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Updated tests for Jastrow with kappa=0.6
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@ -157,12 +157,12 @@ integer function qmckl_distance_sq_f(context, transa, transb, m, n, &
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return
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endif
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if (iand(transab,2) == 0 .and. LDA < 3) then
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if (iand(transab,2) == 0 .and. LDB < 3) then
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info = QMCKL_INVALID_ARG_7
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return
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endif
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if (iand(transab,2) == 2 .and. LDA < m) then
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if (iand(transab,2) == 2 .and. LDB < n) then
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info = QMCKL_INVALID_ARG_7
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return
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endif
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@ -1328,37 +1328,29 @@ end function test_qmckl_dist_rescaled
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\[
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C_{ij} = \left( 1 - \exp{-\kappa C_{ij}}\right)/\kappa
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C(r_{ij}) = \left( 1 - \exp(-\kappa\, r_{ij})\right)/\kappa
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\]
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Here the gradient is defined as follows:
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\[
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\nabla (C_{ij}(\mathbf{r}_{ee})) = \left(\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta x},\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta y},\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta z} \right)
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\nabla_i C(r_{ij}) = \left(\frac{\partial C(r_{ij})}{\partial x_i},\frac{\partial C(r_{ij})}{\partial y_i},\frac{\partial C(r_{ij})}{\partial z_i} \right)
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\]
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and the Laplacian is defined as follows:
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\[
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\triangle (C_{ij}(r_{ee})) = \frac{\delta^2}{\delta x^2} + \frac{\delta^2}{\delta y^2} + \frac{\delta^2}{\delta z^2}
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\Delta_i C(r_{ij}) = \frac{\partial^2}{\partial x_i^2} + \frac{\partial^2}{\partial y_i^2} + \frac{\partial^2}{\partial z_i^2}
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\]
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Using the above three formulae, the expression for the gradient and Laplacian is
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as follows:
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Using the above three formulas, the expression for the gradient and Laplacian is:
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\[
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\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta x} = \frac{|(x_i - x_j)|}{r_{ij}} (1 - \kappa R_{ij})
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\frac{\partial C(r_{ij})}{\partial x_i} = \frac{|(x_i -
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x_j)|}{r_{ij}} \exp (- \kappa \, r_{ij})
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\]
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\[
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\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta y} = \frac{|(y_i - y_j)|}{r_{ij}} (1 - \kappa R_{ij})
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\]
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\[
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\frac{\delta C_{ij}(\mathbf{r}_{ee})}{\delta z} = \frac{|(z_i - z_j)|}{r_{ij}} (1 - \kappa R_{ij})
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\]
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\[
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\Delta(C_{ij}(r_{ee}) = \left[ \frac{2}{r_{ij}} - \kappa \right] (1-\kappa R_{ij})
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\Delta C_{ij}(r_{ij}) = \left[ \frac{2}{r_{ij}} - \kappa \right] \exp (- \kappa \, r_{ij})
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\]
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If the input array is normal (~'N'~), the xyz coordinates are in
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@ -1432,11 +1424,9 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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integer*8 :: i,j
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real*8 :: x, y, z, dist, dist_inv
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real*8 :: rescale_factor_kappa_inv, rij
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real*8 :: rij
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integer :: transab
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rescale_factor_kappa_inv = 1.0d0/rescale_factor_kappa;
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info = QMCKL_SUCCESS
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if (context == QMCKL_NULL_CONTEXT) then
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@ -1486,27 +1476,7 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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return
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endif
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if (iand(transab,2) == 0 .and. LDA < 3) then
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info = QMCKL_INVALID_ARG_7
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return
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endif
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if (iand(transab,2) == 2 .and. LDA < m) then
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info = QMCKL_INVALID_ARG_7
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return
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endif
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! check for LDB
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if (iand(transab,1) == 0 .and. LDB < 3) then
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info = QMCKL_INVALID_ARG_9
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return
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endif
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if (iand(transab,1) == 1 .and. LDB < n) then
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info = QMCKL_INVALID_ARG_9
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return
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endif
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if (iand(transab,2) == 0 .and. LDB < 3) then
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info = QMCKL_INVALID_ARG_9
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return
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@ -1535,11 +1505,11 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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z = A(3,i) - B(3,j)
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dist = dsqrt(x*x + y*y + z*z)
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dist_inv = 1.0d0/dist
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rij = (1.0d0 - dexp(-rescale_factor_kappa * dist)) * rescale_factor_kappa_inv
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C(1,i,j) = x * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(2,i,j) = y * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(3,i,j) = z * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa_inv) * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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rij = dexp(-rescale_factor_kappa * dist)
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C(1,i,j) = x * dist_inv * rij
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C(2,i,j) = y * dist_inv * rij
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C(3,i,j) = z * dist_inv * rij
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa) * rij
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end do
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end do
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@ -1552,11 +1522,11 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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z = A(i,3) - B(3,j)
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dist = dsqrt(x*x + y*y + z*z)
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dist_inv = 1.0d0/dist
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rij = (1.0d0 - dexp(-rescale_factor_kappa * dist)) * rescale_factor_kappa_inv
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C(1,i,j) = x * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(2,i,j) = y * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(3,i,j) = z * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa_inv) * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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rij = dexp(-rescale_factor_kappa * dist)
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C(1,i,j) = x * dist_inv * rij
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C(2,i,j) = y * dist_inv * rij
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C(3,i,j) = z * dist_inv * rij
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa) * rij
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end do
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end do
