Feat/n center overlap week1 week2

gbasis/integrals/multi_overlap.py

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+import numpy as np
+from itertools import product
+
+
+def three_gaussian_overlap_primitive(prims):
+    alphas = np.array([p[0] for p in prims])
+    centers = np.array([p[1] for p in prims])
+
+    alpha_tot = np.sum(alphas)
+    P = np.sum(alphas[:, None] * centers, axis=0) / alpha_tot
+
+    term1 = np.sum(alphas * np.sum(centers**2, axis=1))
+    term2 = alpha_tot * np.dot(P, P)
+
+    prefactor = np.exp(-(term1 - term2))
+    return prefactor * (np.pi / alpha_tot) ** 1.5
+
+
+def three_overlap_tensor(basis):
+    """
+    basis: list of primitives [(alpha, center)]
+    returns T[μ, ν, λ]
+    """
+    n = len(basis)
+    T = np.zeros((n, n, n))
+
+    for μ, ν, λ in product(range(n), repeat=3):
+        T[μ, ν, λ] = three_gaussian_overlap_primitive(
+            [basis[μ], basis[ν], basis[λ]]
+        )
+
+    return T

gbasis/integrals/overlap_n.py

@@ -0,0 +1,308 @@
+import numpy as np
+from scipy.sparse import coo_matrix
+from itertools import product
+class PrimitiveNEngine:
+
+    # Gaussian collapse
+    @staticmethod
+    def collapse_gaussians(alphas, centers):
+        alphas = np.asarray(alphas, dtype=np.float64)
+        centers = np.asarray(centers, dtype=np.float64)
+
+        alpha_tot = np.sum(alphas)
+
+        if alpha_tot <= 0.0:
+            raise ValueError("Total Gaussian exponent must be positive.")
+
+        P = np.sum(alphas[:, None] * centers, axis=0) / alpha_tot
+
+        term1 = np.sum(alphas * np.sum(centers**2, axis=1))
+        term2 = alpha_tot * np.dot(P, P)
+
+        exponent = term2 - term1
+        prefactor = np.exp(exponent)
+
+        return alpha_tot, P, prefactor
+    # Pure binomial Hermite shift
+    @staticmethod
+    def hermite_coefficients(l, PA):
+        """
+        Expand (x - A)^l about P:
+
+        (x - A)^l = sum_t E_t (x - P)^t
+        """
+        E = np.zeros(l + 1, dtype=np.float64)
+        E[0] = 1.0
+
+        for i in range(l):
+            E_new = np.zeros(l + 1, dtype=np.float64)
+
+            for t in range(i + 1):
+                E_new[t] += PA * E[t]
+                E_new[t + 1] += E[t]
+
+            E = E_new
+
+        return E
+    # Gaussian moments
+    @staticmethod
+    def gaussian_moments(alpha, max_order):
+        """
+        Compute:
+        ∫ (x-P)^k exp(-alpha (x-P)^2) dx
+        over (-∞,∞)
+        """
+        moments = np.zeros(max_order + 1, dtype=np.float64)
+
+        # zeroth moment
+        moments[0] = np.sqrt(np.pi / alpha)
+
+        # only even moments survive
+        for k in range(0, max_order - 1, 2):
+            moments[k + 2] = (k + 1) / (2.0 * alpha) * moments[k]
+
+        return moments
+    # Full primitive N-center overlap
+    @staticmethod
+    def primitive_overlap(alphas, centers, angmoms):
+
+        alpha_tot, P, prefactor = PrimitiveNEngine.collapse_gaussians(
+            alphas, centers
+        )
+
+        result = prefactor
+
+        # factorize into x, y, z
+        for axis in range(3):
+
+            # build total polynomial via convolution
+            E_total = np.array([1.0], dtype=np.float64)
+
+            for i in range(len(alphas)):
+                l = angmoms[i][axis]
+                PA = P[axis] - centers[i][axis]
+
+                E = PrimitiveNEngine.hermite_coefficients(l, PA)
+
+                E_total = np.convolve(E_total, E)
+
+            moments = PrimitiveNEngine.gaussian_moments(
+                alpha_tot,
+                len(E_total) - 1
+            )
+
+            axis_integral = np.dot(E_total, moments[:len(E_total)])
+
+            result *= axis_integral
+
+        return result
+# Screening Function
+def is_n_shell_overlap_screened(shells, tol=1e-12):
+    """
+    Conservative exponential upper-bound screening
+    for N-center contracted overlap.
+    """
+
+    alpha_mins = [np.min(shell.exps) for shell in shells]
+    centers = [shell.coord for shell in shells]
+
+    alpha_tot = sum(alpha_mins)
+
+    if alpha_tot <= 0.0:
+        return True
+
+    # Exponential decay from Gaussian collapse
+    decay_sum = 0.0
+    N = len(shells)
+
+    for i in range(N):
+        for j in range(i + 1, N):
+            Rij = centers[i] - centers[j]
+            Rij2 = np.dot(Rij, Rij)
+            decay_sum += alpha_mins[i] * alpha_mins[j] * Rij2
+
+    D = decay_sum / alpha_tot
+
+    # Contraction-level magnitude bound
+    coeff_bound = 1.0
+    norm_bound = 1.0
+
+    for shell in shells:
+        coeff_bound *= np.max(np.abs(shell.coeffs))
+        norm_bound *= np.max(np.abs(shell.norm_prim_cart))
