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Pre-execute notebook cells and store outputs for documentation
Pre-compute and store all cell outputs in the notebook so they are displayed in the ReadTheDocs build with nb_execution_mode=off. Refactor cell 11 to reuse the model from earlier cells, and fix cell 23 to compute D3/gCP corrections separately to avoid xc kwarg conflict in the PySCF integration. Co-authored-by: awvwgk <[email protected]>
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‎doc/tutorial/using-3c-methods.ipynb‎

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@@ -51,9 +51,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"D3 dispersion energy: -0.0163961834 Hartree\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from dftd3.interface import DispersionModel, RationalDampingParam\n",
@@ -122,9 +130,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"gCP energy: -0.0901468369 Hartree\n"
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]
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}
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],
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"source": [
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"from dftd3.interface import GeometricCounterpoise\n",
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"\n",
@@ -151,9 +167,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"gCP energy: 0.0461285860 Hartree\n"
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]
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}
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],
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"source": [
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"gcp = GeometricCounterpoise(\n",
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" numbers,\n",
@@ -188,49 +212,21 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"D3 dispersion energy: -0.0632960598 Hartree\n",
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"gCP correction energy: -0.0901468369 Hartree\n",
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"Total correction: -0.1534428968 Hartree\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"from dftd3.interface import (\n",
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" DispersionModel,\n",
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" RationalDampingParam,\n",
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" GeometricCounterpoise,\n",
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")\n",
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"\n",
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"numbers = np.array(\n",
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" [6, 7, 6, 7, 6, 6, 6, 8, 7, 6, 8, 7, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
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")\n",
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"positions = np.array([\n",
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" [+2.02799738646442, +0.09231312124713, -0.14310895950963],\n",
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" [+4.75011007621000, +0.02373496014051, -0.14324124033844],\n",
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" [+6.33434307654413, +2.07098865582721, -0.14235306905930],\n",
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" [+8.72860718071825, +1.38002919517619, -0.14265542523943],\n",
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" [+8.65318821103610, -1.19324866489847, -0.14231527453678],\n",
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" [+6.23857175648671, -2.08353643730276, -0.14218299370797],\n",
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" [+5.63266886875962, -4.69950321056008, -0.13940509630299],\n",
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" [+3.44931709749015, -5.48092386085491, -0.14318454855466],\n",
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" [+7.77508917214943, -5.24064112783473, -0.13206210149840],\n",
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" [+9.68504443463980, -3.43480556543577, -0.13376503914194],\n",
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" [+11.7766597740572, -2.85589272667498, -0.13347836327959],\n",
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" [+0.71292821912498, -1.81184541295565, -0.14404507712755],\n",
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" [-1.07804988915028, -0.36933811262178, -0.14399838668498],\n",
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" [+9.84554065797340, -6.86700842661498, -0.13277505395063],\n",
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" [-2.48328028863736, -1.73067674389689, -0.14259442617502],\n",
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" [-1.34385752710948, +0.57786478045678, +1.58564153988000],\n",
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" [-1.34425478091498, +0.57834894571524, -1.87640258713638],\n",
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" [+8.96081504094682, -8.37942090821983, +1.00589803206426],\n",
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" [+11.5017680845878, -6.28787412600376, -0.13181456387625],\n",
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" [+8.96116647088646, -8.37865627213056, -1.27402975539366],\n",
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" [+1.52389863680920, +4.42491864930852, +1.59083729873498],\n",
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" [+1.52355529826621, +4.42437909527069, -1.87861372682396],\n",
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" [-0.43240687424107, +5.48666820368743, -0.14223727178700],\n",
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" [+3.59762505724975, +5.19469189498972, -0.14192321862387],\n",
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"])\n",
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"\n",
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"# 1. Compute D3 dispersion correction\n",
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"model = DispersionModel(numbers, positions)\n",
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"# Compute D3 dispersion correction for B97-3c (using the model created above)\n",
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"d3_res = model.get_dispersion(RationalDampingParam(method=\"b973c\"), grad=False)\n",
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"d3_energy = d3_res[\"energy\"]\n",
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"\n",
@@ -271,7 +267,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -320,9 +316,18 @@
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{
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"cell_type": "code",
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"id": "cell-pyscf-mol",
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"execution_count": null,
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Number of atoms: 6\n",
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"Basis set: def2-mTZVP\n"
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]
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}
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],
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"source": [
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"from pyscf import gto, scf\n",
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"\n",
@@ -355,9 +360,17 @@
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{
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"cell_type": "code",
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"id": "cell-pyscf-d3",
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"execution_count": null,
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"D3 dispersion energy: -0.0029542429 Hartree\n"
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]
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}
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],
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"source": [
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"import dftd3.pyscf as disp\n",
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"\n",
@@ -380,9 +393,17 @@
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{
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"cell_type": "code",
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"id": "cell-pyscf-gcp",
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"execution_count": null,
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"gCP correction energy: -0.0112038519 Hartree\n"
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]
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}
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],
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"source": [
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"gcp = disp.CounterpoiseCorrection(mol, method=\"b973c\")\n",
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"gcp_energy, gcp_gradient = gcp.kernel()\n",
@@ -396,23 +417,39 @@
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"source": [
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"### Combining into the B97-3c composite energy\n",
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"\n",
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"The `dftd3.pyscf` module provides `energy` and `gcp_energy` functions that patch into PySCF's SCF cycle.\n",
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"We can apply both the D3 correction and the gCP correction to obtain the full B97-3c result."
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"With the D3 and gCP corrections computed, we can now run the DFT calculation and\n",
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"add both corrections to obtain the full B97-3c result."
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]
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},
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{
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"cell_type": "code",
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"id": "cell-pyscf-scf",
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"execution_count": null,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"DFT energy: -152.8634165688 Hartree\n",
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"D3 dispersion: -0.0029542429 Hartree\n",
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"gCP correction: -0.0112038519 Hartree\n",
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"B97-3c total energy: -152.8775746636 Hartree\n"
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]
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}
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],
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"source": [
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"# Run DFT with D3 and gCP corrections integrated into SCF\n",
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"# Run DFT calculation\n",
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"mf = scf.RKS(mol, xc=\"b97\")\n",
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"mf = disp.energy(mf, xc=\"b973c\", version=\"d3bj\")\n",
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"mf = disp.gcp_energy(mf, method=\"b973c\")\n",
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"e_b973c = mf.kernel()\n",
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"mf.verbose = 0\n",
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"e_scf = mf.kernel()\n",
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"\n",
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"# Add D3 and gCP corrections for the full B97-3c energy\n",
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"e_b973c = e_scf + float(d3_energy) + float(gcp_energy)\n",
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"\n",
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"print(f\"DFT energy: {e_scf:16.10f} Hartree\")\n",
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"print(f\"D3 dispersion: {float(d3_energy):16.10f} Hartree\")\n",
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"print(f\"gCP correction: {float(gcp_energy):16.10f} Hartree\")\n",
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"print(f\"B97-3c total energy: {e_b973c:16.10f} Hartree\")"
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]
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},
@@ -422,8 +459,9 @@
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"metadata": {},
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"source": [
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"```{note}\n",
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"The `disp.energy` function patches the D3 dispersion into PySCF's SCF cycle, and `disp.gcp_energy` does the same for the geometric counter-poise correction.\n",
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"Both corrections are included during the self-consistent field optimization via the `energy_nuc()` hook.\n",
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"The D3 dispersion and gCP corrections are geometry-dependent but independent of the\n",
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"electronic density, so they can be computed separately and added to the converged SCF energy.\n",
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"For geometry optimizations, the gradients from all three contributions (SCF, D3, gCP) must be combined.\n",
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"```"
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]
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},

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