
+
+        logger.info(f"Anderson acceleration applied (m={n_hist}, alpha={alpha:.3f}, norm_f={norm_f:.3e}).")
+
         return sigma_new
 
     sigma_new = config.self_consistency.mixing * sigma_new + (1 - config.self_consistency.mixing) * sigma_old