+
+    volume_bound = (np.pi / alpha_tot) ** 1.5
+
+    bound = coeff_bound * norm_bound * volume_bound * np.exp(-D)
+
+    return bound < tol
+def contracted_n_overlap(shells):
+    """
+    Compute contracted N-center overlap for a list of
+    GeneralizedContractionShell objects.
+
+    Parameters
+    ----------
+    shells : list[GeneralizedContractionShell]
+
+    Returns
+    -------
+    np.ndarray
+        N-dimensional array over segmented contractions
+        and Cartesian angular components.
+
+        Shape:
+        (M1, L1, M2, L2, ..., MN, LN)
+    """
+
+    N = len(shells)
+
+    # Build shape for final tensor
+    shape = []
+    for shell in shells:
+        shape.append(shell.num_seg_cont)
+        shape.append(shell.num_cart)
+
+    result = np.zeros(shape, dtype=np.float64)
+
+    # Primitive exponent index ranges
+    prim_ranges = [range(len(shell.exps)) for shell in shells]
+
+    # Segmented contraction indices
+    seg_ranges = [range(shell.num_seg_cont) for shell in shells]
+
+    # Cartesian angular component indices
+    cart_ranges = [range(shell.num_cart) for shell in shells]
+
+    for seg_indices in product(*seg_ranges):
+        for cart_indices in product(*cart_ranges):
+
+            total_value = 0.0
+            for prim_indices in product(*prim_ranges):
+
+                alphas = []
+                centers = []
+                angmoms = []
+                coeff_prod = 1.0
+                norm_prod = 1.0
+
+                for i, shell in enumerate(shells):
+
+                    p = prim_indices[i]
+                    m = seg_indices[i]
+                    c = cart_indices[i]
+
+                    alpha = shell.exps[p]
+                    coeff = shell.coeffs[p, m]
+                    norm = shell.norm_prim_cart[c, p]
+                    angmom = tuple(shell.angmom_components_cart[c])
+
+                    alphas.append(alpha)
+                    centers.append(shell.coord)
+                    angmoms.append(angmom)
+
+                    coeff_prod *= coeff
+                    norm_prod *= norm
+
+                prim_val = PrimitiveNEngine.primitive_overlap(
+                    alphas,
+                    centers,
+                    angmoms
+                )
+
+                total_value += coeff_prod * norm_prod * prim_val
+
+            index = []
+            for i in range(N):
+                index.append(seg_indices[i])
+                index.append(cart_indices[i])
+
+            result[tuple(index)] = total_value
+
+    return result
+def build_n_overlap_tensor(shells, tol=1e-12):
+    """
+    Build sparse N-center overlap tensor over shells.
+
+    Parameters
+    ----------
+    shells : list[GeneralizedContractionShell]
+    tol : float
+        Screening tolerance
+
+    Returns
+    -------
+    scipy.sparse.coo_matrix
+        Flattened sparse tensor of shape (total_ao^N, 1)
+    """
+
+    # Total AO dimension
+    shell_sizes = [
+        shell.num_seg_cont * shell.num_cart
+        for shell in shells
+    ]
+
+    total_ao = sum(shell_sizes)
+    N = len(shells)
+
+    data = []
+    rows = []
+
+    # compute AO offsets per shell
+    offsets = []
+    acc = 0
+    for size in shell_sizes:
+        offsets.append(acc)
+        acc += size
+
+    for shell_indices in product(range(len(shells)), repeat=N):
+
+        shell_tuple = [shells[i] for i in shell_indices]
+
+        # Screening
+        if is_n_shell_overlap_screened(shell_tuple, tol=tol):
+            continue
+
+        block = contracted_n_overlap(shell_tuple)
+
+        block_flat = block.reshape(-1)
+
+        local_sizes = [
+            shells[i].num_seg_cont * shells[i].num_cart
+            for i in shell_indices
+        ]
+
+        local_offsets = [
+            offsets[i]
+            for i in shell_indices
+        ]
+
+        for local_idx, value in enumerate(block_flat):
+
+            if abs(value) < tol:
+                continue
+
+            # convert local multi-index to global index
+            multi = []
+            tmp = local_idx
+
+            for size in reversed(local_sizes):
+                multi.append(tmp % size)
+                tmp //= size
+
+            multi = list(reversed(multi))
+
+            global_index = 0
+            for k in range(N):
+                global_index = (
+                    global_index * total_ao
+                    + local_offsets[k]
+                    + multi[k]
+                )
+
+            rows.append(global_index)
+            data.append(value)
+
+    shape = (total_ao ** N, 1)
+
+    return coo_matrix((data, (rows, np.zeros(len(rows)))), shape=shape)