{ "cells": [ { "cell_type": "markdown", "id": "076ac5fe", "metadata": {}, "source": [ "# Gaussian input/output treatments" ] }, { "cell_type": "markdown", "id": "e8bc5006", "metadata": {}, "source": [ "## using pymatgen library\n", "\n", "Germain Salvato Vallverdu, March 23, 2023" ] }, { "cell_type": "markdown", "id": "59f79d0b", "metadata": {}, "source": [ "### import used modules and objects" ] }, { "cell_type": "markdown", "id": "18d39836", "metadata": {}, "source": [ "Load some pymatgen specific classes and module" ] }, { "cell_type": "code", "execution_count": 1, "id": "2a154e66-7a0a-4978-9168-63a7b6822783", "metadata": {}, "outputs": [], "source": [ "import pymatgen.core" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1cd1235-78af-4169-b64d-e8b2ccf93ccb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2023.3.23'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pymatgen.core.__version__" ] }, { "cell_type": "code", "execution_count": 3, "id": "47308b9c", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [], "source": [ "from pymatgen.core import Molecule, Site\n", "from pymatgen.io.gaussian import GaussianInput, GaussianOutput" ] }, { "cell_type": "markdown", "id": "7bb769b0", "metadata": {}, "source": [ "Image class of IPython for rendering pictures." ] }, { "cell_type": "code", "execution_count": 4, "id": "aa8f6efb", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [], "source": [ "from IPython.display import Image" ] }, { "cell_type": "markdown", "id": "99daacc4", "metadata": {}, "source": [ "### Generate gaussian inputs" ] }, { "cell_type": "markdown", "id": "8fd26a91", "metadata": {}, "source": [ "Hereafter is the docstring of the ```GaussianInput``` class." ] }, { "cell_type": "code", "execution_count": 3, "id": "313007b3", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class GaussianInput in module pymatgen.io.gaussian:\n", "\n", "class GaussianInput(builtins.object)\n", " | GaussianInput(mol, charge=None, spin_multiplicity=None, title=None, functional='HF', basis_set='6-31G(d)', route_parameters=None, input_parameters=None, link0_parameters=None, dieze_tag='#P', gen_basis=None)\n", " | \n", " | An object representing a Gaussian input file.\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, mol, charge=None, spin_multiplicity=None, title=None, functional='HF', basis_set='6-31G(d)', route_parameters=None, input_parameters=None, link0_parameters=None, dieze_tag='#P', gen_basis=None)\n", " | Args:\n", " | mol: Input molecule. It can either be a Molecule object,\n", " | a string giving the geometry in a format supported by Gaussian,\n", " | or ``None``. If the molecule is ``None``, you will need to use\n", " | read it in from a checkpoint. Consider adding ``CHK`` to the\n", " | ``link0_parameters``.\n", " | charge: Charge of the molecule. If None, charge on molecule is used.\n", " | Defaults to None. This allows the input file to be set a\n", " | charge independently from the molecule itself.\n", " | If ``mol`` is not a Molecule object, then you must specify a charge.\n", " | spin_multiplicity: Spin multiplicity of molecule. Defaults to None,\n", " | which means that the spin multiplicity is set to 1 if the\n", " | molecule has no unpaired electrons and to 2 if there are\n", " | unpaired electrons. If ``mol`` is not a Molecule object, then you\n", " | must specify the multiplicity\n", " | title: Title for run. Defaults to formula of molecule if None.\n", " | functional: Functional for run.\n", " | basis_set: Basis set for run.