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Rewrote Jastrow ee
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@ -2550,12 +2550,17 @@ assert(fabs(factor_ee[0]+4.282760865958113) < 1.e-12);
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There are four components, the gradient which has 3 components in the \(x, y, z\)
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There are four components, the gradient which has 3 components in the \(x, y, z\)
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directions and the laplacian as the last component.
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directions and the laplacian as the last component.
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\[ \nabla_i f_\text{ee} = \frac{1}{2}\sum_{j}
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\[ \nabla_i f_\text{ee} = \sum_{j\ne i}
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\left[\frac{\delta_{ij}^{\uparrow\downarrow} B_0\, \nabla_i
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\left[\frac{\delta_{ij}^{\uparrow\downarrow} B_0\, \nabla_i
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C_{ij}}{(1 - B_1\, C_{ij})^2} + \sum^{n_\text{ord}}_{k=2}
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C_{ij}}{(1 + B_1\, C_{ij})^2} + \sum^{n_\text{ord}}_{k=2}
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B_k\, k\, C_{ij}^{k-1} \nabla C_{ij} \right] \]
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B_k\, k\, C_{ij}^{k-1} \nabla C_{ij} \right] \]
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# TODO Formula for Laplacian
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\[ \Delta_i f_\text{ee} = \sum_{j \ne i}
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\left[ \delta_{ij}^{\uparrow\downarrow} B_0
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\left(\frac{ \Delta_i C_{ij}}{(1 + B_1\, C_{ij})^2} -\frac{2\,B_1
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\left(\nabla_i C_{ij}\right)^2 }{(1 + B_1\, C_{ij})^3} \right) + \sum^{n_\text{ord}}_{k=2}
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B_k\, k\, \left((k-1)\, C_{ij}^{k-2} \left(\nabla_i C_{ij}\right)^2 + C_{ij}^{k-1} \Delta_i C_{ij} \right) \right] \]
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**** Get
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**** Get
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#+begin_src c :comments org :tangle (eval h_func) :noweb yes
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#+begin_src c :comments org :tangle (eval h_func) :noweb yes
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qmckl_exit_code
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qmckl_exit_code
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@ -2734,12 +2739,11 @@ integer function qmckl_compute_jastrow_champ_factor_ee_deriv_e_doc_f( &
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double precision , intent(in) :: ee_distance_rescaled_deriv_e(4,elec_num, elec_num,walk_num) !TODO
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double precision , intent(in) :: ee_distance_rescaled_deriv_e(4,elec_num, elec_num,walk_num) !TODO
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double precision , intent(out) :: factor_ee_deriv_e(elec_num,4,walk_num)
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double precision , intent(out) :: factor_ee_deriv_e(elec_num,4,walk_num)
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integer*8 :: i, j, p, nw, ii
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integer*8 :: i, j, k, nw, ii
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double precision :: x, spin_fact, y
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double precision :: x, x1, kf
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double precision :: den, invden, invden2, invden3, xinv
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double precision :: denom, invdenom, invdenom2, f
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double precision :: lap1, lap2, lap3, third
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double precision :: grad_c2
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double precision, dimension(3) :: pow_ser_g
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double precision :: dx(4)
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double precision, dimension(4) :: dx
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info = QMCKL_SUCCESS
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info = QMCKL_SUCCESS
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@ -2763,60 +2767,56 @@ integer function qmckl_compute_jastrow_champ_factor_ee_deriv_e_doc_f( &
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return
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return
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endif
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endif
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factor_ee_deriv_e = 0.0d0
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third = 1.0d0 / 3.0d0
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do nw =1, walk_num
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do nw =1, walk_num
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do j = 1, elec_num
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factor_ee_deriv_e(:,:,nw) = 0.0d0
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do i = 1, elec_num
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do i = 1, elec_num
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x = ee_distance_rescaled(i,j,nw)
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do j = 1, elec_num
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if(abs(x) < 1.0d-18) cycle
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if (j==i) cycle
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pow_ser_g = 0.0d0
