1077 lines
245 KiB
Plaintext
1077 lines
245 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Jastrow factors"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## H2O"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-21T10:23:56.428148Z",
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"start_time": "2020-04-21T10:23:56.383715Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"onedet = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J2.ud_exp.dat\"\n",
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"os = 1.-7.92535494e-01\n",
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"\n",
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"multidet = \"Jastrows/H2O/H2O-cc-pcvTz-multidet.wfj_J2.ud_exp.dat\"\n",
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"ms = 1.-8.59001137e-01\n",
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"\n",
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"onedet_H = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.H_exp.dat\"\n",
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"ohs = 1.-1.03221118e+00\n",
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"onedet_O = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.O_exp.dat\"\n",
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"oos = 1.-1.43887500e+00\n",
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"\n",
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"#onedet = \"~/Anouar/Jastrows/N2H4/N2H4-tr6.wfj_J2.ud_exp.dat\"\n",
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"#os = 1.-8.03464242e-01\n",
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"\n",
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"!pwd\n",
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"/home/scemama/TEX/RSDFT-CIPSI-QMC/Data\n",
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"unset output\n"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"onedet = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J2.ud_exp.dat\"\n",
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"os = 1.-7.92535494e-01\n",
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"\n",
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"multidet = \"Jastrows/H2O/H2O-cc-pcvTz-multidet.wfj_J2.ud_exp.dat\"\n",
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"ms = 1.-8.59001137e-01\n",
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"\n",
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"onedet_H = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.H_exp.dat\"\n",
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"ohs = 1.-1.03221118e+00\n",
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"onedet_O = \"Jastrows/H2O/H2O-cc-pcvTz.wfj-1det_J1.O_exp.dat\"\n",
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"oos = 1.-1.43887500e+00\n",
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"\n",
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"#onedet = \"~/Anouar/Jastrows/N2H4/N2H4-tr6.wfj_J2.ud_exp.dat\"\n",
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"#os = 1.-8.03464242e-01\n",
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"\n",
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"!pwd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2020-04-21T10:24:00.383722Z",
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"start_time": "2020-04-21T10:24:00.144428Z"
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<IPython.core.display.Image object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"data": {