\n", " | route_parameters: Additional route parameters as a dict. For example,\n", " | {'SP':\"\", \"SCF\":\"Tight\"}\n", " | input_parameters: Additional input parameters for run as a dict. Used\n", " | for example, in PCM calculations. E.g., {\"EPS\":12}\n", " | link0_parameters: Link0 parameters as a dict. E.g., {\"%mem\": \"1000MW\"}\n", " | dieze_tag: # preceding the route line. E.g. \"#p\"\n", " | gen_basis: allows a user-specified basis set to be used in a Gaussian\n", " | calculation. If this is not None, the attribute ``basis_set`` will\n", " | be set to \"Gen\".\n", " | \n", " | __str__(self)\n", " | Return str(self).\n", " | \n", " | as_dict(self)\n", " | :return: MSONable dict\n", " | \n", " | get_cart_coords(self)\n", " | Return the Cartesian coordinates of the molecule\n", " | \n", " | get_zmatrix(self)\n", " | Returns a z-matrix representation of the molecule.\n", " | \n", " | to_string(self, cart_coords=False)\n", " | Return GaussianInput string\n", " | \n", " | Option: when cart_coords is set to True return the Cartesian coordinates\n", " | instead of the z-matrix\n", " | \n", " | write_file(self, filename, cart_coords=False)\n", " | Write the input string into a file\n", " | \n", " | Option: see __str__ method\n", " | \n", " | ----------------------------------------------------------------------\n", " | Class methods defined here:\n", " | \n", " | from_dict(d) from builtins.type\n", " | :param d: dict\n", " | :return: GaussianInput\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | from_file(filename)\n", " | Creates GaussianInput from a file.\n", " | \n", " | Args:\n", " | filename: Gaussian input filename\n", " | \n", " | Returns:\n", " | GaussianInput object\n", " | \n", " | from_string(contents)\n", " | Creates GaussianInput from a string.\n", " | \n", " | Args:\n", " | contents: String representing an Gaussian input file.\n", " | \n", " | Returns:\n", " | GaussianInput object\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties defined here:\n", " | \n", " | molecule\n", " | Returns molecule associated with this GaussianInput.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", "\n" ] } ], "source": [ "help(GaussianInput)" ] }, { "cell_type": "markdown", "id": "342e0a80", "metadata": {}, "source": [ "#### Examples" ] }, { "cell_type": "markdown", "id": "0d43b754", "metadata": {}, "source": [ "In this example, we get a $PO_2N^{2-}$ molecule and look for the best place for two Li atoms in the plane of the molecule. This example shows how to set up a Gaussian input file for all possible combinations. Coordinates of this molecule are available in xyz format :" ] }, { "cell_type": "code", "execution_count": 5, "id": "95b007f4", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "P1 N1 O2\n", "P -2.714301 0.000003 0.056765\n", "O -1.926896 -1.284476 0.128633\n", "O -1.925428 1.283516 0.129485\n", "N -4.181288 0.000879 -0.079861\n" ] }, { "data": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mol = Molecule.from_file(\"data_nglview/PO2N.xyz\")\n", "print(mol.to(fmt=\"xyz\"))\n", "Image(\"data_nglview/PO2N.png\")" ] }, { "cell_type": "markdown", "id": "627f07be", "metadata": {}, "source": [ "First we set up the three guessed positions for Li atoms in the plane of the molecule. Each position is along the bissector of two PO or PN bonds. We build a list of ```pymatgen.Site``` object which represent an atom with its symbol, coordinates ...." ] }, { "cell_type": "code", "execution_count": 6, "id": "04da272c", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[Site: Li (-0.4240, -0.0010, 0.2670), Site: Li (-3.7890, -2.0310, -0.0460), Site: Li (-3.7870, 2.0320, -0.0440)]\n" ] } ], "source": [ "posLi = [Site(\"Li\", [-0.424, -0.001, 0.267]),\n", " Site(\"Li\", [-3.789, -2.031, -0.046]),\n", " Site(\"Li\", [-3.787, 2.032, -0.044])]\n", "print(posLi)" ] }, { "cell_type": "markdown", "id": "231d457e", "metadata": {}, "source": [ "Now for each combinaison of two Li atoms among three possibilities we will write a gaussian input file. Look at the ```combinations``` method of ```itertools``` module." ] }, { "cell_type": "code", "execution_count": 7, "id": "76fb672e-9462-481c-a91f-b8846b4150a1", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [], "source": [ "from itertools import combinations" ] }, { "cell_type": "code", "execution_count": 8, "id": "2459a3ca-7e9e-4d89-a64c-7fe5f30d2114", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(Site: Li (-0.4240, -0.0010, 0.2670), Site: Li (-3.7890, -2.0310, -0.0460)) \t indices : [0, 1]\n", "(Site: Li (-0.4240, -0.0010, 0.2670), Site: Li (-3.7870, 2.0320, -0.0440)) \t indices : [0, 2]\n", "(Site: Li (-3.7890, -2.0310, -0.0460), Site: Li (-3.7870, 2.0320, -0.0440)) \t indices : [1, 2]\n" ] } ], "source": [ "for sites in combinations(posLi, 2):\n", " print(sites, \"\\t indices : \", [posLi.index(site) for site in sites])" ] }, { "cell_type": "markdown", "id": "d6c17441", "metadata": {}, "source": [ "Hereafter is the loop over combinations which will write all input files." ] }, { "cell_type": "code", "execution_count": 9, "id": "a31d5a6f", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "combination_01\n", "combination_02\n", "combination_12\n" ] } ], "source": [ "# Gaussian keywords\n", "route_parms = {\"opt\": \"tight\", \"Freq\": \"\"}\n", "link0 = {\"%Nproc\": \"5\"}\n", "DFT = \"B3LYP\"\n", "\n", "# the loop\n", "for sites in combinations(posLi, 2):\n", " # load the molecule\n", " mol = Molecule.from_file(\"data_nglview/PO2N.xyz\")\n", " \n", " # set up a label according to the combination\n", " title = \"combination_\" + \"\".join([str(posLi.index(site)) for site in sites])\n", " print(title)\n", " \n", " # add the two Li atoms to the molecule\n", " for site in sites:\n", " mol.append(site.specie, site.coords)\n", " \n", " # set up the calculation\n", " gau = GaussianInput(mol, charge=0, spin_multiplicity=1, title=title, \n", " functional=DFT, route_parameters=route_parms, link0_parameters=link0)\n", " gau.write_file(title + \".com\", cart_coords=True)" ] }, { "cell_type": "markdown", "id": "80688467", "metadata": {}, "source": [ "We can also load an input file and get some information." ] }, { "cell_type": "code", "execution_count": 10, "id": "baecb0d1", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6-31G(d) B3LYP\n" ] } ], "source": [ "gau = GaussianInput.from_file(\"combination_01.com\")\n", "print(gau.basis_set, gau.functional)" ] }, { "cell_type": "code", "execution_count": 11, "id": "c40eb356", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "%Nproc=5\n", "#P B3LYP/6-31G(d) Freq opt=tight\n", "\n", "combination_01\n", "\n", "0 1\n", "P -2.714301 0.000003 0.056765\n", "O -1.926896 -1.284476 0.128633\n", "O -1.925428 1.283516 0.129485\n", "N -4.181288 0.000879 -0.079861\n", "Li -0.424000 -0.001000 0.267000\n", "Li -3.789000 -2.031000 -0.046000\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(gau.to_string(cart_coords=True))" ] }, { "cell_type": "markdown", "id": "d1b6d5f0", "metadata": {}, "source": [ "### Post-treatments of Gaussian outputs" ] }, { "cell_type": "markdown", "id": "8a940998", "metadata": {}, "source": [ "#### Docstring of the ```GaussianOutput``` class" ] }, { "cell_type": "code", "execution_count": 11, "id": "4bfe34c5", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class GaussianOutput in module pymatgen.io.gaussian:\n", "\n", "class GaussianOutput(builtins.object)\n", " | GaussianOutput(filename)\n", " | \n", " | Parser for Gaussian output files.\n", " | \n", " | .. note::\n", " | \n", " | Still in early beta.