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spin_fact = 1.0d0
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den = 1.0d0 + b_vector(2) * x
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invden = 1.0d0 / den
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invden2 = invden * invden
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invden3 = invden2 * invden
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xinv = 1.0d0 / (x + 1.0d-18)
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dx(1) = ee_distance_rescaled_deriv_e(1, i, j, nw)
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x = ee_distance_rescaled(j,i,nw)
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dx(2) = ee_distance_rescaled_deriv_e(2, i, j, nw)
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dx(3) = ee_distance_rescaled_deriv_e(3, i, j, nw)
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dx(4) = ee_distance_rescaled_deriv_e(4, i, j, nw)
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if((i <= up_num .and. j <= up_num ) .OR. &
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denom = 1.0d0 + b_vector(2) * x
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(i > up_num .and. j > up_num)) then
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invdenom = 1.0d0 / denom
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spin_fact = 0.5d0
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invdenom2 = invdenom * invdenom
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endif
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lap1 = 0.0d0
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dx(1) = ee_distance_rescaled_deriv_e(1, j, i, nw)
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lap2 = 0.0d0
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dx(2) = ee_distance_rescaled_deriv_e(2, j, i, nw)
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lap3 = 0.0d0
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dx(3) = ee_distance_rescaled_deriv_e(3, j, i, nw)
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do ii = 1, 3
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dx(4) = ee_distance_rescaled_deriv_e(4, j, i, nw)
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x = ee_distance_rescaled(i, j, nw)
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if(abs(x) < 1.0d-18) cycle
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do p = 2, bord_num
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y = p * b_vector(p + 1) * x
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pow_ser_g(ii) = pow_ser_g(ii) + y * dx(ii)
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lap1 = lap1 + (p - 1) * y * xinv * dx(ii) * dx(ii)
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lap2 = lap2 + y
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x = x * ee_distance_rescaled(i, j, nw)
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end do
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lap3 = lap3 - 2.0d0 * b_vector(2) * dx(ii) * dx(ii)
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grad_c2 = dx(1)*dx(1) + dx(2)*dx(2) + dx(3)*dx(3)
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if((i <= up_num .and. j <= up_num ) .or. (i > up_num .and. j > up_num)) then
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f = 0.5d0 * b_vector(1) * invdenom2
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else
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f = b_vector(1) * invdenom2
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end if
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factor_ee_deriv_e(i,1,nw) = factor_ee_deriv_e(i,1,nw) + f * dx(1)
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factor_ee_deriv_e(i,2,nw) = factor_ee_deriv_e(i,2,nw) + f * dx(2)
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factor_ee_deriv_e(i,3,nw) = factor_ee_deriv_e(i,3,nw) + f * dx(3)
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factor_ee_deriv_e(i,4,nw) = factor_ee_deriv_e(i,4,nw) &
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+ f * (dx(4) - 2.d0 * b_vector(2) * grad_c2 * invdenom)
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kf = 2.d0
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x1 = x
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x = 1.d0
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do k=2, bord_num
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f = b_vector(k+1) * kf * x
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factor_ee_deriv_e(i,1,nw) = factor_ee_deriv_e(i,1,nw) + f * x1 * dx(1)
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factor_ee_deriv_e(i,2,nw) = factor_ee_deriv_e(i,2,nw) + f * x1 * dx(2)
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factor_ee_deriv_e(i,3,nw) = factor_ee_deriv_e(i,3,nw) + f * x1 * dx(3)
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factor_ee_deriv_e(i,4,nw) = factor_ee_deriv_e(i,4,nw) &
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+ f * (x1 * dx(4) + (kf-1.d0) * grad_c2)
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x = x*x1
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kf = kf + 1.d0
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end do
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factor_ee_deriv_e( j, ii, nw) = factor_ee_deriv_e( j, ii, nw) &
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+ spin_fact * b_vector(1) * dx(ii) * invden2 + pow_ser_g(ii)
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end do
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end do
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ii = 4
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lap2 = lap2 * dx(ii) * third
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lap3 = lap3 + den * dx(ii)
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lap3 = lap3 * (spin_fact * b_vector(1) * invden3)
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factor_ee_deriv_e( j, ii, nw) = factor_ee_deriv_e( j, ii, nw) + lap1 + lap2 + lap3
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end do
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end do
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end do
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end do
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end do
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end function qmckl_compute_jastrow_champ_factor_ee_deriv_e_doc_f
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end function qmckl_compute_jastrow_champ_factor_ee_deriv_e_doc_f
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@ -3036,7 +3036,6 @@ integer(c_int32_t) function qmckl_compute_jastrow_champ_factor_ee_deriv_e_doc &
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}
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}
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#+end_src
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#+end_src
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**** Test
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**** Test
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#+begin_src python :results output :exports none :noweb yes
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#+begin_src python :results output :exports none :noweb yes
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import numpy as np
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import numpy as np
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@ -3046,84 +3045,103 @@ import numpy as np
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<<asymp_jasb>>
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<<asymp_jasb>>
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kappa = 1.0
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kappa = 1.0
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dx = 1.e-3
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elec_coord = np.array(elec_coord)[0]
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elec_coord = np.array(elec_coord)[0]
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elec_dist = np.zeros(shape=(elec_num, elec_num),dtype=float)
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for i in range(elec_num):
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for j in range(elec_num):
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elec_dist[i, j] = np.linalg.norm(elec_coord[i] - elec_coord[j])
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elec_dist_deriv_e = np.zeros(shape=(4,elec_num, elec_num),dtype=float)
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def make_dist(elec_coord):
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for j in range(elec_num):
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for i in range(elec_num):
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rij_inv = 1.0 / elec_dist[i, j]
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for ii in range(3):
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elec_dist_deriv_e[ii, i, j] = (elec_coord[j][ii] - elec_coord[i][ii]) * rij_inv
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elec_dist_deriv_e[3, i, j] = 2.0 * rij_inv
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elec_dist_deriv_e[:, j, j] = 0.0
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ee_distance_rescaled_deriv_e = np.zeros(shape=(4,elec_num,elec_num),dtype=float)
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elec_dist = np.zeros(shape=(elec_num, elec_num),dtype=float)
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for j in range(elec_num):
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for i in range(elec_num):
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for i in range(elec_num):
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for j in range(elec_num):
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f = 1.0 - kappa * ee_distance_rescaled[i][j]
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elec_dist[i, j] = np.linalg.norm(elec_coord[i] - elec_coord[j])
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for ii in range(4):
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return elec_dist
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ee_distance_rescaled_deriv_e[ii][i][j] = elec_dist_deriv_e[ii][i][j]
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ee_distance_rescaled_deriv_e[3][i][j] = ee_distance_rescaled_deriv_e[3][i][j] + \
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(-kappa * ee_distance_rescaled_deriv_e[0][i][j] * ee_distance_rescaled_deriv_e[0][i][j]) + \
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def make_dist_deriv(elec_coord):
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(-kappa * ee_distance_rescaled_deriv_e[1][i][j] * ee_distance_rescaled_deriv_e[1][i][j]) + \
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(-kappa * ee_distance_rescaled_deriv_e[2][i][j] * ee_distance_rescaled_deriv_e[2][i][j])
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elec_dist_d = np.zeros(shape=(4, elec_num, elec_num),dtype=float)
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for ii in range(4):
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for i in range(elec_num):
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ee_distance_rescaled_deriv_e[ii][i][j] = ee_distance_rescaled_deriv_e[ii][i][j] * f
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for j in range(elec_num):
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rij = np.linalg.norm(elec_coord[i] - elec_coord[j])
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rijm = np.linalg.norm(elec_coord[i] - elec_coord[j]+np.array((dx,0.,0.)))
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rijp = np.linalg.norm(elec_coord[i] - elec_coord[j]-np.array((dx,0.,0.)))
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elec_dist_d[0, i, j] = (rijp-rijm)/(2.*dx)
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elec_dist_d[3, i, j] = (rijp+rijm-2.*rij)/(dx**2)
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rijm = np.linalg.norm(elec_coord[i] - elec_coord[j]+np.array((0.,dx,0.)))
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rijp = np.linalg.norm(elec_coord[i] - elec_coord[j]-np.array((0.,dx,0.)))
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elec_dist_d[1, i, j] = (rijp-rijm)/(2.*dx)
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elec_dist_d[3, i, j] += (rijp+rijm-2.*rij)/(dx**2)
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rijm = np.linalg.norm(elec_coord[i] - elec_coord[j]+np.array((0.,0.,dx)))
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rijp = np.linalg.norm(elec_coord[i] - elec_coord[j]-np.array((0.,0.,dx)))
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elec_dist_d[2, i, j] = (rijp-rijm)/(2.*dx)
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elec_dist_d[3, i, j] += (rijp+rijm-2.*rij)/(dx**2)
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return elec_dist_d
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def func(elec_coord):
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elec_dist = make_dist(elec_coord)
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elec_dist_deriv_e = np.zeros(shape=(4,elec_num, elec_num),dtype=float)
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for j in range(elec_num):
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for i in range(elec_num):
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rij_inv = 1.0 / elec_dist[i, j]
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for ii in range(3):
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elec_dist_deriv_e[ii, i, j] = (elec_coord[j][ii] - elec_coord[i][ii]) * rij_inv
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elec_dist_deriv_e[3, i, j] = 2.0 * rij_inv
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elec_dist_deriv_e[:, j, j] = 6.0
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ee_distance_rescaled_deriv_e = np.zeros(shape=(4,elec_num,elec_num),dtype=float)
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for j in range(elec_num):
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for i in range(elec_num):
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f = 1.0 - kappa * ee_distance_rescaled[i][j]
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for ii in range(4):
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ee_distance_rescaled_deriv_e[ii][i][j] = elec_dist_deriv_e[ii][i][j]
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ee_distance_rescaled_deriv_e[3][i][j] = ee_distance_rescaled_deriv_e[3][i][j] + \
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(-kappa * ee_distance_rescaled_deriv_e[0][i][j] * ee_distance_rescaled_deriv_e[0][i][j]) + \
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(-kappa * ee_distance_rescaled_deriv_e[1][i][j] * ee_distance_rescaled_deriv_e[1][i][j]) + \
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(-kappa * ee_distance_rescaled_deriv_e[2][i][j] * ee_distance_rescaled_deriv_e[2][i][j])
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for ii in range(4):
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ee_distance_rescaled_deriv_e[ii][i][j] = ee_distance_rescaled_deriv_e[ii][i][j] * f
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return ee_distance_rescaled_deriv_e, elec_dist_deriv_e
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ee_distance_rescaled_deriv_e, elec_dist_deriv_e = func(elec_coord)
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#print(elec_dist_deriv_e[3,:,:])
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#print(make_dist_deriv(elec_coord)[3,:,:])
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third = 1.0 / 3.0
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factor_ee_deriv_e = np.zeros(shape=(4,elec_num),dtype=float)
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factor_ee_deriv_e = np.zeros(shape=(4,elec_num),dtype=float)
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dx = np.zeros(shape=(4),dtype=float)
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dx = np.zeros(shape=(4),dtype=float)
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pow_ser_g = np.zeros(shape=(4),dtype=float)
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pow_ser_g = np.zeros(shape=(4),dtype=float)
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for j in range(elec_num):
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for j in range(elec_num):
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for i in range(elec_num):
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for i in range(elec_num):
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x = ee_distance_rescaled[j][i]
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if i == j: continue
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if abs(x) < 1e-18:
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x = ee_distance_rescaled[j,i]
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continue
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den = 1.0 + b_vector[1] * x
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pow_ser_g = np.zeros(shape=(4),dtype=float)
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spin_fact = 1.0
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den = 1.0 + b_vector[1] * ee_distance_rescaled[j][i]
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invden = 1.0 / den
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invden = 1.0 / den
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invden2 = invden * invden
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invden2 = invden * invden
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invden3 = invden2 * invden
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invden3 = invden2 * invden
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xinv = 1.0 / (ee_distance_rescaled[j][i] + 1.0E-18)
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xinv = 1.0 / x
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ipar = 1
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ipar = 1
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for ii in range(4):
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dx[:] = ee_distance_rescaled_deriv_e[:,j,i]
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dx[ii] = ee_distance_rescaled_deriv_e[ii][j][i]
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if((i <= (up_num-1) and j <= (up_num-1) ) or \
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if((i < up_num and j < up_num) or (i >= up_num and j >= up_num) ):
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(i > (up_num-1) and j > (up_num-1))):
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spin_fact = 0.5
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spin_fact = 0.5
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else:
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spin_fact = 1.0
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lap1 = 0.0
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factor_ee_deriv_e[:,j] += spin_fact * b_vector[0] * dx[:] * invden2
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lap2 = 0.0
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for k in range(2,bord_num+1):
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lap3 = 0.0
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factor_ee_deriv_e[:,j] += b_vector[k]*k*x**(k-1)*dx[:]
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for ii in range(3):
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x = ee_distance_rescaled[j][i]
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if x < 1e-18:
|
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continue
|
|
||||||
for p in range(2,bord_num+1):
|
|
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y = p * b_vector[(p-1) + 1] * x
|
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||||||
pow_ser_g[ii] = pow_ser_g[ii] + y * dx[ii]
|
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||||||
lap1 = lap1 + (p - 1) * y * xinv * dx[ii] * dx[ii]
|
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||||||
lap2 = lap2 + y
|
|
||||||
x = x * ee_distance_rescaled[j][i]
|
|
||||||
|
|
||||||
lap3 = lap3 - 2.0 * b_vector[1] * dx[ii] * dx[ii]
|
grad_c2 = np.dot(ee_distance_rescaled_deriv_e[:3,j,i], ee_distance_rescaled_deriv_e[:3,j,i])
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||||||
|
factor_ee_deriv_e[3,j] -= spin_fact * b_vector[0] * 2. * b_vector[1] * grad_c2 * invden3
|
||||||
|
for k in range(2,bord_num+1):
|
||||||
|
factor_ee_deriv_e[3,j] += b_vector[k]*k*(k-1)*x**(k-2)*grad_c2
|
||||||
|
|
||||||
factor_ee_deriv_e[ii][j] = factor_ee_deriv_e[ii][j] + spin_fact * b_vector[0] * \
|
|
||||||
dx[ii] * invden2 + pow_ser_g[ii]
|
|
||||||
|
|
||||||
ii = 3
|
|
||||||
lap2 = lap2 * dx[ii] * third
|
|
||||||
lap3 = lap3 + den * dx[ii]
|
|
||||||
lap3 = lap3 * (spin_fact * b_vector[0] * invden3)
|
|
||||||
factor_ee_deriv_e[ii][j] = factor_ee_deriv_e[ii][j] + lap1 + lap2 + lap3
|
|
||||||
|
|
||||||
print("factor_ee_deriv_e[0][0]:",factor_ee_deriv_e[0][0])
|
print("factor_ee_deriv_e[0][0]:",factor_ee_deriv_e[0][0])
|
||||||
print("factor_ee_deriv_e[1][0]:",factor_ee_deriv_e[1][0])
|
print("factor_ee_deriv_e[1][0]:",factor_ee_deriv_e[1][0])
|
||||||
@ -3133,8 +3151,8 @@ print("factor_ee_deriv_e[3][0]:",factor_ee_deriv_e[3][0])
|
|||||||
|
|
||||||
#+RESULTS:
|
#+RESULTS:
|
||||||
: asym_one : 0.43340325572525706
|
: asym_one : 0.43340325572525706
|
||||||
: asymp_jasb[0] : 0.5323750557252571
|
: asymp_jasb[0] : 0.31567342786262853
|
||||||
: asymp_jasb[1] : 0.31567342786262853
|
: asymp_jasb[1] : 0.5323750557252571
|
||||||
: factor_ee_deriv_e[0][0]: 0.16364894652107934
|
: factor_ee_deriv_e[0][0]: 0.16364894652107934
|
||||||
: factor_ee_deriv_e[1][0]: -0.6927548119830084
|
: factor_ee_deriv_e[1][0]: -0.6927548119830084
|
||||||
: factor_ee_deriv_e[2][0]: 0.073267755223968
|
: factor_ee_deriv_e[2][0]: 0.073267755223968
|
||||||
@ -3150,9 +3168,16 @@ double factor_ee_deriv_e[walk_num][4][elec_num];
|
|||||||
rc = qmckl_get_jastrow_champ_factor_ee_deriv_e(context, &(factor_ee_deriv_e[0][0][0]),walk_num*4*elec_num);
|
rc = qmckl_get_jastrow_champ_factor_ee_deriv_e(context, &(factor_ee_deriv_e[0][0][0]),walk_num*4*elec_num);
|
||||||
|
|
||||||
// check factor_ee_deriv_e
|
// check factor_ee_deriv_e
|
||||||
|
printf("%f %f\n", factor_ee_deriv_e[0][0][0], 0.16364894652107934);
|
||||||
assert(fabs(factor_ee_deriv_e[0][0][0]-0.16364894652107934) < 1.e-12);
|
assert(fabs(factor_ee_deriv_e[0][0][0]-0.16364894652107934) < 1.e-12);
|
||||||
|
|
||||||
|
printf("%f %f\n", factor_ee_deriv_e[0][1][0],-0.6927548119830084 );
|
||||||
assert(fabs(factor_ee_deriv_e[0][1][0]+0.6927548119830084 ) < 1.e-12);
|
assert(fabs(factor_ee_deriv_e[0][1][0]+0.6927548119830084 ) < 1.e-12);
|
||||||
|
|
||||||
|
printf("%f %f\n", factor_ee_deriv_e[0][2][0], 0.073267755223968 );
|
||||||
assert(fabs(factor_ee_deriv_e[0][2][0]-0.073267755223968 ) < 1.e-12);
|
assert(fabs(factor_ee_deriv_e[0][2][0]-0.073267755223968 ) < 1.e-12);
|
||||||
|
|
||||||
|
printf("%f %f\n", factor_ee_deriv_e[0][3][0], 1.5111672803213185 );
|
||||||
assert(fabs(factor_ee_deriv_e[0][3][0]-1.5111672803213185 ) < 1.e-12);
|
assert(fabs(factor_ee_deriv_e[0][3][0]-1.5111672803213185 ) < 1.e-12);
|
||||||
#+end_src
|
#+end_src
|
||||||
|
|
||||||
|
|||||||
Loading…
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Reference in New Issue
Block a user