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"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set yrange [1:1.4]\n",
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464640.145517.692211090260.png'\n",
|
||
|
|
"plot onedet u 1:($2+os) w l title \"HF\" , multidet u 1:($2+ms) w l title \"FCI\"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"set yrange [0:1]\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464640.145606.7249194234.png'\n",
|
||
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\" , onedet_H u 1:($2+ohs) w l title \"H\"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 2,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set yrange [1:1.4]\n",
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"plot onedet u 1:($2+os) w l title \"HF\"\\\n",
|
||
|
|
", multidet u 1:($2+ms) w l title \"FCI\"\n",
|
||
|
|
"\n",
|
||
|
|
"set yrange [0:1]\n",
|
||
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\"\\\n",
|
||
|
|
", onedet_H u 1:($2+ohs) w l title \"H\"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:03.912512Z",
|
||
|
|
"start_time": "2020-04-21T10:24:03.529845Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"unset output\n",
|
||
|
|
" f1(x) = exp(a_1 * x/(1. + b_1*x))\n",
|
||
|
|
"f2(x) = exp(a_2 * x/(1. + b_2*x))\n",
|
||
|
|
"f3(x) = exp(a_3 * x**2+b_3)\n",
|
||
|
|
"f4(x) = exp(a_4 * x**2+b_4)\n",
|
||
|
|
"a_1 = 0.5\n",
|
||
|
|
"a_2 = 0.5\n",
|
||
|
|
"a_3 = 0.5\n",
|
||
|
|
"a_4 = 0.5\n",
|
||
|
|
"b_1 = 1.\n",
|
||
|
|
"b_2 = 1.\n",
|
||
|
|
"b_3 = 1.\n",
|
||
|
|
"b_4 = 1.\n",
|
||
|
|
"\n",
|
||
|
|
"fit f1(x) onedet u 1:($2+os) via b_1\n",
|
||
|
|
"Max. number of data points scaled up to: 3072\n",
|
||
|
|
"iter chisq delta/lim lambda b_1 \n",
|
||
|
|
" 0 5.7947436972e+01 0.00e+00 2.58e-01 1.000000e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 1 5.1445429129e+00 -1.03e+06 2.58e-02 1.538628e+00\n",
|
||
|
|
" 2 1.4305276261e-01 -3.50e+06 2.58e-03 1.862386e+00\n",
|
||
|
|
" 3 3.1749513729e-02 -3.51e+05 2.58e-04 1.928474e+00\n",
|
||
|
|
" 4 3.1669788584e-02 -2.52e+02 2.58e-05 1.930350e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-04 1.930346e+00\n",
|
||
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-03 1.930346e+00\n",
|
||
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-02 1.930346e+00\n",
|
||
|
|
" * 3.1669788590e-02 1.98e-05 2.58e-01 1.930346e+00\n",
|
||
|
|
" * 3.1669788589e-02 1.67e-05 2.58e+00 1.930346e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 5 3.1669788530e-02 -1.69e-04 2.58e-01 1.930347e+00\n",
|
||
|
|
"iter chisq delta/lim lambda b_1 \n",
|
||
|
|
"\n",
|
||
|
|
"After 5 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.0316698\n",
|
||
|
|
"rel. change during last iteration : -1.69165e-09\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 2999\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.00324963\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 1.05601e-05\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"b_1 = 1.93035 +/- 0.0006837 (0.03542%)\n",
|
||
|
|
"fit f2(x) multidet u 1:($2+ms) via b_2\n",
|
||
|
|
"Max. number of data points scaled up to: 3072\n",
|
||
|
|
"iter chisq delta/lim lambda b_2 \n",
|
||
|
|
" 0 1.6351844388e+02 0.00e+00 2.58e-01 1.000000e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 1 2.8331475972e+01 -4.77e+05 2.58e-02 1.903108e+00\n",
|
||
|
|
" 2 3.2508405969e+00 -7.72e+05 2.58e-03 2.993388e+00\n",
|
||
|
|
" 3 4.4711074925e-01 -6.27e+05 2.58e-04 3.771719e+00\n",
|
||
|
|
" 4 3.5779184535e-01 -2.50e+04 2.58e-05 3.979382e+00\n",
|
||
|
|
