{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Excel to Empirical\n", "In this example, we will read *ab-initio* data from an Excel spreadsheet and create a Nasa object from it. Even though we use a Nasa object here, the Shomate class also has the ability to be generated from a StatMech model.\n", "\n", "## Topics Covered\n", "- Reading *ab-initio* data from an Excel file\n", "- Initialize Reference objects and a References object\n", "- Generate a Nasa object using StatMech models\n", "- Write Nasa object to a thermdat file\n", "\n", "## Files Required\n", "- [references.xlsx](references.xlsx) (Excel spreadsheet containing *ab-initio* and experimental data for reference species)\n", "- [input.xlsx](input.xlsx) (Excel spreadsheet containing *ab-initio* data for the species you would like to generate Nasa objects\n", "- files containing atomic coordinates (files that can be read using ase.io.read. These files are only necessary if using rotational and translational modes (which tend to only be relevant for gas-phase species). For this example, we are using CONTCAR files. \n", "\n", "## Importing the *ab-initio* data from Excel\n", "First, we will import data from the ``input.xlsx`` spreadsheet. The contents of the spreadsheet are shown below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| name | phase | elements.C | elements.H | elements.O | statmech_model | potentialenergy | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber |\n", "|-----------|-------|------------|------------|------------|----------------|-----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|\n", "| H2_cus(S) | S | 0 | 2 | 0 | Harmonic | -6.98609748 | 3066.979319 | 1520.963162 | 846.589184 | 537.019612 | 469.648212 | 444.089371 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| H2Obr(S) | S | 0 | 2 | 1 | Harmonic | -14.813281 | 3732.4315 | 3615.316915 | 1533.760861 | 451.030069 | 429.209457 | 389.942902 | 204.875732 | 162.988907 | 113.146516 | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| H2Ocus(S) | S | 0 | 2 | 1 | Harmonic | -15.4204017 | 3732.4315 | 3615.316915 | 1533.760861 | 451.030069 | 429.209457 | 389.942902 | 204.875732 | 162.988907 | 113.146516 | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| H_cus(S) | S | 0 | 1 | 0 | Harmonic | -3.47020748 | 1868.8092 | 681.336865 | 578.20889 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| HObr(S) | S | 0 | 1 | 1 | Harmonic | -11.577085 | 3678.377186 | 851.655046 | 573.216546 | 418.298492 | 374.446393 | 204.720161 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| Obr(S) | S | 0 | 0 | 1 | Harmonic | -7.519858 | 537.116485 | 439.995941 | 256.38412 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| IPA(S) | S | 3 | 8 | 1 | Harmonic | -64.935392 | 3072.824921 | 3056.583116 | 3049.635786 | 3038.996872 | 3031.532711 | 3008.868219 | 2969.688478 | 2956.451871 | 1460.613697 | 1454.472854 | 1435.981201 | 1432.413496 | 1413.554005 | 1366.129117 | 1353.211574 | 1327.903925 | 1321.25272 | 1148.721745 | 1130.053472 | 1098.855825 | 933.64652 | 921.005746 | 903.030098 | 825.727381 | 809.440337 | 535.60253 | 414.794848 | 378.193102 | 295.278829 | 237.586784 | 230.560246 | 171.110789 | 129.36901 | 77.415076 | 64.350293 | 56.879635 |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To import the data, we use the ``pmutt.io.excel.read_excel`` function." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C:\\Users\\jonat\\Dropbox\\UDelDocuments\\UDelResearch\\Github\\pmutt_dev\\docs\\source\\examples_jupyter\\excel_to_empirical\n", "[{'elec_model': ,\n", " 'elements': {'C': 0, 'H': 2, 'O': 0},\n", " 'model': ,\n", " 'name': 'H2_cus(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -6.9860974799998985,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3066.979319,\n", " 1520.963162,\n", " 846.589184,\n", " 537.019612,\n", " 469.648212,\n", " 444.089371]},\n", " {'elec_model': ,\n", " 'elements': {'C': 0, 'H': 2, 'O': 1},\n", " 'model': ,\n", " 'name': 'H2Obr(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -14.81328099999996,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3732.4315,\n", " 3615.316915,\n", " 1533.760861,\n", " 451.030069,\n", " 429.209457,\n", " 389.942902,\n", " 204.875732,\n", " 162.988907,\n", " 113.146516]},\n", " {'elec_model': ,\n", " 'elements': {'C': 0, 'H': 2, 'O': 1},\n", " 'model': ,\n", " 'name': 'H2Ocus(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -15.420401699999957,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3732.4315,\n", " 3615.316915,\n", " 1533.760861,\n", " 451.030069,\n", " 429.209457,\n", " 389.942902,\n", " 204.875732,\n", " 162.988907,\n", " 113.146516]},\n", " {'elec_model': ,\n", " 'elements': {'C': 0, 'H': 1, 'O': 0},\n", " 'model': ,\n", " 'name': 'H_cus(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -3.470207479999999,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [1868.8092, 681.336865, 578.20889]},\n", " {'elec_model': ,\n", " 'elements': {'C': 0, 'H': 1, 'O': 1},\n", " 'model': ,\n", " 'name': 'HObr(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -11.577084999999897,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3678.377186,\n", " 851.655046,\n", " 573.216546,\n", " 418.298492,\n", " 374.446393,\n", " 204.720161]},\n", " {'elec_model': ,\n", " 'elements': {'C': 0, 'H': 0, 'O': 1},\n", " 'model': ,\n", " 'name': 'Obr(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -7.519857999999886,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [537.116485, 439.995941, 256.38412]},\n", " {'elec_model': ,\n", " 'elements': {'C': 3, 'H': 8, 'O': 1},\n", " 'model': ,\n", " 'name': 'IPA(S)',\n", " 'phase': 'S',\n", " 'potentialenergy': -64.93539200000009,\n", " 'required': ('vib_wavenumbers', 'potentialenergy', 'spin'),\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3072.824921,\n", " 3056.583116,\n", " 3049.635786,\n", " 3038.996872,\n", " 3031.532711,\n", " 3008.868219,\n", " 2969.688478,\n", " 2956.451871,\n", " 1460.613697,\n", " 1454.472854,\n", " 1435.981201,\n", " 1432.413496,\n", " 1413.554005,\n", " 1366.129117,\n", " 1353.211574,\n", " 1327.903925,\n", " 1321.25272,\n", " 1148.721745,\n", " 1130.053472,\n", " 1098.855825,\n", " 933.64652,\n", " 921.005746,\n", " 903.030098,\n", " 825.727381,\n", " 809.440337,\n", " 535.60253,\n", " 414.794848,\n", " 378.193102,\n", " 295.278829,\n", " 237.586784,\n", " 230.560246,\n", " 171.110789,\n", " 129.36901,\n", " 77.415076,\n", " 64.350293,\n", " 56.879635]}]\n" ] } ], "source": [ "import os\n", "from pathlib import Path\n", "\n", "from pprint import pprint\n", "from pmutt.io.excel import read_excel\n", "\n", "try:\n", " notebook_path = os.path.dirname(__file__)\n", "except NameError:\n", " notebook_path = Path().resolve()\n", "\n", "print(os.getcwd())\n", "species_data = read_excel(io='./test/input.xlsx')\n", "pprint(species_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the output of ``read_excel`` is a list of dictionaries. Each dictionary in the list corresponds to the data to initialize the objects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Importing the *ab-initio* references data from Excel\n", "Before initializing the Nasa objects, we have to adjust the enthalpies from the DFT reference to the standard reference (i.e. pure species have an enthalpy of 0 at 298 K and 1 atm). The references.xlsx file contains the following information." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| name | phase | elements.C | elements.H | elements.O | statmech_model | T_ref | HoRT_ref | potentialenergy | atoms | symmetrynumber | spin | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber | vib_wavenumber |\n", "|------|-------|------------|------------|------------|----------------|-------|------------|-----------------|---------------|----------------|------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|\n", "| IPA | G | 3 | 8 | 1 | IdealGas | 298 | -110.1078 | -63.6353 | ./CONTCAR_IPA | 1 | 0 | 3695.6947 | 3058.1512 | 3056.3031 | 3037.6154 | 3027.4596 | 2977.8791 | 2962.0141 | 2959.4138 | 1455.9381 | 1446.9241 | 1432.5845 | 1430.694 | 1365.4299 | 1359.4158 | 1345.7613 | 1312.957 | 1258.3806 | 1144.4464 | 1121.8044 | 1043.2856 | 936.5137 | 910.6527 | 894.8818 | 802.0075 | 459.1458 | 414.8802 | 355.2703 | 261.3417 | 250.5753 | 219.2591 |\n", "| H2O | G | 0 | 2 | 1 | IdealGas | 298 | -97.606043 | -14.2209 | ./CONTCAR_H2O | 2 | 0 | 3825.434 | 3710.2642 | 1582.432 | | | | | | | | | | | | | | | | | | | | | | | | | | | |\n", "| H2 | G | 0 | 2 | 0 | IdealGas | 298 | 0 | -6.7598 | ./CONTCAR_H2 | 2 | 0 | 4306.1793 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that these species contain extra fields, such as ``atoms``, ``symmetrynumber``, and ``spin`` since the ``IdealGas`` statistical mechanical model will be used. The ``T_ref`` and ``HoRT_ref`` are extra fields that are necessary for reference species." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'HoRT_ref': -110.10779908717083,\n", " 'T_ref': 298,\n", " 'atoms': Atoms(symbols='H6C5O2', pbc=True, cell=[12.843615942469413, 9.254207587944354, 28.20143514534594]),\n", " 'elec_model': ,\n", " 'elements': {'C': 3, 'H': 8, 'O': 1},\n", " 'model': ,\n", " 'n_degrees': 3,\n", " 'name': 'IPA',\n", " 'optional': 'atoms',\n", " 'phase': 'G',\n", " 'potentialenergy': -63.6353,\n", " 'required': ('molecular_weight',\n", " 'vib_wavenumbers',\n", " 'potentialenergy',\n", " 'spin',\n", " 'geometry',\n", " 'rot_temperatures',\n", " 'symmetrynumber'),\n", " 'rot_model': ,\n", " 'spin': 0,\n", " 'symmetrynumber': 1,\n", " 'trans_model': ,\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3695.6947,\n", " 3058.1512,\n", " 3056.3031,\n", " 3037.6154,\n", " 3027.4596,\n", " 2977.8791,\n", " 2962.0141,\n", " 2959.4138,\n", " 1455.9381,\n", " 1446.9241,\n", " 1432.5845,\n", " 1430.694,\n", " 1365.4299,\n", " 1359.4158,\n", " 1345.7613,\n", " 1312.957,\n", " 1258.3806,\n", " 1144.4464,\n", " 1121.8044,\n", " 1043.2856,\n", " 936.5137,\n", " 910.6527,\n", " 894.8818,\n", " 802.0075,\n", " 459.1458,\n", " 414.8802,\n", " 355.2703,\n", " 261.3417,\n", " 250.5753,\n", " 219.2591]},\n", " {'HoRT_ref': -97.60604333597571,\n", " 'T_ref': 298,\n", " 'atoms': Atoms(symbols='OH2', pbc=True, cell=[20.0, 21.526478, 20.596309]),\n", " 'elec_model': ,\n", " 'elements': {'C': 0, 'H': 2, 'O': 1},\n", " 'model': ,\n", " 'n_degrees': 3,\n", " 'name': 'H2O',\n", " 'optional': 'atoms',\n", " 'phase': 'G',\n", " 'potentialenergy': -14.2209,\n", " 'required': ('molecular_weight',\n", " 'vib_wavenumbers',\n", " 'potentialenergy',\n", " 'spin',\n", " 'geometry',\n", " 'rot_temperatures',\n", " 'symmetrynumber'),\n", " 'rot_model': ,\n", " 'spin': 0,\n", " 'symmetrynumber': 2,\n", " 'trans_model': ,\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [3825.434, 3710.2642, 1582.432]},\n", " {'HoRT_ref': 0.0,\n", " 'T_ref': 298,\n", " 'atoms': Atoms(symbols='H2', pbc=True, cell=[20.0, 20.0, 20.737166]),\n", " 'elec_model': ,\n", " 'elements': {'C': 0, 'H': 2, 'O': 0},\n", " 'model': ,\n", " 'n_degrees': 3,\n", " 'name': 'H2',\n", " 'optional': 'atoms',\n", " 'phase': 'G',\n", " 'potentialenergy': -6.7598,\n", " 'required': ('molecular_weight',\n", " 'vib_wavenumbers',\n", " 'potentialenergy',\n", " 'spin',\n", " 'geometry',\n", " 'rot_temperatures',\n", " 'symmetrynumber'),\n", " 'rot_model': ,\n", " 'spin': 0,\n", " 'symmetrynumber': 2,\n", " 'trans_model': ,\n", " 'vib_model': ,\n", " 'vib_wavenumbers': [4306.1793]}]\n" ] } ], "source": [ "refs_input = read_excel(io='references.xlsx')\n", "pprint(refs_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've imported the data, we can make a ``References`` object, which is made of ``Reference`` objects using the following syntax." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'C': -357.259494801186, 'H': -124.67018779818417, 'O': -180.81926439919044}\n" ] } ], "source": [ "from pmutt.empirical.references import Reference, References\n", "\n", "refs = References(descriptor='elements', \n", " references=[Reference(**ref_input) \n", " for ref_input in refs_input])\n", "print(refs.offset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``References`` object calculated the offset between C, H, and O." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Initialize the Nasa objects\n", "The ``from_model`` method allows us to initialize the Nasa objects from the data." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "from pmutt.empirical.nasa import Nasa\n", "\n", "T_low = 300. # K\n", "T_high = 1100. # K\n", "\n", "species = [Nasa.from_model(references=refs, T_low=T_low, T_high=T_high, \n", " **specie_data) for specie_data in species_data]\n", "# Printing an example of a Nasa species\n", "print(species[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing Nasa objects to a Thermdat file" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "THERMO ALL\n", " 100 500 1500\n", "H2_cus(S) 20200130H 2 S300.0 1100.0 610.2 1\n", "-1.32355622E-01 1.17210568E-02-1.21067695E-05 6.77414609E-09-1.56703390E-12 2\n", "-1.96688030E+03-1.13738972E+00-2.37252750E+00 2.60714871E-02-4.73583826E-05 3\n", " 4.61096590E-08-1.83624246E-11-1.68100073E+03 8.63988878E+00 4\n", "H2Obr(S) 20200130H 2O 1 S300.0 1100.0 561.2 1\n", " 3.33914632E+00 8.65676156E-03-1.03441245E-05 7.11690970E-09-1.91112739E-12 2\n", "-3.65044468E+04-1.59546195E+01 3.68293150E-01 3.06585581E-02-7.30019576E-05 3\n", " 8.80731362E-08-4.17597362E-11-3.61742347E+04-3.41084606E+00 4\n", "H2Ocus(S) 20200130H 2O 1 S300.0 1100.0 561.2 1\n", " 3.33914632E+00 8.65676156E-03-1.03441245E-05 7.11690970E-09-1.91112739E-12 2\n", "-4.35497924E+04-1.59546195E+01 3.68293150E-01 3.06585581E-02-7.30019576E-05 3\n", " 8.80731362E-08-4.17597362E-11-4.32195803E+04-3.41084606E+00 4\n", "H_cus(S) 20200130H 1 S300.0 1100.0 544.9 1\n", "-4.56626289E-01 6.52756961E-03-5.72385869E-06 2.56064809E-09-4.74217654E-13 2\n", "-8.85760036E+02 1.29271332E+00-1.70971795E+00 1.57880872E-02-3.17011214E-05 3\n", " 3.53093929E-08-1.61102148E-11-7.48349620E+02 6.57690129E+00 4\n", "HObr(S) 20200130H 1O 1 S300.0 1100.0 610.2 1\n", " 1.32317902E+00 1.07943705E-02-1.40435635E-05 9.13293283E-09-2.29632826E-12 2\n", "-3.92611148E+04-7.89390797E+00-1.90464381E+00 3.13120166E-02-6.40095247E-05 3\n", " 6.43701568E-08-2.56553082E-11-3.88464459E+04 6.21729957E+00 4\n", "Obr(S) 20200130O 1 S300.0 1100.0 561.2 1\n", " 1.10929486E+00 6.02227730E-03-8.09582034E-06 5.13595359E-09-1.26396875E-12 2\n", "-3.27378795E+04-6.27177767E+00-1.46028728E+00 2.43280424E-02-5.80735069E-05 3\n", " 6.69898412E-08-3.04663321E-11-3.24427306E+04 4.67078224E+00 4\n", "IPA(S) 20200130C 3H 8O 1 S300.0 1100.0 544.9 1\n", "-2.41027713E+00 6.03014873E-02-5.21384920E-05 2.61809899E-08-5.76980678E-12 2\n", "-4.86934052E+04 1.14552028E+01 6.36065045E+00-8.82654398E-03 1.52433654E-04 3\n", "-2.43446336E-07 1.27830717E-10-4.95836691E+04-2.49129983E+01 4\n", "END\n" ] } ], "source": [ "from pmutt.io.thermdat import write_thermdat\n", "\n", "# Writing thermdat to a file\n", "write_thermdat(nasa_species=species, filename='thermdat', write_date=True)\n", "\n", "# The thermdat contents can also be returned as a string by omitting filename\n", "thermdat_str = write_thermdat(nasa_species=species, write_date=True)\n", "print(thermdat_str)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking the fit of the Nasa object\n", "To see if the Nasa object's predictions of the thermodynamic data matches the statistical mechanical model inputted, use the ``plot_statmech_and_empirical`` method." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = species[0].plot_statmech_and_empirical(Cp_units='J/mol/K', H_units='kJ/mol',\n", " S_units='J/mol/K', G_units='kJ/mol')\n", "fig.set_size_inches((10, 8))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 }