\n", " | \n", " | Attributes:\n", " | .. attribute:: structures\n", " | \n", " | All structures from the calculation in the standard orientation. If the\n", " | symmetry is not considered, the standard orientation is not printed out\n", " | and the input orientation is used instead. Check the `standard_orientation`\n", " | attribute.\n", " | \n", " | .. attribute:: structures_input_orientation\n", " | \n", " | All structures from the calculation in the input orientation or the\n", " | Z-matrix orientation (if an opt=z-matrix was requested).\n", " | \n", " | .. attribute:: opt_structures\n", " | \n", " | All optimized structures from the calculation in the standard orientation,\n", " | if the attribute 'standard_orientation' is True, otherwise in the input\n", " | or the Z-matrix orientation.\n", " | \n", " | .. attribute:: energies\n", " | \n", " | All energies from the calculation.\n", " | \n", " | .. attribute:: eigenvalues\n", " | \n", " | List of eigenvalues for the last geometry\n", " | \n", " | .. attribute:: MO_coefficients\n", " | \n", " | Matrix of MO coefficients for the last geometry\n", " | \n", " | .. attribute:: cart_forces\n", " | \n", " | All Cartesian forces from the calculation.\n", " | \n", " | .. attribute:: frequencies\n", " | \n", " | A list for each freq calculation and for each mode of a dict with\n", " | {\n", " | \"frequency\": freq in cm-1,\n", " | \"symmetry\": symmetry tag\n", " | \"r_mass\": Reduce mass,\n", " | \"f_constant\": force constant,\n", " | \"IR_intensity\": IR Intensity,\n", " | \"mode\": normal mode\n", " | }\n", " | \n", " | The normal mode is a 1D vector of dx, dy dz of each atom.\n", " | \n", " | .. attribute:: hessian\n", " | \n", " | Matrix of second derivatives of the energy with respect to cartesian\n", " | coordinates in the **input orientation** frame. Need #P in the\n", " | route section in order to be in the output.\n", " | \n", " | .. attribute:: properly_terminated\n", " | \n", " | True if run has properly terminated\n", " | \n", " | .. attribute:: is_pcm\n", " | \n", " | True if run is a PCM run.\n", " | \n", " | .. attribute:: is_spin\n", " | \n", " | True if it is an unrestricted run\n", " | \n", " | .. attribute:: stationary_type\n", " | \n", " | If it is a relaxation run, indicates whether it is a minimum (Minimum)\n", " | or a saddle point (\"Saddle\").\n", " | \n", " | .. attribute:: corrections\n", " | \n", " | Thermochemical corrections if this run is a Freq run as a dict. Keys\n", " | are \"Zero-point\", \"Thermal\", \"Enthalpy\" and \"Gibbs Free Energy\"\n", " | \n", " | .. attribute:: functional\n", " | \n", " | Functional used in the run.\n", " | \n", " | .. attribute:: basis_set\n", " | \n", " | Basis set used in the run\n", " | \n", " | .. attribute:: route\n", " | \n", " | Additional route parameters as a dict. For example,\n", " | {'SP':\"\", \"SCF\":\"Tight\"}\n", " | \n", " | .. attribute:: dieze_tag\n", " | \n", " | # preceding the route line, e.g. \"#P\"\n", " | \n", " | .. attribute:: link0\n", " | \n", " | Link0 parameters as a dict. E.g., {\"%mem\": \"1000MW\"}\n", " | \n", " | .. attribute:: charge\n", " | \n", " | Charge for structure\n", " | \n", " | .. attribute:: spin_multiplicity\n", " | \n", " | Spin multiplicity for structure\n", " | \n", " | .. attribute:: num_basis_func\n", " | \n", " | Number of basis functions in the run.\n", " | \n", " | .. attribute:: electrons\n", " | \n", " | number of alpha and beta electrons as (N alpha, N beta)\n", " | \n", " | .. attribute:: pcm\n", " | \n", " | PCM parameters and output if available.