" 5 3.5770183297e-01 -2.52e+01 2.58e-06 3.986616e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 6 3.5770182428e-01 -2.43e-03 2.58e-07 3.986505e+00\n",
|
||
|
|
"iter chisq delta/lim lambda b_2 \n",
|
||
|
|
"\n",
|
||
|
|
"After 6 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.357702\n",
|
||
|
|
"rel. change during last iteration : -2.42953e-08\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 2999\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0109212\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000119274\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"b_2 = 3.9865 +/- 0.008324 (0.2088%)\n",
|
||
|
|
"fit [0:1] f3(x) onedet_O u 1:($2+oos) via a_3, b_3\n",
|
||
|
|
"iter chisq delta/lim lambda a_3 b_3 \n",
|
||
|
|
" 0 5.9977169495e+03 0.00e+00 2.41e+00 5.000000e-01 1.000000e+00\n",
|
||
|
|
" 1 5.3596427313e+02 -1.02e+06 2.41e-01 2.883807e-01 3.494757e-01\n",
|
||
|
|
" 2 2.5513945129e+01 -2.00e+06 2.41e-02 -5.571542e-02 3.008984e-02\n",
|
||
|
|
" 3 4.3942069520e-01 -5.71e+06 2.41e-03 -3.233185e-01 -1.938040e-02\n",
|
||
|
|
" 4 1.3950695794e-01 -2.15e+05 2.41e-04 -3.784304e-01 -1.599114e-02\n",
|
||
|
|
" 5 1.3930187120e-01 -1.47e+02 2.41e-05 -3.801893e-01 -1.569610e-02\n",
|
||
|
|
" 6 1.3930184761e-01 -1.69e-02 2.41e-06 -3.802119e-01 -1.569005e-02\n",
|
||
|
|
"iter chisq delta/lim lambda a_3 b_3 \n",
|
||
|
|
"\n",
|
||
|
|
"After 6 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.139302\n",
|
||
|
|
"rel. change during last iteration : -1.69332e-07\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 998\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0118144\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000139581\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"a_3 = -0.380212 +/- 0.001559 (0.41%)\n",
|
||
|
|
"b_3 = -0.0156901 +/- 0.0005992 (3.819%)\n",
|
||
|
|
"\n",
|
||
|
|
"correlation matrix of the fit parameters:\n",
|
||
|
|
" a_3 b_3 \n",
|
||
|
|
"a_3 1.000 \n",
|
||
|
|
"b_3 -0.704 1.000 \n",
|
||
|
|
"fit [0:1] f4(x) onedet_H u 1:($2+ohs) via a_4, b_4\n",
|
||
|
|
"iter chisq delta/lim lambda a_4 b_4 \n",
|
||
|
|
" 0 5.3856009833e+03 0.00e+00 2.41e+00 5.000000e-01 1.000000e+00\n",
|
||
|
|
" 1 4.3790957890e+02 -1.13e+06 2.41e-01 3.455453e-01 3.625475e-01\n",
|
||
|
|
" 2 1.5298404788e+01 -2.76e+06 2.41e-02 1.187994e-01 5.316664e-02\n",
|
||
|
|
" 3 7.1643957948e-02 -2.13e+07 2.41e-03 -2.301630e-02 -5.474022e-04\n",
|
||
|
|
" 4 4.7733373771e-04 -1.49e+07 2.41e-04 -4.157329e-02 -9.283341e-04\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 5 4.7373176545e-04 -7.60e+02 2.41e-05 -4.173964e-02 -9.101041e-04\n",
|
||
|
|
" 6 4.7373176473e-04 -1.53e-04 2.41e-06 -4.173975e-02 -9.100709e-04\n",
|
||
|
|
"iter chisq delta/lim lambda a_4 b_4 \n",
|
||
|
|
"\n",
|
||
|
|
"After 6 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.000473732\n",
|
||
|
|
"rel. change during last iteration : -1.52553e-09\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 998\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.000688971\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 4.74681e-07\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"a_4 = -0.0417397 +/- 7.482e-05 (0.1792%)\n",
|
||
|
|
"b_4 = -0.000910071 +/- 3.289e-05 (3.614%)\n",
|
||
|
|
"\n",
|
||
|
|
"correlation matrix of the fit parameters:\n",
|
||
|
|
" a_4 b_4 \n",
|
||
|
|
"a_4 1.000 \n",
|
||
|
|
"b_4 -0.740 1.000 \n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 3,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"f1(x) = exp(a_1 * x/(1. + b_1*x))\n",
|
||
|
|
"f2(x) = exp(a_2 * x/(1. + b_2*x))\n",
|
||
|
|
"f3(x) = exp(a_3 * x**2+b_3)\n",
|
||
|
|
"f4(x) = exp(a_4 * x**2+b_4)\n",
|
||
|
|
"a_1 = 0.5\n",
|
||
|
|
"a_2 = 0.5\n",
|
||
|
|
"a_3 = 0.5\n",
|
||
|
|
"a_4 = 0.5\n",
|
||
|
|
"b_1 = 1.\n",
|
||
|
|
"b_2 = 1.\n",
|
||
|
|
"b_3 = 1.\n",
|
||
|
|
"b_4 = 1.\n",
|
||
|
|
"\n",
|
||
|
|
"fit f1(x) onedet u 1:($2+os) via b_1\n",
|
||
|
|
"fit f2(x) multidet u 1:($2+ms) via b_2\n",
|
||
|
|
"fit [0:1] f3(x) onedet_O u 1:($2+oos) via a_3, b_3\n",
|
||
|
|
"fit [0:1] f4(x) onedet_H u 1:($2+ohs) via a_4, b_4\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:04.702021Z",
|
||
|
|
"start_time": "2020-04-21T10:24:04.625567Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set yrange [1.:1.3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464644.6269653.290848243961.png'\n",
|
||
|
|
"plot onedet u 1:($2+os) w l title \"HF\" , multidet u 1:($2+ms) w l title \"FCI\" , f1(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\" , f2(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 4,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set yrange [1.:1.3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"plot onedet u 1:($2+os) w l title \"HF\"\\\n",
|
||
|
|
", multidet u 1:($2+ms) w l title \"FCI\"\\\n",
|
||
|
|
", f1(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\"\\\n",
|
||
|
|
", f2(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:05.565443Z",
|
||
|
|
"start_time": "2020-04-21T10:24:05.480752Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set xrange [0:1]\n",
|
||
|
|
"set yrange [0.6:1.]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464645.4823086.669140871663.png'\n",
|
||
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\" , onedet_H u 1:($2+ohs) w l title \"H\" , f3(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\" , f4(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set xrange [0:1]\n",
|
||
|
|
"set yrange [0.6:1.]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"plot onedet_O u 1:($2+oos) w l title \"O\"\\\n",
|
||
|
|
", onedet_H u 1:($2+ohs) w l title \"H\"\\\n",
|
||
|
|
", f3(x) title \"exp(- 0.5 r_{12} / (1. + 1.93 r_{12}))\"\\\n",
|
||
|
|
", f4(x) title \"exp(- 0.5 r_{12} / (1. + 3.99 r_{12}))\"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:08.538856Z",
|
||
|
|
"start_time": "2020-04-21T10:24:08.535239Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"#set term pdf font \"Times,15pt\"\n",
|
||
|
|
"#set output \"jastrow_h2o.pdf\"\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 6,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"#set term pdf font \"Times,15pt\"\n",
|
||
|
|
"#set output \"jastrow_h2o.pdf\""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Range-separated Coulomb operator"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:23.559508Z",
|
||
|
|
"start_time": "2020-04-21T10:24:23.551368Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"w(x) = 1./x\n",
|
||
|
|
"w_lr(mu,x) = erf(mu*x)/x\n",
|
||
|
|
"w_sr(mu,x) = w(x) - w_lr(mu,x)\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 7,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"w(x) = 1./x\n",
|
||
|
|
"w_lr(mu,x) = erf(mu*x)/x\n",
|
||
|
|
"w_sr(mu,x) = w(x) - w_lr(mu,x)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:24.194298Z",
|
||
|
|
"start_time": "2020-04-21T10:24:24.163407Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set xrange [0:4]\n",
|
||
|
|
"set yrange [0:3]\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464664.164814.164661607519.png'\n",
|
||
|
|