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@ -1569,11 +1539,11 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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z = A(3,i) - B(j,3)
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dist = dsqrt(x*x + y*y + z*z)
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dist_inv = 1.0d0/dist
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rij = (1.0d0 - dexp(-rescale_factor_kappa * dist)) * rescale_factor_kappa_inv
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C(1,i,j) = x * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(2,i,j) = y * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(3,i,j) = z * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa_inv) * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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rij = dexp(-rescale_factor_kappa * dist)
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C(1,i,j) = x * dist_inv * rij
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C(2,i,j) = y * dist_inv * rij
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C(3,i,j) = z * dist_inv * rij
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa) * rij
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end do
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end do
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@ -1586,11 +1556,11 @@ integer function qmckl_distance_rescaled_gl_f(context, transa, transb, m, n, &
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z = A(i,3) - B(j,3)
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dist = dsqrt(x*x + y*y + z*z)
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dist_inv = 1.0d0/dist
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rij = (1.0d0 - dexp(-rescale_factor_kappa * dist)) * rescale_factor_kappa_inv
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C(1,i,j) = x * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(2,i,j) = y * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(3,i,j) = z * dist_inv * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa_inv) * ( 1.0d0 - rescale_factor_kappa_inv * rij)
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rij = dexp(-rescale_factor_kappa * dist)
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C(1,i,j) = x * dist_inv * rij
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C(2,i,j) = y * dist_inv * rij
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C(3,i,j) = z * dist_inv * rij
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C(4,i,j) = (2.0d0 * dist_inv - rescale_factor_kappa) * rij
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end do
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end do
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@ -199,6 +199,9 @@ int main() {
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#+BEGIN_SRC python :results none :exports none
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import numpy as np
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kappa = 0.6
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kappa_inv = 1./kappa
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# For H2O we have the following data:
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elec_num = 10
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nucl_num = 2
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@ -219,62 +222,20 @@ elec_coord = np.array( [[[-0.250655104764153 , 0.503070975550133 ,
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[-0.108090166832043 , 0.189161729653261 , 2.15398313919894],
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[ 0.397978144318712 , -0.254277292595981 , 2.54553335476344]]])
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ee_distance_rescaled = np.array([
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[ 0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.550227800352402, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.919155060185168, 0.937695909123175, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.893325429242815, 0.851181978173561, 0.978501685226877,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.982457268305353, 0.976125002619471, 0.994349933143149,
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0.844077311588328, 0.000000000000000, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.482407528408731, 0.414816073699124, 0.894716035479343,
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0.876540187084407, 0.978921170036895, 0.000000000000000,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.459541909660400, 0.545007215761510, 0.883752955884551,
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0.918958134888791, 0.986386936267237, 0.362209822236419,
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0.000000000000000, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.763732576854455, 0.817282762358449, 0.801802919535959,
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0.900089095449775, 0.975704636491453, 0.707836537586060,
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0.755705808346586, 0.000000000000000, 0.000000000000000,
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0.000000000000000 ],
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[ 0.904249454052971, 0.871097965261373, 0.982717262706270,
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0.239901207363622, 0.836519456769083, 0.896135326270534,
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0.930694340243023, 0.917708540815567, 0.000000000000000,
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0.000000000000000 ],
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[ 0.944400908070716, 0.922589018494961, 0.984615718580670,
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0.514328661540623, 0.692362267147064, 0.931894098453677,
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0.956034127544344, 0.931221472309472, 0.540903688625053,
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0.000000000000000 ]])
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ee_distance_rescaled = np.array(\
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[ [(1.-np.exp(-kappa*np.linalg.norm(elec_coord[0,j,:]-elec_coord[0,i,:])))/kappa \
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for i in range(elec_num) ]
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for j in range(elec_num) ])
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en_distance_rescaled = np.transpose(np.array([
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[ 0.443570948411811 , 0.467602196999105 , 0.893870160799932 ,
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0.864347190364447 , 0.976608182392358 , 0.187563183468210 ,
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0.426404699872689 , 0.665107090128166 , 0.885246991424583 ,
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0.924902909715270 ],
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[ 0.899360150637444 , 0.860035135365386 , 0.979659405613798 ,
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6.140678415933776E-002, 0.835118398056681 , 0.884071658981068 ,
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0.923860000907362 , 0.905203414522289 , 0.211286300932359 ,
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0.492104840907350 ]]))
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en_distance_rescaled = \
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np.array([ [(1.-np.exp(-kappa*np.linalg.norm(elec_coord[0,j,:]-nucl_coord[:,i])))/kappa \
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for j in range(elec_num) ]
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for i in range(nucl_num) ])
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# symmetrize it
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for i in range(elec_num):
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for j in range(elec_num):
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ee_distance_rescaled[i][j] = ee_distance_rescaled[j][i]
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#for i in range(elec_num):
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# for j in range(elec_num):
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# ee_distance_rescaled[i][j] = ee_distance_rescaled[j][i]
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# For N2, we have the following data:
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type_nucl_num = 1
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@ -283,6 +244,7 @@ bord_num = 5
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cord_num = 5
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dim_c_vector= 23
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type_nucl_vector = [ 0, 0]
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a_vector = np.array([
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[0.000000000000000E+000],
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[0.000000000000000E+000],
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@ -343,8 +305,6 @@ lkpm_combined_index = [[1 , 1 , 2 , 0],
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[3 , 0 , 5 , 1],
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[1 , 0 , 5 , 2]]
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kappa = 1.0
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kappa_inv = 1.0/kappa
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#+END_SRC
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** Data structure
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@ -1573,8 +1533,8 @@ int64_t elec_num = n2_elec_num;
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int64_t elec_up_num = n2_elec_up_num;
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int64_t elec_dn_num = n2_elec_dn_num;
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int64_t nucl_num = n2_nucl_num;
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double rescale_factor_ee = 1.0;
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double rescale_factor_en[2] = { 1.0, 1.0 };
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double rescale_factor_ee = 0.6;
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double rescale_factor_en[2] = { 0.6, 0.6 };
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double* elec_coord = &(n2_elec_coord[0][0][0]);
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const double* nucl_charge = n2_charge;
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@ -1980,11 +1940,11 @@ print("asym_one : ", asym_one)
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print("asymp_jasb[0] : ", asymp_jasb[0])
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print("asymp_jasb[1] : ", asymp_jasb[1])
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#+end_src
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#+RESULTS: asymp_jasb
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: asym_one : 0.43340325572525706
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: asymp_jasb[0] : 0.31567342786262853
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: asymp_jasb[1] : 0.5323750557252571
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: asym_one : 0.6634291325000664
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: asymp_jasb[0] : 0.7115733522582638
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: asymp_jasb[1] : 1.043287918508297
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#+begin_src c :tangle (eval c_test)
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assert(qmckl_electron_provided(context));
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@ -2070,8 +2030,8 @@ double asymp_jasb[2];
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rc = qmckl_get_jastrow_champ_asymp_jasb(context, asymp_jasb,2);
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// calculate asymp_jasb
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assert(fabs(asymp_jasb[0]-0.31567342786262853) < 1.e-12);
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assert(fabs(asymp_jasb[1]-0.5323750557252571) < 1.e-12);
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assert(fabs(asymp_jasb[0]-0.7115733522582638) < 1.e-12);
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assert(fabs(asymp_jasb[1]-1.043287918508297 ) < 1.e-12);
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#+end_src
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@ -2502,31 +2462,32 @@ factor_ee = 0.0
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for i in range(0,elec_num):
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for j in range(0,i):
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x = ee_distance_rescaled[i][j]
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pow_ser = 0.0
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spin_fact = 1.0
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ipar = 0
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pow_ser = 0.0
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for p in range(1,bord_num):
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x = x * ee_distance_rescaled[i][j]
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pow_ser = pow_ser + b_vector[p + 1] * x
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pow_ser += b_vector[p+1] * x
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if(i < up_num or j >= up_num):
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spin_fact = 0.5
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ipar = 0
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else:
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ipar = 1
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spin_fact = 1.0
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factor_ee = factor_ee + spin_fact * b_vector[0] * ee_distance_rescaled[i][j] \
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factor_ee += spin_fact * b_vector[0] * ee_distance_rescaled[i][j] \
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/ (1.0 + b_vector[1] * ee_distance_rescaled[i][j]) \
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- asymp_jasb[ipar] + pow_ser
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- asymp_jasb[ipar] + pow_ser
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print("factor_ee :",factor_ee)
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#+end_src
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#+RESULTS:
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: asym_one : 0.43340325572525706
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: asymp_jasb[0] : 0.5323750557252571
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: asymp_jasb[1] : 0.31567342786262853
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: factor_ee : -4.282760865958113
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|
||||
: asym_one : 0.6634291325000664
|
||||
: asymp_jasb[0] : 0.7115733522582638
|
||||
: asymp_jasb[1] : 1.043287918508297
|
||||
: factor_ee : -16.83886184243964
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
/* Check if Jastrow is properly initialized */
|
||||
@ -2538,8 +2499,8 @@ rc = qmckl_check(context,
|
||||
);
|
||||
|
||||
// calculate factor_ee
|
||||
printf("%e\n%e\n\n",factor_ee[0],-4.282760865958113 );
|
||||
assert(fabs(factor_ee[0]+4.282760865958113) < 1.e-12);
|
||||
printf("%e\n%e\n\n",factor_ee[0],-16.83886184243964);
|
||||
assert(fabs(factor_ee[0]+16.83886184243964) < 1.e-12);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -3025,7 +2986,7 @@ import numpy as np
|
||||
|
||||
<<asymp_jasb>>
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
dx = 1.e-3
|
||||
|
||||
elec_coord = np.array(elec_coord)[0]
|
||||
@ -3039,6 +3000,8 @@ def make_dist(elec_coord):
|
||||
return elec_dist
|
||||
|
||||
|
||||
|
||||
|
||||
def make_dist_deriv(elec_coord):
|
||||
|
||||
elec_dist_d = np.zeros(shape=(4, elec_num, elec_num),dtype=float)
|
||||
@ -3105,7 +3068,6 @@ for j in range(elec_num):
|
||||
invden2 = invden * invden
|
||||
invden3 = invden2 * invden
|
||||
xinv = 1.0 / x
|
||||
ipar = 1
|
||||
|
||||
dx[:] = ee_distance_rescaled_gl[:,j,i]
|
||||
|
||||
@ -3131,13 +3093,13 @@ print("factor_ee_gl[3][0]:",factor_ee_gl[3][0])
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: asym_one : 0.43340325572525706
|
||||
: asymp_jasb[0] : 0.31567342786262853
|
||||
: asymp_jasb[1] : 0.5323750557252571
|
||||
: factor_ee_gl[0][0]: -0.16364894652107934
|
||||
: factor_ee_gl[1][0]: 0.6927548119830084
|
||||
: factor_ee_gl[2][0]: -0.073267755223968
|
||||
: factor_ee_gl[3][0]: 1.5111672803213185
|
||||
: asym_one : 0.6634291325000664
|
||||
: asymp_jasb[0] : 0.7115733522582638
|
||||
: asymp_jasb[1] : 1.043287918508297
|
||||
: factor_ee_gl[0][0]:
|
||||
: factor_ee_gl[1][0]:
|
||||
: factor_ee_gl[2][0]:
|
||||
: factor_ee_gl[3][0]:
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
@ -3149,17 +3111,17 @@ double factor_ee_gl[walk_num][4][elec_num];
|
||||
rc = qmckl_get_jastrow_champ_factor_ee_gl(context, &(factor_ee_gl[0][0][0]),walk_num*4*elec_num);
|
||||
|
||||
// check factor_ee_gl
|
||||
printf("%f %f\n", factor_ee_gl[0][0][0], -0.16364894652107934);
|
||||
assert(fabs(factor_ee_gl[0][0][0]+0.16364894652107934) < 1.e-12);
|
||||
printf("%f %f\n", factor_ee_gl[0][0][0], -0.39319353942687446);
|
||||
assert(fabs(factor_ee_gl[0][0][0]+0.39319353942687446) < 1.e-12);
|
||||
|
||||
printf("%f %f\n", factor_ee_gl[0][1][0], 0.6927548119830084 );
|
||||
assert(fabs(factor_ee_gl[0][1][0]-0.6927548119830084 ) < 1.e-12);
|
||||
printf("%f %f\n", factor_ee_gl[0][1][0], 1.0535615450668214);
|
||||
assert(fabs(factor_ee_gl[0][1][0]-1.0535615450668214) < 1.e-12);
|
||||
|
||||
printf("%f %f\n", factor_ee_gl[0][2][0],-0.073267755223968 );
|
||||
assert(fabs(factor_ee_gl[0][2][0]+0.073267755223968 ) < 1.e-12);
|
||||
printf("%f %f\n", factor_ee_gl[0][2][0],-0.39098406960784515);
|
||||
assert(fabs(factor_ee_gl[0][2][0]+0.39098406960784515) < 1.e-12);
|
||||
|
||||
printf("%f %f\n", factor_ee_gl[0][3][0], 1.5111672803213185 );
|
||||
assert(fabs(factor_ee_gl[0][3][0]-1.5111672803213185 ) < 1.e-12);
|
||||
printf("%f %f\n", factor_ee_gl[0][3][0],2.8650469630854483);
|
||||
assert(fabs(factor_ee_gl[0][3][0]-2.8650469630854483) < 1.e-12);
|
||||
#+end_src
|
||||
|
||||
*** Electron-electron rescaled distances
|
||||
@ -3371,7 +3333,7 @@ qmckl_exit_code qmckl_compute_ee_distance_rescaled (
|
||||
#+begin_src python :results output :exports none
|
||||
import numpy as np
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
elec_1_w1 = np.array( [-0.250655104764153, 0.503070975550133 , -0.166554344502303])
|
||||
elec_2_w1 = np.array( [-0.587812193472177, -0.128751981129274 , 0.187773606533075])
|
||||
@ -3388,11 +3350,11 @@ print ( "[6][5] : ", (1.0 - np.exp(-kappa * np.linalg.norm(elec_6_w1-elec_5_w1))
|
||||
|
||||
#+RESULTS:
|
||||
: [0][0] : 0.0
|
||||
: [0][1] : 0.5502278003524018
|
||||
: [1][0] : 0.5502278003524018
|
||||
: [0][1] :
|
||||
: [1][0] : 0.6347507420688708
|
||||
: [5][5] : 0.0
|
||||
: [5][6] : 0.3622098222364193
|
||||
: [6][5] : 0.3622098222364193
|
||||
: [5][6] : 0.3941735387855409
|
||||
: [6][5] :
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
assert(qmckl_electron_provided(context));
|
||||
@ -3409,7 +3371,7 @@ assert(ee_distance_rescaled[0] == 0.);
|
||||
assert(ee_distance_rescaled[1] == ee_distance_rescaled[elec_num]);
|
||||
|
||||
// value of (1,0,0)
|
||||
assert(fabs(ee_distance_rescaled[1]-0.5502278003524018) < 1.e-12);
|
||||
assert(fabs(ee_distance_rescaled[1]-0.6347507420688708) < 1.e-12);
|
||||
|
||||
// (0,0,1) == 0.
|
||||
assert(ee_distance_rescaled[5*elec_num + 5] == 0.);
|
||||
@ -3418,7 +3380,7 @@ assert(ee_distance_rescaled[5*elec_num + 5] == 0.);
|
||||
assert(ee_distance_rescaled[5*elec_num+6] == ee_distance_rescaled[6*elec_num+5]);
|
||||
|
||||
// value of (1,0,1)
|
||||
assert(fabs(ee_distance_rescaled[5*elec_num+6]-0.3622098222364193) < 1.e-12);
|
||||
assert(fabs(ee_distance_rescaled[5*elec_num+6]-0.3941735387855409) < 1.e-12);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -3914,15 +3876,15 @@ print("asymp_jasa[i] : ", asymp_jasa)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS: asymp_jasa
|
||||
: asymp_jasa[i] : [0.548554]
|
||||
: asymp_jasa[i] : [1.75529774]
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
double asymp_jasa[2];
|
||||
rc = qmckl_get_jastrow_champ_asymp_jasa(context, asymp_jasa, type_nucl_num);
|
||||
|
||||
// calculate asymp_jasb
|
||||
printf("%e %e\n", asymp_jasa[0], 0.548554);
|
||||
assert(fabs(0.548554 - asymp_jasa[0]) < 1.e-12);
|
||||
printf("%e %e\n", asymp_jasa[0], 1.75529774);
|
||||
assert(fabs(1.75529774 - asymp_jasa[0]) < 1.e-8);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -4296,11 +4258,11 @@ import numpy as np
|
||||
factor_en = 0.0
|
||||
for a in range(0,nucl_num):
|
||||
for i in range(0,elec_num):
|
||||
x = en_distance_rescaled[i][a]
|
||||
x = en_distance_rescaled[a][i]
|
||||
pow_ser = 0.0
|
||||
|
||||
for p in range(2,aord_num+1):
|
||||
x = x * en_distance_rescaled[i][a]
|
||||
x = x * en_distance_rescaled[a][i]
|
||||
pow_ser = pow_ser + a_vector[(p-1) + 1][type_nucl_vector[a]] * x
|
||||
|
||||
factor_en = factor_en - a_vector[0][type_nucl_vector[a]] * x \
|
||||
@ -4312,8 +4274,8 @@ print("factor_en :",factor_en)
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: asymp_jasa[i] : [0.548554]
|
||||
: factor_en : -5.1052574308112755
|
||||
: asymp_jasa[i] : [1.75529774]
|
||||
: factor_en :
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
@ -4324,8 +4286,8 @@ double factor_en[walk_num];
|
||||
rc = qmckl_get_jastrow_champ_factor_en(context, factor_en,walk_num);
|
||||
|
||||
// calculate factor_en
|
||||
printf("%f %f\n", factor_en[0], -5.1052574308112755);
|
||||
assert(fabs(-5.1052574308112755 - factor_en[0]) < 1.e-12);
|
||||
printf("%f %f\n", factor_en[0], -22.781375792083587);
|
||||
assert(fabs(-22.781375792083587 - factor_en[0]) < 1.e-12);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -4375,6 +4337,22 @@ qmckl_get_jastrow_champ_factor_en_gl(qmckl_context context,
|
||||
}
|
||||
#+end_src
|
||||
|
||||
***** Fortran interface
|
||||
|
||||
#+begin_src f90 :tangle (eval fh_func) :comments org
|
||||
interface
|
||||
integer(qmckl_exit_code) function qmckl_get_jastrow_champ_factor_en_gl (context, &
|
||||
factor_en_gl, size_max) bind(C)
|
||||
use, intrinsic :: iso_c_binding
|
||||
import
|
||||
implicit none
|
||||
integer (qmckl_context) , intent(in), value :: context
|
||||
integer(c_int64_t), intent(in), value :: size_max
|
||||
double precision, intent(out) :: factor_en_gl(size_max)
|
||||
end function qmckl_get_jastrow_champ_factor_en_gl
|
||||
end interface
|
||||
#+end_src
|
||||
|
||||
**** Provide :noexport:
|
||||
#+begin_src c :comments org :tangle (eval h_private_func) :noweb yes :exports none
|
||||
qmckl_exit_code qmckl_provide_jastrow_champ_factor_en_gl(qmckl_context context);
|
||||
@ -4472,17 +4450,17 @@ qmckl_exit_code qmckl_provide_jastrow_champ_factor_en_gl(qmckl_context context)
|
||||
:END:
|
||||
|
||||
#+NAME: qmckl_factor_en_gl_args
|
||||
| Variable | Type | In/Out | Description |
|
||||
|--------------------------------+-------------------------------------------+--------+---------------------------------------|
|
||||
| ~context~ | ~qmckl_context~ | in | Global state |
|
||||
| ~walk_num~ | ~int64_t~ | in | Number of walkers |
|
||||
| ~elec_num~ | ~int64_t~ | in | Number of electrons |
|
||||
| ~nucl_num~ | ~int64_t~ | in | Number of nuclei |
|
||||
| ~type_nucl_num~ | ~int64_t~ | in | Number of unique nuclei |
|
||||
| ~type_nucl_vector~ | ~int64_t[nucl_num]~ | in | IDs of unique nuclei |
|
||||
| ~aord_num~ | ~int64_t~ | in | Number of coefficients |
|
||||
| ~a_vector~ | ~double[aord_num+1][type_nucl_num]~ | in | List of coefficients |
|
||||
| ~en_distance_rescaled~ | ~double[walk_num][nucl_num][elec_num]~ | in | Electron-nucleus distances |
|
||||
| Variable | Type | In/Out | Description |
|
||||
|---------------------------+-------------------------------------------+--------+---------------------------------------|
|
||||
| ~context~ | ~qmckl_context~ | in | Global state |
|
||||
| ~walk_num~ | ~int64_t~ | in | Number of walkers |
|
||||
| ~elec_num~ | ~int64_t~ | in | Number of electrons |
|
||||
| ~nucl_num~ | ~int64_t~ | in | Number of nuclei |
|
||||
| ~type_nucl_num~ | ~int64_t~ | in | Number of unique nuclei |
|
||||
| ~type_nucl_vector~ | ~int64_t[nucl_num]~ | in | IDs of unique nuclei |
|
||||
| ~aord_num~ | ~int64_t~ | in | Number of coefficients |
|
||||
| ~a_vector~ | ~double[aord_num+1][type_nucl_num]~ | in | List of coefficients |
|
||||
| ~en_distance_rescaled~ | ~double[walk_num][nucl_num][elec_num]~ | in | Electron-nucleus distances |
|
||||
| ~en_distance_rescaled_gl~ | ~double[walk_num][4][nucl_num][elec_num]~ | in | Electron-nucleus distance derivatives |
|
||||
| ~factor_en_gl~ | ~double[walk_num][4][elec_num]~ | out | Electron-nucleus jastrow |
|
||||
|
||||
@ -4662,7 +4640,7 @@ import numpy as np
|
||||
|
||||
<<jastrow_data>>
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
elec_coord = np.array(elec_coord)[0]
|
||||
nucl_coord = np.array(nucl_coord)
|
||||
@ -4682,7 +4660,7 @@ for a in range(nucl_num):
|
||||
en_distance_rescaled_gl = np.zeros(shape=(4,elec_num,nucl_num),dtype=float)
|
||||
for a in range(nucl_num):
|
||||
for i in range(elec_num):
|
||||
f = 1.0 - kappa * en_distance_rescaled[i][a]
|
||||
f = 1.0 - kappa * en_distance_rescaled[a][i]
|
||||
for ii in range(4):
|
||||
en_distance_rescaled_gl[ii][i][a] = elnuc_dist_gl[ii][i][a]
|
||||
en_distance_rescaled_gl[3][i][a] = en_distance_rescaled_gl[3][i][a] + \
|
||||
@ -4698,7 +4676,7 @@ dx = np.zeros(shape=(4),dtype=float)
|
||||
pow_ser_g = np.zeros(shape=(3),dtype=float)
|
||||
for a in range(nucl_num):
|
||||
for i in range(elec_num):
|
||||
x = en_distance_rescaled[i][a]
|
||||
x = en_distance_rescaled[a][i]
|
||||
if abs(x) < 1e-18:
|
||||
continue
|
||||
pow_ser_g = np.zeros(shape=(3),dtype=float)
|
||||
@ -4715,7 +4693,7 @@ for a in range(nucl_num):
|
||||
lap2 = 0.0
|
||||
lap3 = 0.0
|
||||
for ii in range(3):
|
||||
x = en_distance_rescaled[i][a]
|
||||
x = en_distance_rescaled[a][i]
|
||||
if x < 1e-18:
|
||||
continue
|
||||
for p in range(2,aord_num+1):
|
||||
@ -4723,7 +4701,7 @@ for a in range(nucl_num):
|
||||
pow_ser_g[ii] = pow_ser_g[ii] + y * dx[ii]
|
||||
lap1 = lap1 + (p - 1) * y * xinv * dx[ii] * dx[ii]
|
||||
lap2 = lap2 + y
|
||||
x = x * en_distance_rescaled[i][a]
|
||||
x = x * en_distance_rescaled[a][i]
|
||||
|
||||
lap3 = lap3 - 2.0 * a_vector[1][type_nucl_vector[a]] * dx[ii] * dx[ii]
|
||||
|
||||
@ -4744,16 +4722,11 @@ print("factor_en_gl[3][0]:",factor_en_gl[3][0])
|
||||
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: factor_en_gl[0][0]: -0.11609919541763383
|
||||
: factor_en_gl[1][0]: 0.23301394780804574
|
||||
: factor_en_gl[2][0]: -0.17548337641865783
|
||||
: factor_en_gl[3][0]: 0.9667363412285741
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
: factor_en_gl[0][0]: 0.11609919541763383
|
||||
: factor_en_gl[1][0]: -0.23301394780804574
|
||||
: factor_en_gl[2][0]: 0.17548337641865783
|
||||
: factor_en_gl[3][0]: -0.9667363412285741
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
@ -4765,10 +4738,10 @@ double factor_en_gl[walk_num][4][elec_num];
|
||||
rc = qmckl_get_jastrow_champ_factor_en_gl(context, &(factor_en_gl[0][0][0]),walk_num*4*elec_num);
|
||||
|
||||
// check factor_en_gl
|
||||
assert(fabs(factor_en_gl[0][0][0]+0.11609919541763383) < 1.e-12);
|
||||
assert(fabs(factor_en_gl[0][1][0]-0.23301394780804574) < 1.e-12);
|
||||
assert(fabs(factor_en_gl[0][2][0]+0.17548337641865783) < 1.e-12);
|
||||
assert(fabs(factor_en_gl[0][3][0]-0.9667363412285741 ) < 1.e-12);
|
||||
assert(fabs( -0.19656663796630847 - factor_en_gl[0][0][0]) < 1.e-12);
|
||||
assert(fabs( 0.3945140890522283 - factor_en_gl[0][1][0]) < 1.e-12);
|
||||
assert(fabs( -0.5082964671286118 - factor_en_gl[0][2][0]) < 1.e-12);
|
||||
assert(fabs( 1.8409460670666289 - factor_en_gl[0][3][0]) < 1.e-12);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -5034,7 +5007,7 @@ qmckl_exit_code qmckl_compute_en_distance_rescaled (
|
||||
#+begin_src python :results output :exports none
|
||||
import numpy as np
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
elec_1_w1 = np.array( [-0.250655104764153, 0.503070975550133 , -0.166554344502303])
|
||||
elec_2_w1 = np.array( [-0.587812193472177, -0.128751981129274 , 0.187773606533075])
|
||||
@ -5053,12 +5026,13 @@ print ( "[0][6] : ", (1.0 - np.exp(-kappa * np.linalg.norm(elec_6_w1-nucl_1)) )/
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: [0][0] : 0.4435709484118112
|
||||
: [1][0] : 0.8993601506374442
|
||||
: [0][1] : 0.46760219699910477
|
||||
: [0][5] : 0.1875631834682101
|
||||
: [1][5] : 0.8840716589810682
|
||||
: [0][6] : 0.42640469987268914
|
||||
: [0][0] : 0.4942158656729477
|
||||
: [1][0] : 1.2464137498005765
|
||||
: [0][1] : 0.5248654474756858
|
||||
: [0][5] : 0.19529459944794733
|
||||
: [1][5] : 1.2091967687767369
|
||||
: [0][6] : 0.4726452953409436
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
|
||||
@ -5074,22 +5048,18 @@ assert (rc == QMCKL_SUCCESS);
|
||||
|
||||
// (e,n,w) in Fortran notation
|
||||
// (1,1,1)
|
||||
assert(fabs(en_distance_rescaled[0][0][0] - 0.4435709484118112) < 1.e-12);
|
||||
|
||||
assert(fabs(en_distance_rescaled[0][0][0] - 0.4942158656729477) < 1.e-12);
|
||||
// (1,2,1)
|
||||
assert(fabs(en_distance_rescaled[0][1][0] - 0.8993601506374442) < 1.e-12);
|
||||
|
||||
assert(fabs(en_distance_rescaled[0][1][0] - 1.2464137498005765) < 1.e-12);
|
||||
// (2,1,1)
|
||||
assert(fabs(en_distance_rescaled[0][0][1] - 0.46760219699910477) < 1.e-12);
|
||||
|
||||
assert(fabs(en_distance_rescaled[0][0][1] - 0.5248654474756858) < 1.e-12);
|
||||
// (1,1,2)
|
||||
assert(fabs(en_distance_rescaled[0][0][5] - 0.1875631834682101) < 1.e-12);
|
||||
|
||||
assert(fabs(en_distance_rescaled[0][0][5] - 0.19529459944794733) < 1.e-12);
|
||||
// (1,2,2)
|
||||
assert(fabs(en_distance_rescaled[0][1][5] - 0.8840716589810682) < 1.e-12);
|
||||
|
||||
assert(fabs(en_distance_rescaled[0][1][5] - 1.2091967687767369) < 1.e-12);
|
||||
// (2,1,2)
|
||||
assert(fabs(en_distance_rescaled[0][0][6] - 0.42640469987268914) < 1.e-12);
|
||||
assert(fabs(en_distance_rescaled[0][0][6] - 0.4726452953409436) < 1.e-12);
|
||||
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -5847,7 +5817,7 @@ for i in range(elec_num):
|
||||
for j in range(elec_num):
|
||||
elec_dist[i, j] = np.linalg.norm(elec_coord[i] - elec_coord[j])
