{ "cells": [ { "cell_type": "markdown", "id": "c36973f8", "metadata": {}, "source": [ "# Excitations of thiophene in its environment\n", "\n", "How does the environment of a molecule in an amorphous film change its optical excitations? This tutorial computes the lowest singlet excitations of one thiophene molecule from the morphology of the tutorial *From an MD morphology to a state file* with GW-BSE, first in vacuum and then embedded in its neighbours, and compares the ways VOTCA-XTP offers to include the environment:\n", "\n", "| scheme | environment | how it acts on the excitation |\n", "|---|---|---|\n", "| vacuum | none | |\n", "| static | fixed multipoles | electrostatic field of the neighbours |\n", "| polarizable, iterative | multipoles and induced dipoles | the environment is polarized self-consistently by the ground state and, separately, by the excited state; the excitation energy is the difference of the two total energies (a $\\Delta$SCF-like scheme) |\n", "| polarizable, embedded | multipoles and induced dipoles | the environment is polarized by the ground state; its response to the excitation enters GW-BSE as an additional screening (reaction field) |\n", "\n", "The two polarizable schemes are described in the manual chapter *Embedded GW-BSE*.\n", "\n", "## Requirements\n", "* VOTCA installed and the environment activated with `VOTCARC.bash` (see the [installation instructions](https://github.com/votca/votca/blob/master/share/sphinx/INSTALL.rst)).\n", "* Python with `numpy`, `h5py` and `matplotlib`." ] }, { "cell_type": "code", "execution_count": 1, "id": "a971360f", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:58:45.674935Z", "iopub.status.busy": "2026-11-10T20:58:45.674287Z", "iopub.status.idle": "2026-11-10T20:58:46.523859Z", "shell.execute_reply": "2026-11-10T20:58:46.523305Z" } }, "outputs": [], "source": [ "import subprocess\n", "import numpy as np\n", "import h5py\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "def run(command, show=0, output=False):\n", " \"\"\"Run a VOTCA command; print the last `show` lines of its output (0: none).\"\"\"\n", " result = subprocess.run(command, shell=True, text=True,\n", " stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n", " if result.returncode != 0:\n", " print(result.stdout)\n", " raise RuntimeError(f\"'{command}' failed\")\n", " if show:\n", " print(\"\\n\".join(result.stdout.splitlines()[-show:]))\n", " return result.stdout if output else None\n", "import glob\n", "import xml.etree.ElementTree as ET\n", "\n", "HARTREE = 27.211386 # eV\n", "\n", "run(\"rm -rf state.hdf5 QMMM *.jobs excitation_*.xml\")\n", "run(\"xtp_map -t MD_FILES/thiophene.data -c MD_FILES/traj.dump -s system.xml -f state.hdf5\")" ] }, { "cell_type": "markdown", "id": "13ef74d3", "metadata": {}, "source": [ "## Job options\n", "\n", "All calculations are `qmmm` jobs for molecule 0. Region 0 is the QM region: the molecule described with DFT and GW-BSE (def2-SVP basis, G0W0, all singlets of the full BSE). Its `state` is the excitation that the job follows: `s1` is the lowest singlet, tracked through the calculation by the overlap of its wave function with that of the previous iteration (`statetracker`). The geometry of the molecule is the ground-state geometry mapped onto the MD (`use_gs_for_ex`), since the excitations are vertical." ] }, { "cell_type": "code", "execution_count": 2, "id": "47080ee5", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:58:46.526293Z", "iopub.status.busy": "2026-11-10T20:58:46.526010Z", "iopub.status.idle": "2026-11-10T20:58:46.532882Z", "shell.execute_reply": "2026-11-10T20:58:46.532332Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " \n", " system.xml\n", " embedded.jobs\n", " \n", " s1\n", " 0\n", " true\n", " \n", " \n", " \n", " 0\n", " jobfile\n", " jobfile\n", " \n", " def2-svp\n", " aux-def2-svp\n", " \n", " \n", " G0W0\n", " full\n", " 10\n", " \n", " 0.8\n", " \n", " \n", " \n", " 1\n", " \n", " n\n", " 1.0\n", " 0\n", " \n", " \n", " \n", " 2\n", " \n", " n\n", " 2.0\n", " 0\n", " \n", " \n", " \n", " \n", "\n", "\n" ] } ], "source": [ "qm_region = \"\"\"\n", " \n", " 0\n", " jobfile\n", " jobfile\n", " \n", " def2-svp\n", " aux-def2-svp\n", " \n", " \n", " G0W0\n", " full\n", " 10\n", " \n", " 0.8{screening}\n", " \"\"\"\n", "\n", "polar_region = \"\"\"\n", " \n", " {id}\n", " \n", " n\n", " 1.0\n", " 0\n", " \n", " \"\"\"\n", "\n", "static_region = \"\"\"\n", " \n", " {id}\n", " \n", " n\n", " 2.0\n", " 0\n", " \n", " \"\"\"\n", "\n", "\n", "def write_options(name, regions, states=\"n s1\"):\n", " open(f\"excitation_{name}.xml\", \"w\").write(f\"\"\"\n", "\n", " \n", " system.xml\n", " {name}.jobs\n", " \n", " {states}\n", " 0\n", " true\n", " \n", " {regions}\n", " \n", " \n", "\n", "\"\"\")\n", "\n", "\n", "write_options(\"vacuum\", qm_region.format(screening=\"\"))\n", "write_options(\"static\", qm_region.format(screening=\"\") + static_region.format(id=1))\n", "write_options(\"iterative\", qm_region.format(screening=\"\") + polar_region.format(id=1)\n", " + static_region.format(id=2))\n", "write_options(\"embedded\", qm_region.format(screening=\"\\n \")\n", " + polar_region.format(id=1) + static_region.format(id=2), states=\"s1\")\n", "print(open(\"excitation_embedded.xml\").read())" ] }, { "cell_type": "markdown", "id": "f4f3b303", "metadata": {}, "source": [ "The environment consists of the neighbours within 1 nm of the molecule as a polarizable region (atomic multipoles and polarizabilities, Thole-damped induced dipoles) and those up to 2 nm as a static region (multipoles only). The embedded scheme is switched on by `environment_screening` in the QM region; everything else is the same as for the iterative scheme. It screens a GW-BSE calculation, so it has no ground-state job of its own: only the `s1` job is written.\n", "\n", "## Running the jobs\n", "\n", "`-j write` creates one job for the ground state `n` and one for the excitation `s1` of molecule 0 (for the embedded scheme only the latter). The results of each job go to `QMMM/frame_10000/job__`, so we give the jobs of each scheme their own tags before running them:" ] }, { "cell_type": "code", "execution_count": 3, "id": "2295ddae", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:58:46.535109Z", "iopub.status.busy": "2026-11-10T20:58:46.534535Z", "iopub.status.idle": "2026-11-10T20:59:38.975132Z", "shell.execute_reply": "2026-11-10T20:59:38.974594Z" } }, "outputs": [], "source": [ "def run_scheme(name):\n", " options = f\"excitation_{name}.xml\"\n", " run(f\"xtp_parallel -e qmmm -o {options} -f state.hdf5 -j write\")\n", " tree = ET.parse(f\"{name}.jobs\")\n", " for job in tree.getroot().iter(\"job\"):\n", " state = job.find(\"input/regions/qmregion/state\").text\n", " job.find(\"tag\").text = f\"{name}_{state}\"\n", " tree.write(f\"{name}.jobs\")\n", " return run(f\"xtp_parallel -e qmmm -o {options} -f state.hdf5 -j run -x 2\", output=True)\n", "\n", "\n", "logs = {name: run_scheme(name) for name in [\"vacuum\", \"static\", \"iterative\", \"embedded\"]}" ] }, { "cell_type": "markdown", "id": "7cc2c1d6", "metadata": {}, "source": [ "## Results\n", "\n", "Each job writes the total energy of all regions to its job file and the GW-BSE results of the QM region to HDF5 checkpoints, one per iteration of the self-consistency between the regions (for the embedded scheme, `checkpoint_screened.hdf5` holds the final screened GW-BSE run). We read the singlet energies $\\Omega$ and the transition dipoles $\\mathbf{d}$, from which the oscillator strengths $f = \\tfrac{2}{3}\\Omega|\\mathbf{d}|^2$ follow (atomic units):" ] }, { "cell_type": "code", "execution_count": 4, "id": "f017b3aa", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:59:38.977784Z", "iopub.status.busy": "2026-11-10T20:59:38.977147Z", "iopub.status.idle": "2026-11-10T20:59:38.991018Z", "shell.execute_reply": "2026-11-10T20:59:38.990504Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "scheme S1 (eV) f(S1) S2 (eV) f(S2) E(s1)-E(n) (eV)\n", "vacuum 5.831 0.067 5.907 0.092 5.831\n", "static 5.838 0.068 5.924 0.092 5.838\n", "iterative 5.835 0.069 5.924 0.092 5.839\n", "embedded 5.958 0.082 6.046 0.108 -\n" ] } ], "source": [ "def total_energy(name, state):\n", " root = ET.parse(f\"{name}.jobs\").getroot()\n", " for job in root.iter(\"job\"):\n", " if job.find(\"tag\").text == f\"{name}_{state}\":\n", " return float(job.find(\"output/E_tot\").text) # eV\n", "\n", "\n", "def singlets(name):\n", " folder = glob.glob(f\"QMMM/frame_10000/job_*_{name}_s1\")[0]\n", " files = glob.glob(f\"{folder}/checkpoint_screened.hdf5\") or sorted(\n", " glob.glob(f\"{folder}/checkpoint_iter_*.hdf5\"), key=lambda f: int(f.split(\"_\")[-1][:-5]))\n", " with h5py.File(files[-1], \"r\") as f:\n", " qm = f[\"region_0\"][\"QMdata\"]\n", " omega = qm[\"BSE_singlet\"][\"eigenvalues\"][()].ravel()\n", " dipoles = np.array([qm[\"transition_dipoles\"][f\"ind{i}\"][()].ravel() for i in range(len(omega))])\n", " return omega * HARTREE, 2 / 3 * omega * np.sum(dipoles**2, axis=1)\n", "\n", "\n", "print(\"scheme S1 (eV) f(S1) S2 (eV) f(S2) E(s1)-E(n) (eV)\")\n", "spectra = {}\n", "for name in [\"vacuum\", \"static\", \"iterative\", \"embedded\"]:\n", " energy, strength = singlets(name)\n", " spectra[name] = (energy, strength)\n", " if name == \"embedded\":\n", " delta = \"-\"\n", " else:\n", " delta = f\"{total_energy(name, 's1') - total_energy(name, 'n'):.3f}\"\n", " print(f\"{name:10s} {energy[0]:8.3f} {strength[0]:7.3f} {energy[1]:9.3f} {strength[1]:7.3f} {delta:>12s}\")" ] }, { "cell_type": "markdown", "id": "c9955e3d", "metadata": {}, "source": [ "How to read the table:\n", "\n", "* For **vacuum** and the **static** environment, the difference of the total energies of the `s1` and `n` jobs equals the BSE excitation energy, since nothing in the environment responds to the excitation.\n", "* In the **iterative** scheme, the environment is polarized by the density of the excited state in the `s1` job and by that of the ground state in the `n` job. The excitation energy is the difference of the total energies, which contains the change of the polarization energy between the two states. The BSE energy of the `s1` job alone is the excitation energy in an environment polarized by the excited state, not the excitation energy of the molecule in its ground-state environment.\n", "* In the **embedded** scheme, the environment is polarized by the ground state in the self-consistent loop, and its response to the excitation is part of the screened interaction of GW-BSE. The excitation energy is the BSE energy of the final, screened run.\n", "\n", "For thiophene, a small molecule without permanent dipole moment whose lowest excitations change its charge distribution little, the static and iterative schemes shift the two lowest excitations by less than 0.02 eV. The embedded scheme shifts them by about 0.13 eV to higher energies and increases their oscillator strengths slightly. Its log shows why:" ] }, { "cell_type": "code", "execution_count": 5, "id": "3c44054e", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:59:38.992852Z", "iopub.status.busy": "2026-11-10T20:59:38.992663Z", "iopub.status.idle": "2026-11-10T20:59:38.996249Z", "shell.execute_reply": "2026-11-10T20:59:38.995812Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HOMO +0.5847 eV, LUMO -0.6651 eV, gap -1.2498 eV\n" ] } ], "source": [ "for line in logs[\"embedded\"].splitlines():\n", " if \"HOMO\" in line and \"gap\" in line:\n", " print(line.split(\"...\")[-1].strip())" ] }, { "cell_type": "markdown", "id": "e752f03f", "metadata": {}, "source": [ "The reaction field of the environment stabilizes the added electron and the added hole of the quasiparticle states and closes the quasiparticle gap by more than 1 eV; these are the polarization energies of the charged states that also enter the site energies of the charge-transport tutorial. The same reaction field screens the attraction between electron and hole and reduces the exciton binding energy by almost as much, and the excitation energy is the small difference of the two.\n", "\n", "The iterative scheme contains the response of the environment to the *mean* change of the charge density upon excitation, which is small for thiophene. The embedded scheme contains this as well and, in addition, the response to the correlated motion of electron and hole and to the transition density (manual chapter *Embedded GW-BSE*, section *Relation to the iterative scheme*), which here produce the shift of about 0.13 eV.\n", "\n", "## Spectra\n", "\n", "The absorption spectra of the two lowest bright singlets, broadened with Gaussians of 0.1 eV width:" ] }, { "cell_type": "code", "execution_count": 6, "id": "c66ad5df", "metadata": { "execution": { "iopub.execute_input": "2026-11-10T20:59:38.998204Z", "iopub.status.busy": "2026-11-10T20:59:38.997759Z", "iopub.status.idle": "2026-11-10T20:59:39.100923Z", "shell.execute_reply": "2026-11-10T20:59:39.100267Z" } }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid = np.linspace(5.4, 6.6, 600)\n", "for name, (energy, strength) in spectra.items():\n", " spectrum = sum(f * np.exp(-0.5 * ((grid - e) / 0.1)**2) for e, f in zip(energy, strength))\n", " plt.plot(grid, spectrum, label=name)\n", "plt.xlabel(\"energy (eV)\")\n", "plt.ylabel(\"absorption (arb. units)\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2b1a95a4", "metadata": {}, "source": [ "## Summary\n", "\n", "* `qmmm` jobs combine a QM region (DFT and GW-BSE) with static and polarizable classical regions; the states of the job (`n`, `s1`, ...) and the regions are set in the options and in the job file.\n", "* For neutral excitations in a nonpolar environment, the electrostatic field and the state-specific polarization (static and iterative schemes) cause small shifts.\n", "* The embedded scheme adds the environment's response to the correlated motion of electron and hole: the polarization of the quasiparticles and the screening of the electron-hole attraction, which are large individually and largely cancel in the excitation energy. It needs one GW-BSE run instead of one per iteration.\n", "* With a small QM region and a 1 nm polarizable shell this tutorial runs in about a minute; production calculations need a converged environment (larger polarizable region, or the periodic Ewald background of the charge-transport tutorial) and several molecules for statistics." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.14.8" } }, "nbformat": 4, "nbformat_minor": 5 }