"plot w(x) title '1/r_{12}', w_lr(0.5,x) title '{/Symbol m}=0.5' ls 2, w_sr(0.5,x) notitle ls 2, w_lr(1.0,x) title '{/Symbol m}=1.0' ls 4, w_sr(1.0,x) notitle ls 4\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 8,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set xrange [0:4]\n",
|
||
|
|
"set yrange [0:3]\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"plot w(x) title '1/r_{12}',\\\n",
|
||
|
|
" w_lr(0.5,x) title '{/Symbol m}=0.5' ls 2,\\\n",
|
||
|
|
" w_sr(0.5,x) notitle ls 2, \\\n",
|
||
|
|
" w_lr(1.0,x) title '{/Symbol m}=1.0' ls 4,\\\n",
|
||
|
|
" w_sr(1.0,x) notitle ls 4"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## DMC Energies"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Single-determinant"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:32.345058Z",
|
||
|
|
"start_time": "2020-04-21T10:24:32.301229Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"data = \"Jastrows/data_dmc\"\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"set xrange [0:7.5]\n",
|
||
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
||
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
||
|
|
"set format y \"%.4f\"\n",
|
||
|
|
"set yrange [-76.4115:-76.4095]\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464672.3021863.691199111999.png'\n",
|
||
|
|
"plot data index 0 u 1:5:6 w errorlines title \"1 det / cc-pVDZ\"\n",
|
||
|
|
"set yrange [-76.4215:-76.4195]\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464672.302258.883689911885.png'\n",
|
||
|
|
"plot data index 1 u 1:5:6 w errorlines title \"1 det / cc-pVTZ\"\n",
|
||
|
|
"set yrange [-76.4232:-76.4212]\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464672.3023045.89529610025.png'\n",
|
||
|
|
"plot data index 2 u 1:5:6 w errorlines title \"1 det / cc-pVQZ\"\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data = \"Jastrows/data_dmc\"\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"set xrange [0:7.5]\n",
|
||
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
||
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
||
|
|
"set format y \"%.4f\"\n",
|
||
|
|
"set yrange [-76.4115:-76.4095]\n",
|
||
|
|
"plot data index 0 u 1:5:6 w errorlines title \"1 det / cc-pVDZ\"\n",
|
||
|
|
"set yrange [-76.4215:-76.4195]\n",
|
||
|
|
"plot data index 1 u 1:5:6 w errorlines title \"1 det / cc-pVTZ\"\n",
|
||
|
|
"set yrange [-76.4232:-76.4212]\n",
|
||
|
|
"plot data index 2 u 1:5:6 w errorlines title \"1 det / cc-pVQZ\""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"### Multi-determinant"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:44.333376Z",
|
||
|
|
"start_time": "2020-04-21T10:24:44.311082Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"data = \"Jastrows/data_dmc\"\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"set xrange [0:7.5]\n",
|
||
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
||
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
||
|
|
"set format y \"%.3f\"\n",
|
||
|
|
"set yrange [-76.44:-76.405]\n",
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464684.3126948.332194534957.png'\n",
|
||
|
|
"plot data index 0 u 1:7:8 w errorlines title \"CIPSI / cc-pVDZ\", data index 1 u 1:7:8 w errorlines title \"CIPSI / cc-pVTZ\", data index 2 u 1:7:8 w errorlines title \"CIPSI / cc-pVQZ\"\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 11,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"data = \"Jastrows/data_dmc\"\n",
|
||
|
|
"set key top right\n",
|
||
|
|
"set xrange [0:7.5]\n",
|
||
|
|
"set xlabel \"{/Symbol m} (a.u.)\"\n",
|
||
|
|
"set ylabel \"Energy (a.u.)\"\n",
|
||
|
|
"set format y \"%.3f\"\n",
|
||
|
|
"set yrange [-76.44:-76.405]\n",
|
||
|
|
"plot data index 0 u 1:7:8 w errorlines title \"CIPSI / cc-pVDZ\", \\\n",
|
||
|
|
"data index 1 u 1:7:8 w errorlines title \"CIPSI / cc-pVTZ\", \\\n",
|
||
|
|
"data index 2 u 1:7:8 w errorlines title \"CIPSI / cc-pVQZ\""
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## Fit $\\mu$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"-----\n",
|
||
|
|
"$\\Psi(r_1,\\dots,r_N)$ is a CI trial wave function:\n",
|
||
|
|
"$$\n",
|
||
|
|
"|\\Psi \\rangle = \\sum_{I \\in \\mathcal{B}} c_I |I\\rangle\n",
|
||
|
|
"$$\n",
|
||
|
|
"When running a FN-DMC calculation, the fixed-node wave function can be written as $\\Phi(r_1,\\dots,r_N) = \\Psi(r_1,\\dots,r_N)\\times w(r_1,\\dots,r_N)$, where $w$ is a positive function, such that\n",
|
||
|
|
"$$\n",
|
||
|
|
"E = \\min_w \\frac{ \\langle w \\Psi | H | \\Psi \\rangle } {\\langle w \\Psi | \\Psi \\rangle}\n",
|
||
|
|
"$$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"We want to find the change in the potential that would model the effect of the FN-DMC. This corresponds to removing some short-range potential:\n",
|
||
|
|
"\n",
|
||
|
|
"$$\n",
|
||
|
|
"\\frac{ \\langle w \\Psi | V_{ee} | w \\Psi \\rangle } {\\langle w \\Psi | w \\Psi \\rangle} = \n",
|
||
|
|
"\\frac{ \\langle \\Psi | V_{ee} - \\delta V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} \n",
|
||
|
|
"$$\n",
|
||
|
|
"where\n",
|
||
|
|
"\n",
|
||
|
|
"$$\n",
|
||
|
|
"V_{ee} = \\frac{1}{r_{12}} \n",
|
||
|
|
"$$\n",
|
||
|
|
"and\n",
|
||
|
|
"$$\n",
|
||
|
|
"\\delta V_{ee} = \n",
|
||
|
|
"\\alpha \\left( \\frac{1}{r_{12}} - \\frac{\\text{erf}( \\mu r_{12})}{r_{12}} \\right).\n",
|
||
|
|
"$$\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"$$\n",
|
||
|
|
"\\frac{ \\langle w \\Psi | V_{ee} | w \\Psi \\rangle } {\\langle w \\Psi | w \\Psi \\rangle} = \n",
|
||
|
|
"\\frac{ \\langle \\Psi | V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} -\n",
|
||
|
|
"\\alpha \\frac{ \\langle \\Psi | V_{ee} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} +\n",
|
||
|
|
"\\alpha \\frac{ \\langle \\Psi | V_{ee}^{lr} | \\Psi \\rangle } {\\langle \\Psi | \\Psi \\rangle} \n",
|
||
|
|
"$$\n",
|
||
|
|
"\n",
|
||
|
|
"$$\n",
|
||
|
|
"\\frac{\\langle \\Psi | w^2 V_{ee} | \\Psi \\rangle}\n",
|
||
|
|
"{\\langle w \\Psi | w \\Psi \\rangle}\n",
|
||
|
|
"=\n",
|
||
|
|
"(1-\\alpha) \\langle \\Psi | V_{ee} | \\Psi \\rangle +\n",
|
||
|
|
"\\alpha \\langle \\Psi | V_{ee}^{lr} | \\Psi \\rangle \n",
|
||
|
|
"$$\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"$$\n",
|
||
|
|
"w^2 = \\left[ (1-\\alpha) + \\alpha\\, \\text{erf}(\\mu\\, r_{12}) \\right]\\langle w \\Psi | w \\Psi \\rangle\n",
|
||
|
|
"$$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"To find the parameter $\\mu$, we minimize\n",
|
||
|
|
"$$\n",
|
||
|
|
"\\int \\left|a\\, \\text{erf}(\\mu\\,r_{12}) + b - w(r_{12})^2 \\right|^2 \\text{d}r_{12}\n",
|
||
|
|
"$$"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:24:58.433108Z",
|
||
|
|
"start_time": "2020-04-21T10:24:58.207652Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set yrange [1:1.3]\n",
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
||
|
|
"mu = 2.\n",
|
||
|
|
"b=1.\n",
|
||
|
|
"a = 1.\n",
|
||
|
|
"fit f(x) multidet u 1:( ($2+ms)**2 ) via a, b, mu\n",
|
||
|
|
"Max. number of data points scaled up to: 3072\n",
|
||
|
|
"iter chisq delta/lim lambda a b mu \n",
|
||
|
|
" 0 1.4844650639e+03 0.00e+00 7.96e-01 1.000000e+00 1.000000e+00 2.000000e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 1 6.1263778783e-01 -2.42e+08 7.96e-02 2.358053e-01 1.001813e+00 2.187483e+00\n",
|
||
|
|
" 2 1.0286041895e-01 -4.96e+05 7.96e-03 2.362049e-01 1.001768e+00 2.898819e+00\n",
|
||
|
|
" 3 8.8809271871e-02 -1.58e+04 7.96e-04 2.419347e-01 9.971019e-01 3.071483e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 4 8.8738434949e-02 -7.98e+01 7.96e-05 2.413214e-01 9.977999e-01 3.052213e+00\n",
|
||
|
|
" 5 8.8736855066e-02 -1.78e+00 7.96e-06 2.414138e-01 9.977008e-01 3.054867e+00\n",
|
||
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-05 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-04 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
||
|
|
" * 8.8736862980e-02 8.92e-03 7.96e-03 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
||
|
|
" * 8.8736862977e-02 8.91e-03 7.96e-02 2.414014e-01 9.977142e-01 3.054509e+00\n",
|
||
|
|
" * 8.8736862692e-02 8.59e-03 7.96e-01 2.414015e-01 9.977141e-01 3.054511e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 6 8.8736851827e-02 -3.65e-03 7.96e-02 2.414067e-01 9.977084e-01 3.054660e+00\n",
|
||
|
|
"iter chisq delta/lim lambda a b mu \n",
|
||
|
|
"\n",
|
||
|
|
"After 6 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.0887369\n",
|
||
|
|
"rel. change during last iteration : -3.65015e-08\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 2997\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.00544137\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 2.96086e-05\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"a = 0.241407 +/- 0.0006833 (0.283%)\n",
|
||
|
|
"b = 0.997708 +/- 0.0006832 (0.06848%)\n",
|
||
|
|
"mu = 3.05466 +/- 0.01162 (0.3804%)\n",
|
||
|
|
"\n",
|
||
|
|
"correlation matrix of the fit parameters:\n",
|
||
|
|
" a b mu \n",
|
||
|
|
"a 1.000 \n",
|
||
|
|
"b -0.987 1.000 \n",
|
||
|
|
"mu 0.590 -0.636 1.000 \n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 12,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set yrange [1:1.3]\n",
|
||
|
|
"set xrange [0:3]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
||
|
|
"mu = 2.\n",
|
||
|
|
"b=1.\n",
|
||
|
|
"a = 1.\n",
|
||
|
|
"fit f(x) multidet u 1:( ($2+ms)**2 ) via a, b, mu\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:25:03.688870Z",
|
||
|
|
"start_time": "2020-04-21T10:25:03.627414Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464703.6283643.751356038540.png'\n",
|
||
|
|
"plot f(x), multidet u 1:( ($2+ms)**2) title \"{/Symbol m}=3.05\" w l\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 13,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot f(x), multidet u 1:( ($2+ms)**2) title \"{/Symbol m}=3.05\" w l"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:25:10.154538Z",
|
||
|
|
"start_time": "2020-04-21T10:25:09.898089Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"unset output\n",
|
||
|
|
" set yrange [1:1.7]\n",
|
||
|
|
"set xrange [0:2.5]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
||
|
|
"mu = 2.\n",
|
||
|
|
"b=1.\n",
|
||
|
|
"a = 1.\n",
|
||
|
|
"fit f(x) onedet u 1:( ($2+os)**2) via a, b, mu\n",
|
||
|
|
"Max. number of data points scaled up to: 3072\n",
|
||
|
|
"iter chisq delta/lim lambda a b mu \n",
|
||
|
|
" 0 6.3210450983e+02 0.00e+00 7.92e-01 1.000000e+00 1.000000e+00 2.000000e+00\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 1 3.7399726680e+00 -1.68e+07 7.92e-02 4.372151e-01 1.042051e+00 1.438663e+00\n",
|
||
|
|
" 2 1.0886112747e+00 -2.44e+05 7.92e-03 4.564069e-01 1.038516e+00 9.132499e-01\n",
|
||
|
|
" 3 4.1264008258e-01 -1.64e+05 7.92e-04 4.670442e-01 1.043923e+00 9.352905e-01\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" 4 4.1237511508e-01 -6.43e+01 7.92e-05 4.674657e-01 1.043147e+00 9.390785e-01\n",
|
||
|
|
" 5 4.1236761417e-01 -1.82e+00 7.92e-06 4.675227e-01 1.043020e+00 9.397870e-01\n",
|
||
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-05 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-04 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
||
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-03 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
" * 4.1236765338e-01 9.51e-03 7.92e-02 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
||
|
|
" * 4.1236765335e-01 9.50e-03 7.92e-01 4.675328e-01 1.042997e+00 9.399192e-01\n",
|
||
|
|
" * 4.1236765043e-01 8.79e-03 7.92e+00 4.675326e-01 1.042997e+00 9.399178e-01\n",
|
||
|
|
" 6 4.1236757813e-01 -8.74e-03 7.92e-01 4.675248e-01 1.043011e+00 9.398501e-01\n",
|
||
|
|
"iter chisq delta/lim lambda a b mu \n",
|
||
|
|
"\n",
|
||
|
|
"After 6 iterations the fit converged.\n",
|
||
|
|
"final sum of squares of residuals : 0.412368\n",
|
||
|
|
"rel. change during last iteration : -8.74041e-08\n",
|
||
|
|
"\n",
|
||
|
|
"degrees of freedom (FIT_NDF) : 2497\n",
|
||
|
|
"rms of residuals (FIT_STDFIT) = sqrt(WSSR/ndf) : 0.0128509\n",
|
||
|
|
"variance of residuals (reduced chisquare) = WSSR/ndf : 0.000165145\n",
|
||
|
|
"\n",
|
||
|
|
"Final set of parameters Asymptotic Standard Error\n",
|
||
|
|
"======================= ==========================\n",
|
||
|
|
"a = 0.467525 +/- 0.0009255 (0.198%)\n",
|
||
|
|
"b = 1.04301 +/- 0.0009232 (0.08851%)\n",
|
||
|
|
"mu = 0.93985 +/- 0.003224 (0.3431%)\n",
|
||
|
|
"\n",
|
||
|
|
"correlation matrix of the fit parameters:\n",
|
||
|
|
" a b mu \n",
|
||
|
|
"a 1.000 \n",
|
||
|
|
"b -0.873 1.000 \n",
|
||
|
|
"mu 0.269 -0.620 1.000 \n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 14,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"set yrange [1:1.7]\n",
|
||
|
|
"set xrange [0:2.5]\n",
|
||
|
|
"set grid\n",
|
||
|
|
"set xlabel \"r_{12} (a.u.)\"\n",
|
||
|
|
"set format y \"%.2f\"\n",
|
||
|
|
"set format x \"%.1f\"\n",
|
||
|
|
"set key bottom right\n",
|
||
|
|
"\n",
|
||
|
|
"f(x) = (a * (erf(mu*x)) +b)\n",
|
||
|
|
"mu = 2.\n",
|
||
|
|
"b=1.\n",
|
||
|
|
"a = 1.\n",
|
||
|
|
"fit f(x) onedet u 1:( ($2+os)**2) via a, b, mu\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {
|
||
|
|
"ExecuteTime": {
|
||
|
|
"end_time": "2020-04-21T10:25:15.207225Z",
|
||
|
|
"start_time": "2020-04-21T10:25:15.165730Z"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<IPython.core.display.Image object>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"set output '/tmp/gnuplot-inline-1587464715.1665385.128003803699.png'\n",
|
||
|
|
"plot f(x), onedet u 1:( ($2+os)**2 ) title \"{/Symbol m}=0.94\" w l\n",
|
||
|
|
"unset output\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 15,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"plot f(x), onedet u 1:( ($2+os)**2 ) title \"{/Symbol m}=0.94\" w l"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "gnuplot",
|
||
|
|
"language": "gnuplot",
|
||
|
|
"name": "gnuplot"
|
||
|
|
},
|
||
|
|
"language_info": {
|
||
|
|
"codemirror_mode": "Octave",
|
||
|
|
"file_extension": ".gp",
|
||
|
|
"help_links": [
|
||
|
|
{
|
||
|
|
"text": "MetaKernel Magics",
|
||
|
|
"url": "https://metakernel.readthedocs.io/en/latest/source/README.html"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"mimetype": "text/x-gnuplot",
|
||
|
|
"name": "gnuplot"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 2
|
||
|
|
}
|