\n", " | \n", " | .. attribute:: errors\n", " | \n", " | error if not properly terminated (list to be completed in error_defs)\n", " | \n", " | .. attribute:: Mulliken_charges\n", " | \n", " | Mulliken atomic charges\n", " | \n", " | .. attribute:: eigenvectors\n", " | \n", " | Matrix of shape (num_basis_func, num_basis_func). Each column is an\n", " | eigenvectors and contains AO coefficients of an MO.\n", " | \n", " | eigenvectors[Spin] = mat(num_basis_func, num_basis_func)\n", " | \n", " | .. attribute:: molecular_orbital\n", " | \n", " | MO development coefficients on AO in a more convenient array dict\n", " | for each atom and basis set label.\n", " | \n", " | mo[Spin][OM j][atom i] = {AO_k: coeff, AO_k: coeff ... }\n", " | \n", " | .. attribute:: atom_basis_labels\n", " | \n", " | Labels of AO for each atoms. These labels are those used in the output\n", " | of molecular orbital coefficients (POP=Full) and in the\n", " | molecular_orbital array dict.\n", " | \n", " | atom_basis_labels[iatom] = [AO_k, AO_k, ...]\n", " | \n", " | .. attribute:: resumes\n", " | \n", " | List of gaussian data resume given at the end of the output file before\n", " | the quotation. The resumes are given as string.\n", " | \n", " | .. attribute:: title\n", " | \n", " | Title of the gaussian run.\n", " | \n", " | .. attribute:: standard_orientation\n", " | \n", " | If True, the geometries stored in the structures are in the standard\n", " | orientation. Else, the geometries are in the input orientation.\n", " | \n", " | .. attribute:: bond_orders\n", " | \n", " | Dict of bond order values read in the output file such as:\n", " | {(0, 1): 0.8709, (1, 6): 1.234, ...}\n", " | \n", " | The keys are the atom indexes and the values are the Wiberg bond indexes\n", " | that are printed using `pop=NBOREAD` and `$nbo bndidx $end`.\n", " | \n", " | Methods:\n", " | .. method:: to_input()\n", " | \n", " | Return a GaussianInput object using the last geometry and the same\n", " | calculation parameters.\n", " | \n", " | .. method:: read_scan()\n", " | \n", " | Read a potential energy surface from a gaussian scan calculation.\n", " | \n", " | .. method:: get_scan_plot()\n", " | \n", " | Get a matplotlib plot of the potential energy surface\n", " | \n", " | .. method:: save_scan_plot()\n", " | \n", " | Save a matplotlib plot of the potential energy surface to a file\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, filename)\n", " | Args:\n", " | filename: Filename of Gaussian output file.\n", " | \n", " | as_dict(self)\n", " | JSON-serializable dict representation.\n", " | \n", " | get_scan_plot(self, coords=None)\n", " | Get a matplotlib plot of the potential energy surface.\n", " | \n", " | Args:\n", " | coords: internal coordinate name to use as abscissa.\n", " | \n", " | get_spectre_plot(self, sigma=0.05, step=0.01)\n", " | Get a matplotlib plot of the UV-visible xas. Transitions are plotted\n", " | as vertical lines and as a sum of normal functions with sigma with. The\n", " | broadening is applied in energy and the xas is plotted as a function\n", " | of the wavelength.\n", " | \n", " | Args:\n", " | sigma: Full width at half maximum in eV for normal functions.\n", " | step: bin interval in eV\n", " | \n", " | Returns:\n", " | A dict: {\"energies\": values, \"lambda\": values, \"xas\": values}\n", " | where values are lists of abscissa (energies, lamba) and\n", " | the sum of gaussian functions (xas).\n", " | A matplotlib plot.\n", " | \n", " | read_excitation_energies(self)\n", " | Read a excitation energies after a TD-DFT calculation.\n", " | \n", " | Returns:\n", " | A list: A list of tuple for each transition such as\n", " | [(energie (eV), lambda (nm), oscillatory strength), ... ]\n", " | \n", " | read_scan(self)\n", " | Read a potential energy surface from a gaussian scan calculation.