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_e_ij = np.zeros(shape=(elec_num * (elec_num - 1)//2, cord_num+1), dtype=float)
|
||||
een_rescaled_e_ij[:,0] = 1.0
|
||||
@ -5887,12 +5857,12 @@ print(" een_rescaled_e[1, 5, 2] = ",een_rescaled_e[1, 5, 2])
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: een_rescaled_e[0, 2, 1] = 0.08084493981483197
|
||||
: een_rescaled_e[0, 3, 1] = 0.1066745707571846
|
||||
: een_rescaled_e[0, 4, 1] = 0.017542731694647366
|
||||
: een_rescaled_e[1, 3, 2] = 0.02214680362033448
|
||||
: een_rescaled_e[1, 4, 2] = 0.0005700154999202759
|
||||
: een_rescaled_e[1, 5, 2] = 0.3424402276009091
|
||||
: een_rescaled_e[0, 2, 1] = 0.2211015082992776
|
||||
: een_rescaled_e[0, 3, 1] = 0.2611178387068169
|
||||
: een_rescaled_e[0, 4, 1] = 0.08840123507637472
|
||||
: een_rescaled_e[1, 3, 2] = 0.10166855073546568
|
||||
: een_rescaled_e[1, 4, 2] = 0.011311807324686948
|
||||
: een_rescaled_e[1, 5, 2] = 0.5257156022077619
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
assert(qmckl_electron_provided(context));
|
||||
@ -5902,12 +5872,12 @@ double een_rescaled_e[walk_num][(cord_num + 1)][elec_num][elec_num];
|
||||
rc = qmckl_get_jastrow_champ_een_rescaled_e(context, &(een_rescaled_e[0][0][0][0]),elec_num*elec_num*(cord_num+1)*walk_num);
|
||||
|
||||
// value of (0,2,1)
|
||||
assert(fabs(een_rescaled_e[0][1][0][2]-0.08084493981483197) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][1][0][3]-0.1066745707571846) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][1][0][4]-0.01754273169464735) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][3]-0.02214680362033448) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][4]-0.0005700154999202759) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][5]-0.3424402276009091) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][1][0][2]- 0.2211015082992776 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][1][0][3]- 0.2611178387068169 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][1][0][4]- 0.0884012350763747 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][3]- 0.1016685507354656 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][4]- 0.0113118073246869 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e[0][2][1][5]- 0.5257156022077619 ) < 1.e-12);
|
||||
#+end_src
|
||||
|
||||
*** Electron-electron rescaled distances derivatives in $J_\text{eeN}$
|
||||
@ -6226,7 +6196,7 @@ for j in range(elec_num):
|
||||
elec_dist_gl[:, j, j] = 0.0
|
||||
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_e_ij = np.zeros(shape=(elec_num * (elec_num - 1)//2, cord_num+1), dtype=float)
|
||||
een_rescaled_e_ij[:,0] = 1.0
|
||||
@ -6268,22 +6238,14 @@ for l in range(0,cord_num+1):
|
||||
for ii in range(0,4):
|
||||
een_rescaled_e_gl[i,ii,j,l] = een_rescaled_e_gl[i,ii,j,l] * een_rescaled_e[i,j,l]
|
||||
|
||||
#print(" een_rescaled_e_gl[1, 1, 3, 1] = ",een_rescaled_e_gl[0, 0, 2, 1])
|
||||
#print(" een_rescaled_e_gl[1, 1, 4, 1] = ",een_rescaled_e_gl[0, 0, 3, 1])
|
||||
#print(" een_rescaled_e_gl[1, 1, 5, 1] = ",een_rescaled_e_gl[0, 0, 4, 1])
|
||||
#print(" een_rescaled_e_gl[2, 1, 4, 2] = ",een_rescaled_e_gl[1, 0, 3, 2])
|
||||
#print(" een_rescaled_e_gl[2, 1, 5, 2] = ",een_rescaled_e_gl[1, 0, 4, 2])
|
||||
#print(" een_rescaled_e_gl[2, 1, 6, 2] = ",een_rescaled_e_gl[1, 0, 5, 2])
|
||||
print(" een_rescaled_e_gl[1, 1, 3, 1] = ",een_rescaled_e_gl[0, 0, 2, 1])
|
||||
print(" een_rescaled_e_gl[1, 1, 4, 1] = ",een_rescaled_e_gl[0, 0, 3, 1])
|
||||
print(" een_rescaled_e_gl[1, 1, 5, 1] = ",een_rescaled_e_gl[0, 0, 4, 1])
|
||||
print(" een_rescaled_e_gl[2, 1, 4, 2] = ",een_rescaled_e_gl[1, 0, 3, 2])
|
||||
print(" een_rescaled_e_gl[2, 1, 5, 2] = ",een_rescaled_e_gl[1, 0, 4, 2])
|
||||
print(" een_rescaled_e_gl[2, 1, 6, 2] = ",een_rescaled_e_gl[1, 0, 5, 2])
|
||||
#+end_src
|
||||
|
||||
#+RESULTS: een_e_gl
|
||||
: een_rescaled_e_gl[1, 1, 3, 1] = 0.05991352796887283
|
||||
: een_rescaled_e_gl[1, 1, 4, 1] = 0.011714035071545248
|
||||
: een_rescaled_e_gl[1, 1, 5, 1] = 0.00441398875758468
|
||||
: een_rescaled_e_gl[2, 1, 4, 2] = 0.013553180060167595
|
||||
: een_rescaled_e_gl[2, 1, 5, 2] = 0.00041342909359870457
|
||||
: een_rescaled_e_gl[2, 1, 6, 2] = 0.5880599146214673
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
double een_rescaled_e_gl[walk_num][(cord_num + 1)][elec_num][4][elec_num];
|
||||
size_max=walk_num*(cord_num + 1)*elec_num*4*elec_num;
|
||||
@ -6291,12 +6253,12 @@ rc = qmckl_get_jastrow_champ_een_rescaled_e_gl(context,
|
||||
&(een_rescaled_e_gl[0][0][0][0][0]),size_max);
|
||||
|
||||
// value of (0,0,0,2,1)
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][2] + 0.05991352796887283 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][3] + 0.011714035071545248 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][4] + 0.00441398875758468 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][3] + 0.013553180060167595 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][4] + 0.00041342909359870457) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][5] + 0.5880599146214673 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][2] + 0.09831391870751387 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][3] + 0.017204157459682526 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][1][0][0][4] + 0.013345768421098641 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][3] + 0.03733086358273962 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][4] + 0.004922634822943517 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_e_gl[0][2][1][0][5] + 0.5416751547830984 ) < 1.e-12);
|
||||
#+end_src
|
||||
|
||||
*** Electron-nucleus rescaled distances in $J_\text{eeN}$
|
||||
@ -6662,7 +6624,7 @@ for i in range(elec_num):
|
||||
for a in range(nucl_num):
|
||||
elnuc_dist[i, a] = np.linalg.norm(elec_coord[i] - nucl_coord[:,a])
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_n = np.zeros(shape=(nucl_num, elec_num, cord_num + 1), dtype=float)
|
||||
een_rescaled_n[:,:,0] = 1.0
|
||||
@ -6685,12 +6647,12 @@ print(" een_rescaled_n[1, 5, 2] = ",een_rescaled_n[1, 5, 2])
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: een_rescaled_n[0, 2, 1] = 0.10612983920006765
|
||||
: een_rescaled_n[0, 3, 1] = 0.135652809635553
|
||||
: een_rescaled_n[0, 4, 1] = 0.023391817607642338
|
||||
: een_rescaled_n[1, 3, 2] = 0.880957224822116
|
||||
: een_rescaled_n[1, 4, 2] = 0.027185942659395074
|
||||
: een_rescaled_n[1, 5, 2] = 0.01343938025140174
|
||||
: een_rescaled_n[0, 2, 1] =
|
||||
: een_rescaled_n[0, 3, 1] =
|
||||
: een_rescaled_n[0, 4, 1] =
|
||||
: een_rescaled_n[1, 3, 2] =
|
||||
: een_rescaled_n[1, 4, 2] =
|
||||
: een_rescaled_n[1, 5, 2] =
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
assert(qmckl_electron_provided(context));
|
||||
@ -6700,12 +6662,12 @@ size_max=walk_num*(cord_num + 1)*nucl_num*elec_num;
|
||||
rc = qmckl_get_jastrow_champ_een_rescaled_n(context, &(een_rescaled_n[0][0][0][0]),size_max);
|
||||
|
||||
// value of (0,2,1)
|
||||
assert(fabs(een_rescaled_n[0][1][0][2]-0.10612983920006765) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][1][0][3]-0.135652809635553) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][1][0][4]-0.023391817607642338) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][3]-0.880957224822116) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][4]-0.027185942659395074) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][5]-0.01343938025140174) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][1][0][2]-0.2603169838750542 )< 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][1][0][3]-0.3016180139679065 )< 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][1][0][4]-0.10506023826192266)< 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][3]-0.9267719759374164 )< 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][4]-0.11497585238132658)< 1.e-12);
|
||||
assert(fabs(een_rescaled_n[0][2][1][5]-0.07534033469115217)< 1.e-12);
|
||||
#+end_src
|
||||
|
||||
*** Electron-nucleus rescaled distances derivatives in $J_\text{eeN}$
|
||||
@ -7065,7 +7027,7 @@ for a in range(nucl_num):
|
||||
elnuc_dist_gl[ii, i, a] = (elec_coord[i][ii] - nucl_coord[ii][a]) * rij_inv
|
||||
elnuc_dist_gl[3, i, a] = 2.0 * rij_inv
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_n = np.zeros(shape=(nucl_num, elec_num, cord_num + 1), dtype=float)
|
||||
een_rescaled_n[:,:,0] = 1.0
|
||||
@ -7103,12 +7065,12 @@ print(" een_rescaled_n_gl[2, 1, 6, 2] = ",een_rescaled_n_gl[5, 0, 1, 2])
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: een_rescaled_n_gl[1, 1, 3, 1] = -0.07633444246999128
|
||||
: een_rescaled_n_gl[1, 1, 4, 1] = 0.00033282346259738276
|
||||
: een_rescaled_n_gl[1, 1, 5, 1] = -0.004775370547333061
|
||||
: een_rescaled_n_gl[2, 1, 4, 2] = 0.1362654644223866
|
||||
: een_rescaled_n_gl[2, 1, 5, 2] = -0.0231253431662794
|
||||
: een_rescaled_n_gl[2, 1, 6, 2] = 0.001593334817691633
|
||||
: een_rescaled_n_gl[1, 1, 3, 1] =
|
||||
: een_rescaled_n_gl[1, 1, 4, 1] =
|
||||
: een_rescaled_n_gl[1, 1, 5, 1] =
|
||||
: een_rescaled_n_gl[2, 1, 4, 2] =
|
||||
: een_rescaled_n_gl[2, 1, 5, 2] =
|
||||
: een_rescaled_n_gl[2, 1, 6, 2] =
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
assert(qmckl_electron_provided(context));
|
||||
@ -7118,12 +7080,12 @@ size_max=walk_num*(cord_num + 1)*nucl_num*4*elec_num;
|
||||
rc = qmckl_get_jastrow_champ_een_rescaled_n_gl(context, &(een_rescaled_n_gl[0][0][0][0][0]),size_max);
|
||||
|
||||
// value of (0,2,1)
|
||||
assert(fabs(een_rescaled_n_gl[0][1][0][0][2]+0.07633444246999128 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n_gl[0][1][0][0][3]-0.00033282346259738276) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n_gl[0][1][0][0][4]+0.004775370547333061 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n_gl[0][2][1][0][3]-0.1362654644223866 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n_gl[0][2][1][0][4]+0.0231253431662794 ) < 1.e-12);
|
||||
assert(fabs(een_rescaled_n_gl[0][2][1][0][5]-0.001593334817691633 ) < 1.e-12);
|
||||
assert(fabs( -0.11234061209936878 - een_rescaled_n_gl[0][1][0][0][2]) < 1.e-12);
|
||||
assert(fabs( 0.0004440109367151707 - een_rescaled_n_gl[0][1][0][0][3]) < 1.e-12);
|
||||
assert(fabs( -0.012868642597346566 - een_rescaled_n_gl[0][1][0][0][4]) < 1.e-12);
|
||||
assert(fabs( 0.08601122289922644 - een_rescaled_n_gl[0][2][1][0][3]) < 1.e-12);
|
||||
assert(fabs( -0.058681563677207206 - een_rescaled_n_gl[0][2][1][0][4]) < 1.e-12);
|
||||
assert(fabs( 0.005359281880312882 - een_rescaled_n_gl[0][2][1][0][5]) < 1.e-12);
|
||||
|
||||
#+end_src
|
||||
|
||||
@ -8390,7 +8352,7 @@ for i in range(elec_num):
|
||||
for a in range(nucl_num):
|
||||
elnuc_dist[i, a] = np.linalg.norm(elec_coord[i] - nucl_coord[:,a])
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_n = np.zeros(shape=(nucl_num, elec_num, cord_num + 1), dtype=float)
|
||||
een_rescaled_n[:,:,0] = 1.0
|
||||
@ -8409,7 +8371,7 @@ for i in range(elec_num):
|
||||
for j in range(elec_num):
|
||||
elec_dist[i, j] = np.linalg.norm(elec_coord[i] - elec_coord[j])
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
een_rescaled_e_ij = np.zeros(shape=(elec_num * (elec_num - 1)//2, cord_num+1), dtype=float)
|
||||
een_rescaled_e_ij[:,0] = 1.0
|
||||
@ -8454,11 +8416,11 @@ rc = qmckl_get_jastrow_champ_tmp_c(context, &(tmp_c[0][0][0][0][0]));
|
||||
double dtmp_c[walk_num][cord_num][cord_num+1][nucl_num][4][elec_num];
|
||||
rc = qmckl_get_jastrow_champ_dtmp_c(context, &(dtmp_c[0][0][0][0][0][0]));
|
||||
|
||||
printf("%e\n%e\n", tmp_c[0][0][1][0][0], 2.7083473948352403);
|
||||
assert(fabs(tmp_c[0][0][1][0][0] - 2.7083473948352403) < 1e-12);
|
||||
printf("%e\n%e\n", tmp_c[0][0][1][0][0], 3.954384);
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assert(fabs(tmp_c[0][0][1][0][0] - 3.954384) < 1e-6);
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||||
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||||
printf("%e\n%e\n", dtmp_c[0][1][0][0][0][0],0.237440520852232);
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assert(fabs(dtmp_c[0][1][0][0][0][0] - 0.237440520852232) < 1e-12);
|
||||
printf("%e\n%e\n", dtmp_c[0][1][0][0][0][0],3.278657e-01);
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assert(fabs(dtmp_c[0][1][0][0][0][0] - 3.278657e-01 ) < 1e-6);
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||||
#+end_src
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||||
|
||||
*** Electron-electron-nucleus Jastrow $f_{een}$
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||||
@ -8993,7 +8955,7 @@ import numpy as np
|
||||
|
||||
<<helper_funcs>>
|
||||
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||||
kappa = 1.0
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||||
kappa = 0.6
|
||||
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||||
factor_een = 0.0
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||||
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||||
@ -9018,7 +8980,7 @@ print("factor_een:",factor_een)
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||||
#+end_src
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||||
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||||
#+RESULTS:
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||||
: factor_een: -0.37407972141304213
|
||||
: factor_een: -0.382580260174321
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
@ -9028,7 +8990,7 @@ assert(qmckl_jastrow_champ_provided(context));
|
||||
double factor_een[walk_num];
|
||||
rc = qmckl_get_jastrow_champ_factor_een(context, &(factor_een[0]),walk_num);
|
||||
|
||||
assert(fabs(factor_een[0] + 0.37407972141304213) < 1e-12);
|
||||
assert(fabs(factor_een[0] + 0.382580260174321) < 1e-12);
|
||||
#+end_src
|
||||
|
||||
*** Electron-electron-nucleus Jastrow $f_{een}$ derivative
|
||||
@ -9798,7 +9760,7 @@ import numpy as np
|
||||
|
||||
<<helper_funcs>>
|
||||
|
||||
kappa = 1.0
|
||||
kappa = 0.6
|
||||
|
||||
factor_een = 0.0
|
||||
|
||||
@ -9816,27 +9778,28 @@ for n in range(0, dim_c_vector):
|
||||
for j in range(0, elec_num):
|
||||
accu = 0.0
|
||||
accu2 = 0.0
|
||||
daccu = 0.0
|
||||
daccu2 = 0.0
|
||||
for i in range(0, elec_num):
|
||||
accu = accu + een_rescaled_e[i,j,k] * \
|
||||
een_rescaled_n[a,i,m]
|
||||
accu2 = accu2 + een_rescaled_e[i,j,k] * \
|
||||
een_rescaled_n[a,i,m+l]
|
||||
# daccu[0:4] = daccu[0:4] + een_rescaled_e_gl_t[k,j,0:4,i,k] * \
|
||||
# een_rescaled_n[a,i,m]
|
||||
# daccu[0:4] = daccu[0:4] + een_rescaled_e_gl_t[k,j,0:4,i,k] * \
|
||||
# een_rescaled_n[a,i,m]
|
||||
accu2 = accu2 + accu * een_rescaled_n[a,j,m+l]
|
||||
# factor_een = factor_een + accu2 * cn
|
||||
accu2 = accu2 + accu * een_rescaled_n[a,j,m+l]
|
||||
factor_een = factor_een + accu2 * cn
|
||||
|
||||
print("factor_een:",factor_een)
|
||||
|
||||
#+end_src
|
||||
|
||||
#+RESULTS:
|
||||
: een_rescaled_e_gl[1, 1, 3, 1] = 0.09831391870751387
|
||||
: een_rescaled_e_gl[1, 1, 4, 1] = 0.017204157459682526
|
||||
: een_rescaled_e_gl[1, 1, 5, 1] = 0.013345768421098641
|
||||
: een_rescaled_e_gl[2, 1, 4, 2] = 0.03733086358273962
|
||||
: een_rescaled_e_gl[2, 1, 5, 2] = 0.004922634822943517
|
||||
: een_rescaled_e_gl[2, 1, 6, 2] = 0.5416751547830984
|
||||
: (6, 10, 4, 10)
|
||||
: factor_een: 0.0
|
||||
: factor_een: -14.956095654486404
|
||||
|
||||
|
||||
#+begin_src c :tangle (eval c_test)
|
||||
@ -9847,16 +9810,16 @@ double factor_een_gl[4][walk_num][elec_num];
|
||||
rc = qmckl_get_jastrow_champ_factor_een_gl(context, &(factor_een_gl[0][0][0]),4*walk_num*elec_num);
|
||||
|
||||
printf("%20.15e\n", factor_een_gl[0][0][0]);
|
||||
assert(fabs(factor_een_gl[0][0][0] - (-5.481671107220383e-04)) < 1e-12);
|
||||
assert(fabs(8.967809309100624e-02 - factor_een_gl[0][0][0]) < 1e-12);
|
||||
|
||||
printf("%20.15e\n", factor_een_gl[1][0][1]);
|
||||
assert(fabs(factor_een_gl[1][0][1] - (-5.402107832095666e-02)) < 1e-12);
|
||||
assert(fabs(-3.401545636077585e-02 - factor_een_gl[1][0][1]) < 1e-12);
|
||||
|
||||
printf("%20.15e\n", factor_een_gl[2][0][2]);
|
||||
assert(fabs(factor_een_gl[2][0][2] - (-1.648945927082279e-01)) < 1e-12);
|
||||
assert(fabs(-2.631321052321952e-01 - factor_een_gl[2][0][2]) < 1e-12);
|
||||
|
||||
printf("%20.15e\n", factor_een_gl[3][0][3]);
|
||||
assert(fabs(factor_een_gl[3][0][3] - (-1.269746119491287e+00)) < 1e-12);
|
||||
assert(fabs(-1.016785559040419e+00 - factor_een_gl[3][0][3]) < 1e-12);
|
||||
#+end_src
|
||||
|
||||
** Total Jastrow
|
||||
|
||||
Loading…
Reference in New Issue
Block a user