\n", " | \n", " | Returns:\n", " | A dict: {\"energies\": [ values ],\n", " | \"coords\": {\"d1\": [ values ], \"A2\", [ values ], ... }}\n", " | \n", " | \"energies\" are the energies of all points of the potential energy\n", " | surface. \"coords\" are the internal coordinates used to compute the\n", " | potential energy surface and the internal coordinates optimized,\n", " | labelled by their name as defined in the calculation.\n", " | \n", " | save_scan_plot(self, filename='scan.pdf', img_format='pdf', coords=None)\n", " | Save matplotlib plot of the potential energy surface to a file.\n", " | \n", " | Args:\n", " | filename: Filename to write to.\n", " | img_format: Image format to use. Defaults to EPS.\n", " | coords: internal coordinate name to use as abcissa.\n", " | \n", " | save_spectre_plot(self, filename='spectre.pdf', img_format='pdf', sigma=0.05, step=0.01)\n", " | Save matplotlib plot of the spectre to a file.\n", " | \n", " | Args:\n", " | filename: Filename to write to.\n", " | img_format: Image format to use. Defaults to EPS.\n", " | sigma: Full width at half maximum in eV for normal functions.\n", " | step: bin interval in eV\n", " | \n", " | to_input(self, mol=None, charge=None, spin_multiplicity=None, title=None, functional=None, basis_set=None, route_parameters=None, input_parameters=None, link0_parameters=None, dieze_tag=None, cart_coords=False)\n", " | Create a new input object using by default the last geometry read in\n", " | the output file and with the same calculation parameters. Arguments\n", " | are the same as GaussianInput class.\n", " | \n", " | Returns:\n", " | gaunip (GaussianInput) : the gaussian input object\n", " | \n", " | ----------------------------------------------------------------------\n", " | Readonly properties defined here:\n", " | \n", " | final_energy\n", " | :return: Final energy in Gaussian output.\n", " | \n", " | final_structure\n", " | :return: Final structure in Gaussian output.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", "\n" ] } ], "source": [ "help(GaussianOutput)" ] }, { "cell_type": "markdown", "id": "3e300b31", "metadata": {}, "source": [ "#### Examples :" ] }, { "cell_type": "markdown", "id": "bb32178a", "metadata": {}, "source": [ "Used the ```GaussianOutput``` class to read a Gaussian output file." ] }, { "cell_type": "code", "execution_count": 12, "id": "cf650a50", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [], "source": [ "logfile = GaussianOutput(\"data_nglview/config_0123.log\")" ] }, { "cell_type": "markdown", "id": "5cc3350c", "metadata": {}, "source": [ "Is termination ```Normal``` ?" ] }, { "cell_type": "code", "execution_count": 13, "id": "73b4a0a3", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logfile.properly_terminated" ] }, { "cell_type": "markdown", "id": "cdae6ec1", "metadata": {}, "source": [ "Display final energy or mulliken charges :" ] }, { "cell_type": "code", "execution_count": 14, "id": "a1006100", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "-1239.01264675" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logfile.final_energy" ] }, { "cell_type": "code", "execution_count": 15, "id": "e1d0fd3c", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{1: ['P', 1.751758],\n", " 2: ['P', 1.203271],\n", " 3: ['O', -0.722993],\n", " 4: ['O', -0.771242],\n", " 5: ['O', -0.804145],\n", " 6: ['O', -0.771148],\n", " 7: ['O', -0.633752],\n", " 8: ['O', -0.633729],\n", " 9: ['O', -0.519418],\n", " 10: ['Li', 0.528585],\n", " 11: ['Li', 0.528596],\n", " 12: ['Li', 0.421974],\n", " 13: ['Li', 0.422242]}" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "logfile.Mulliken_charges" ] }, { "cell_type": "markdown", "id": "dec19bc0", "metadata": {}, "source": [ "You can extract the coordinates of the final structure. All structures are ```pymatgen.Molecule``` objects, see [here](http://pymatgen.org/_static/Molecule.html) or [the reference guide](http://pymatgen.org/pymatgen.core.html#pymatgen.core.structure.Molecule). You can do lot of stuff with this object such as : neighbors list, compute distances or the distance matrix, symmetry operation, atomic substitution ..." ] }, { "cell_type": "code", "execution_count": 16, "id": "a2414705", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n", "Li4 P2 O7\n", "P 1.803152 -0.000062 -0.074603\n", "P -2.714301 0.000003 0.056765\n", "O 0.711492 0.000020 -1.166577\n", "O 1.626159 1.347642 0.712312\n", "O 3.338973 -0.000234 -0.476901\n", "O 1.625851 -1.347548 0.712612\n", "O -1.926896 -1.284476 0.128633\n", "O -1.925428 1.283516 0.129485\n", "O -4.181288 0.000879 -0.079861\n", "Li 3.387513 1.698635 0.310029\n", "Li 3.387174 -1.698958 0.310641\n", "Li -0.135176 -1.487921 -0.211882\n", "Li -0.134065 1.489076 -0.212137\n" ] } ], "source": [ "print(logfile.final_structure.to(fmt=\"xyz\"))" ] }, { "cell_type": "markdown", "id": "aacd5ea5", "metadata": {}, "source": [ "You would prefer a gaussian input format with a z-matrix of the 42th geometrical optimization step." ] }, { "cell_type": "code", "execution_count": 17, "id": "3ed215b6", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "#P HF/6-31G(d) \n", "\n", "Li4 P2 O7\n", "\n", "0 1\n", "P\n", "P 1 B1\n", "O 1 B2 2 A2\n", "O 1 B3 3 A3 2 D3\n", "O 1 B4 4 A4 3 D4\n", "O 1 B5 5 A5 3 D5\n", "O 2 B6 3 A6 6 D6\n", "O 2 B7 7 A7 3 D7\n", "O 2 B8 7 A8 8 D8\n", "Li 4 B9 5 A9 1 D9\n", "Li 6 B10 5 A10 1 D10\n", "Li 7 B11 3 A11 6 D11\n", "Li 8 B12 4 A12 3 D12\n", "\n", "B1=4.376288\n", "B2=1.543372\n", "A2=36.067295\n", "B3=1.572484\n", "A3=105.757762\n", "D3=-63.587256\n", "B4=1.587877\n", "A4=103.530048\n", "D4=127.302261\n", "B5=1.570339\n", "A5=103.613837\n", "D5=118.342854\n", "B6=1.509599\n", "A6=70.094274\n", "D6=-30.800762\n", "B7=1.509898\n", "A7=116.635764\n", "D7=-54.758827\n", "B8=1.474749\n", "A8=121.618553\n", "D8=-179.642556\n", "B9=1.844669\n", "A9=48.464768\n", "D9=-173.301681\n", "B10=1.844418\n", "A10=48.529403\n", "D10=176.422774\n", "B11=1.836204\n", "A11=36.882001\n", "D11=-26.625480\n", "B12=1.836768\n", "A12=14.624477\n", "D12=-64.000507\n", "\n", "\n", "\n", "\n" ] } ], "source": [ "print(logfile.structures[41].to(fmt=\"gjf\"))" ] }, { "cell_type": "markdown", "id": "d8a51e32", "metadata": {}, "source": [ "You can plot the geometrical convergence (here using plotly)." ] }, { "cell_type": "code", "execution_count": 18, "id": "944408ce", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [], "source": [ "import plotly.graph_objs as go" ] }, { "cell_type": "code", "execution_count": 19, "id": "73d107f0", "metadata": { "collapsed": false, "jupyter": { "outputs_hidden": false }, "tags": [] }, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure([\n", " go.Scatter(x=[i for i in range(len(logfile.energies))], y=logfile.energies)\n", "],\n", " layout=go.Layout(\n", " xaxis=dict(title=\"step\"), \n", " yaxis=dict(title=\"Energy (ua)\"),\n", " template=\"plotly_white\",\n", " height=500,\n", " )\n", ")\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 20, "id": "39ad364a-f158-439c-849f-ab3af8751894", "metadata": { "tags": [] }, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 21, "id": "cec59e9b-611d-4344-bb2b-15cd7b2ec2c0", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Energy (ua)')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(logfile.energies, \"o--\")\n", "plt.xlabel(\"step\")\n", "plt.ylabel(\"Energy (ua)\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }