{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Struct2XAS examples: from CIF/XYZ to XANES/EXAFS simulations with FDMNES and FEFF\n", "\n", "- Authors: Beatriz G. Foschiani and Mauro Rovezzi\n", "- Contact: mauro.rovezzi@esrf.fr\n", "- Last modified: September 2023\n", "\n", "Here we provide various examples on the usage of `larch.xrd.struct2xas` module." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import larch" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1: Zn K-edge XANES of wurtzite ZnO with FDMNES from a CIF file\n", "\n", "This example shows how to use the `Struct2XAS` class to convert a CIF file to a FDMNES XANES input. We will simulate Zn K-edge XAS of ZnO. \n", "\n", "- The input structure is taken from the Materials Project database: [mp-2133](https://legacy.materialsproject.org/materials/mp-2133/).\n", "- The experimental data are taken from the SSHADE/FAME database: [DOI:10.26302/SSHADE/EXPERIMENT_ST_20180418_001](https://www.sshade.eu/data/EXPERIMENT_ST_20180418_001)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Import the main class and instantiate it with the CIF file and the name of the absorbing element" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n" ] } ], "source": [ "from larch.xrd.struct2xas import Struct2XAS\n", "mat_obj = Struct2XAS(file = \"../structuredata/struct2xas/ZnO_mp-2133.cif\", abs_atom=\"Zn\")" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "to get the information about absorbing site" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idx_absspeciefrac_coordswyckoff_sitecart_coordsoccupancyidx_in_struct
0Zn[0.3333, 0.6667, 0.4995]2b[-1.6446 0.9495 -2.6505]10
\n" ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# information about absorbing site as pandas.DataFrame\n", "mat_obj.get_abs_sites_info()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "the same, more programmatically" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[0,\n", " 'Zn',\n", " [0.3333, 0.6667, 0.4995],\n", " '2b',\n", " array([-1.6446, 0.9495, -2.6505]),\n", " 1,\n", " 0]]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_obj.get_abs_sites()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Getters and setters are available for changing the absorber site being considered. We can use the methods `set_abs_site` and `get_abs_site`. This structure has only one absorber site (an example with multiple sites is given later)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "mat_obj.set_abs_site(0)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "to get information on the coordination environment around absorber atom" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coord. Env. from absorber atom: Zn at site 0\n", "['T:4', 'T:5']\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ElementDistance
0(O)2.00418
1(O)2.00425
2(O)2.00426
3(O)2.01242
4(O)3.29440
\n", "
" ], "text/plain": [ " Element Distance\n", "0 (O) 2.00418\n", "1 (O) 2.00425\n", "2 (O) 2.00426\n", "3 (O) 2.01242\n", "4 (O) 3.29440" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_obj.get_coord_envs_info()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "or programmatically" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['Coord. Env. for Site 0',\n", " [{'ce_symbol': 'T:4',\n", " 'ce_fraction': 0.8596848561093451,\n", " 'csm': 0.00569144245172932,\n", " 'permutation': [0, 1, 2, 3]},\n", " {'ce_symbol': 'T:5',\n", " 'ce_fraction': 0.1403151438906549,\n", " 'csm': 4.062381866591053,\n", " 'permutation': [0, 1, 4, 2, 3]}],\n", " [{'site': PeriodicNeighbor: O3 (O) (-3.289, 1.899, -3.291) [0.6667, 1.333, 0.6202],\n", " 'index': 3,\n", " 'image_cell': array([0, 1, 0])},\n", " {'site': PeriodicNeighbor: O3 (O) (-6.423e-07, 1.899, -3.291) [-0.3333, 0.3333, 0.6202],\n", " 'index': 3,\n", " 'image_cell': array([-1, 0, 0])},\n", " {'site': PeriodicNeighbor: O2 (O) (-1.645, 0.9495, -0.6381) [0.3333, 0.6667, 0.1202],\n", " 'index': 2,\n", " 'image_cell': array([0, 0, 0])},\n", " {'site': PeriodicNeighbor: O3 (O) (-1.645, -0.9495, -3.291) [0.6667, 0.3333, 0.6202],\n", " 'index': 3,\n", " 'image_cell': array([0, 0, 0])},\n", " {'site': PeriodicNeighbor: O2 (O) (-1.645, 0.9495, -5.945) [0.3333, 0.6667, 1.12],\n", " 'index': 2,\n", " 'image_cell': array([0, 0, 1])}]]]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_obj.get_coord_envs()\n" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "It is possible to visualize the local environment at a ginen radius from the absorber" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : {'Zn': 'red', 'O': 'green'}\n" ] } ], "source": [ "mat_obj.visualize(radius=2.5)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To create the FDMNES input file using the default template in a temporary directory" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-2133/Zn/frame0/site0/job_inp.txt\n" ] } ], "source": [ "mytemplate = None #: uses default FDMNES XANES template (-> `larch/xrd/templates/fdmnes.tmpl`)\n", "mypath = None #: creates a default structure -> \"mydir/fdmnes/input_structure/abs_atom/siteN/\"\n", "mat_obj.make_input_fdmnes(radius=7, green=False, template=mytemplate, parent_path=mypath)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "To show the created input file" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "! FDMNES input file generated by Struct2XAS\n", "\n", "header\n", "\n", "comment\n", "cif file name: ZnO_mp-2133\n", "creation date:2023-09-30_1618\n", "\n", "filout\n", "\n", "job\n", "\n", "range\n", "-30.0 0.1 70.0 1.0 100 \n", "\n", "radius\n", "7\n", "\n", "quadrupole\n", "\n", "density\n", "\n", "SCF\n", "\n", "\n", "\n", "absorber\n", "1\n", "\n", "spgroup\n", "186\n", "\n", "\n", "\n", "crystal\n", "3.28910248 3.28910248 5.30682100 90.00000000 90.00000000 120.00000000\n", "\n", "30 0.33333333 0.66666667 0.49945200 Zn\n", " 8 0.33333333 0.66666667 0.12023800 O\n", "\n", "convolution\n", "\n", "end\n" ] } ], "source": [ "! cat {mat_obj.outdir}/job_inp.txt" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Run FDMNES (refer to FDMNES documentation if needed)\n", "\n", "```python\n", "#Example how to run FDMNES at the ESRF via SLURM \n", "input = mat_ojb.parent_path\n", "! cd {input}; subfdmnes -c 30\n", "```\n", "\n", "The output files created by FDMNES will be:\n", "- `job.txt`: non-convoluted spectra\n", "- `job_conv.txt`: convoluted spectra (by fdmnes with default parameters)\n", "- `job_out.txt`: FDMNES output during the execution of the program (NOTE: this is created by the SLURM submission script)\n", "- `job_sd0.txt`: file with the projected density of states information\n", "- `job_bav.txt`: file with large simulation infos (better to compress it or remove)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Please, refer to [XANES_Convolution.ipynb](./XANES_Convolution.ipynb) for a comparison of the simulated data with experimental one." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Example 1a: Zn K-edge EXAFS with FEFF\n", "\n", "We can also simulate the EXAFS spectrum with FEFF" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/ZnO_mp-2133/Zn/frame0/site0/feff.inp\n" ] } ], "source": [ "mytemplate = None #: uses default FEFF EXAFS template (-> `larch/xrd/templates/feff_exafs.tmpl`)\n", "mypath = mat_obj.parent_path #: if None creates a default structure -> \"mydir/feff/input_structure/abs_atom/frameN/siteN/\"\n", "mat_obj.make_input_feff(radius=7, template=mytemplate, parent_path=mypath)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "to show the input file" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "* FEFF8* input file generated by Struct2XAS\n", "\n", "TITLE ZnO_mp-2133\n", "TITLE 2023-09-30_1618\n", "TITLE site 0\n", "\n", "* edge energy = eV\n", "EDGE K\n", "S02 0.0\n", "\n", "* pot xsph fms paths genfmt ff2chi\n", "CONTROL 1 1 1 1 1 1\n", "PRINT 1 0 0 0 0 0\n", "\n", "*** ixc=0 means to use Hedin-Lundqvist\n", "* ixc [ Vr Vi ]\n", "EXCHANGE 0\n", "\n", "* r_scf [ l_scf n_scf ca ]\n", "*SCF 5.0\n", "\n", "RPATH 7\n", "EXAFS 20\n", "NLEG 6\n", "\n", "* EXAFS damping, to mimic the structural disorder\n", "* SIG2 0.005\n", "\n", "* Debye-Waller factors\n", "* DEBYE 0 0\n", "\n", "* emin emax eimag\n", "*LDOS -30 20 0.1\n", "\n", "\n", "POTENTIALS\n", "\n", "* ipot Z tag [lmax1 lmax2 xnatph sphinph]\n", " 0 30 Zn(abs)\n", " 2 8 O\n", " 1 30 Zn \n", "\n", "ATOMS\n", "\n", "* x y z ipot tag distance occupancy\n", " 0.000048 -0.000018 -0.000002 0 Zn(abs) 0.00000 *1 \n", " -1.644504 0.949464 -0.640992 2 O 2.00418 *1 \n", " 0.000049 -1.898982 -0.640992 2 O 2.00425 *1 \n", " 1.644599 0.949464 -0.640992 2 O 2.00426 *1 \n", " 0.000048 -0.000018 2.012418 2 O 2.01242 *1 \n", " -1.644504 0.949464 -2.653413 1 Zn 3.26289 *1 \n", " -1.644504 0.949464 2.653408 1 Zn 3.26289 *1 \n", " 0.000049 -1.898982 -2.653413 1 Zn 3.26293 *1 \n", " 0.000049 -1.898982 2.653408 1 Zn 3.26293 *1 \n", " 1.644599 0.949464 -2.653413 1 Zn 3.26294 *1 \n", " 1.644599 0.949464 2.653408 1 Zn 3.26294 *1 \n", " -3.289055 -0.000018 -0.000002 1 Zn 3.28905 *1 \n", " -1.644504 2.848428 -0.000002 1 Zn 3.28906 *1 \n", " -1.644502 -2.848464 -0.000002 1 Zn 3.28909 *1 \n", " 1.644599 2.848428 -0.000002 1 Zn 3.28911 *1 \n", " 1.644601 -2.848464 -0.000002 1 Zn 3.28914 *1 \n", " 3.289151 -0.000018 -0.000002 1 Zn 3.28915 *1 \n", " 0.000048 -0.000018 -3.294403 2 O 3.29440 *1 \n", " -3.289054 -1.898982 -0.640992 2 O 3.85161 *1 \n", " 0.000047 3.797910 -0.640992 2 O 3.85162 *1 \n", " 3.289152 -1.898982 -0.640992 2 O 3.85169 *1 \n", " -3.289055 -0.000018 2.012418 2 O 3.85587 *1 \n", " -1.644504 2.848428 2.012418 2 O 3.85587 *1 \n", " -1.644502 -2.848464 2.012418 2 O 3.85590 *1 \n", " 1.644599 2.848428 2.012418 2 O 3.85591 *1 \n", " 1.644601 -2.848464 2.012418 2 O 3.85594 *1 \n", " 3.289151 -0.000018 2.012418 2 O 3.85595 *1 \n", " -3.289054 -1.898982 -2.653413 1 Zn 4.63299 *1 \n", " -3.289054 -1.898982 2.653408 1 Zn 4.63299 *1 \n", " 0.000047 3.797910 -2.653413 1 Zn 4.63300 *1 \n", " 0.000047 3.797910 2.653408 1 Zn 4.63300 *1 \n", " 3.289152 -1.898982 -2.653413 1 Zn 4.63306 *1 \n", " 3.289152 -1.898982 2.653408 1 Zn 4.63306 *1 \n", " -3.289055 -0.000018 -3.294403 2 O 4.65521 *1 \n", " -1.644504 2.848428 -3.294403 2 O 4.65521 *1 \n", " -1.644502 -2.848464 -3.294403 2 O 4.65524 *1 \n", " 1.644599 2.848428 -3.294403 2 O 4.65525 *1 \n", " 1.644601 -2.848464 -3.294403 2 O 4.65527 *1 \n", " 3.289151 -0.000018 -3.294403 2 O 4.65528 *1 \n", " -1.644504 0.949464 4.665829 2 O 5.03744 *1 \n", " 0.000049 -1.898982 4.665829 2 O 5.03747 *1 \n", " 1.644599 0.949464 4.665829 2 O 5.03747 *1 \n", " -4.933607 0.949464 -0.640992 2 O 5.06486 *1 \n", " -3.289056 3.797910 -0.640992 2 O 5.06487 *1 \n", " -1.644502 -4.747428 -0.640992 2 O 5.06491 *1 \n", " 3.289150 3.797910 -0.640992 2 O 5.06493 *1 \n", " 1.644601 -4.747428 -0.640992 2 O 5.06494 *1 \n", " 4.933702 0.949464 -0.640992 2 O 5.06496 *1 \n", " 0.000048 -0.000018 -5.306823 1 Zn 5.30682 *1 \n", " 0.000048 -0.000018 5.306819 1 Zn 5.30682 *1 \n", " -4.933607 0.949464 -2.653413 1 Zn 5.68177 *1 \n", " -4.933607 0.949464 2.653408 1 Zn 5.68177 *1 \n", " -3.289056 3.797910 -2.653413 1 Zn 5.68178 *1 \n", " -3.289056 3.797910 2.653408 1 Zn 5.68178 *1 \n", " -1.644502 -4.747428 -2.653413 1 Zn 5.68182 *1 \n", " -1.644502 -4.747428 2.653408 1 Zn 5.68182 *1 \n", " 3.289150 3.797910 -2.653413 1 Zn 5.68183 *1 \n", " 3.289150 3.797910 2.653408 1 Zn 5.68183 *1 \n", " 1.644601 -4.747428 -2.653413 1 Zn 5.68185 *1 \n", " 1.644601 -4.747428 2.653408 1 Zn 5.68185 *1 \n", " 4.933702 0.949464 2.653408 1 Zn 5.68185 *1 \n", " 4.933702 0.949464 -2.653413 1 Zn 5.68186 *1 \n", " -4.933607 2.848428 -0.000002 1 Zn 5.69684 *1 \n", " -4.933605 -2.848464 -0.000002 1 Zn 5.69686 *1 \n", " 0.000046 5.696874 -0.000002 1 Zn 5.69687 *1 \n", " 0.000050 -5.696910 -0.000002 1 Zn 5.69691 *1 \n", " 4.933702 2.848428 -0.000002 1 Zn 5.69693 *1 \n", " 4.933704 -2.848464 -0.000002 1 Zn 5.69694 *1 \n", " -3.289054 -1.898982 4.665829 2 O 6.01614 *1 \n", " 0.000047 3.797910 4.665829 2 O 6.01615 *1 \n", " 3.289152 -1.898982 4.665829 2 O 6.01620 *1 \n", " -4.933607 2.848428 2.012418 2 O 6.04184 *1 \n", " -4.933605 -2.848464 2.012418 2 O 6.04186 *1 \n", " 0.000046 5.696874 2.012418 2 O 6.04187 *1 \n", " 0.000050 -5.696910 2.012418 2 O 6.04190 *1 \n", " 4.933702 2.848428 2.012418 2 O 6.04192 *1 \n", " 4.933704 -2.848464 2.012418 2 O 6.04194 *1 \n", " -3.289055 -0.000018 5.306819 1 Zn 6.24341 *1 \n", " -3.289055 -0.000018 -5.306823 1 Zn 6.24342 *1 \n", " -1.644504 2.848428 -5.306823 1 Zn 6.24342 *1 \n", " -1.644504 2.848428 5.306819 1 Zn 6.24342 *1 \n", " -1.644502 -2.848464 5.306819 1 Zn 6.24343 *1 \n", " -1.644502 -2.848464 -5.306823 1 Zn 6.24344 *1 \n", " 1.644599 2.848428 5.306819 1 Zn 6.24344 *1 \n", " 1.644599 2.848428 -5.306823 1 Zn 6.24345 *1 \n", " 1.644601 -2.848464 -5.306823 1 Zn 6.24346 *1 \n", " 1.644601 -2.848464 5.306819 1 Zn 6.24346 *1 \n", " 3.289151 -0.000018 5.306819 1 Zn 6.24346 *1 \n", " 3.289151 -0.000018 -5.306823 1 Zn 6.24347 *1 \n", " -1.644504 0.949464 -5.947813 2 O 6.24359 *1 \n", " 0.000049 -1.898982 -5.947813 2 O 6.24361 *1 \n", " 1.644599 0.949464 -5.947813 2 O 6.24361 *1 \n", " -6.578158 -0.000018 -0.000002 1 Zn 6.57816 *1 \n", " -3.289057 5.696874 -0.000002 1 Zn 6.57817 *1 \n", " -3.289053 -5.696910 -0.000002 1 Zn 6.57820 *1 \n", " 3.289149 5.696874 -0.000002 1 Zn 6.57821 *1 \n", " 3.289153 -5.696910 -0.000002 1 Zn 6.57825 *1 \n", " 6.578254 -0.000018 -0.000002 1 Zn 6.57825 *1 \n", " -4.933607 2.848428 -3.294403 2 O 6.58081 *1 \n", " -4.933605 -2.848464 -3.294403 2 O 6.58083 *1 \n", " 0.000046 5.696874 -3.294403 2 O 6.58084 *1 \n", " 0.000050 -5.696910 -3.294403 2 O 6.58087 *1 \n", " 4.933702 2.848428 -3.294403 2 O 6.58088 *1 \n", " 4.933704 -2.848464 -3.294403 2 O 6.58090 *1 \n", " -4.933607 0.949464 4.665829 2 O 6.85652 *1 \n", " -3.289056 3.797910 4.665829 2 O 6.85653 *1 \n", " -1.644502 -4.747428 4.665829 2 O 6.85656 *1 \n", " 3.289150 3.797910 4.665829 2 O 6.85657 *1 \n", " 1.644601 -4.747428 4.665829 2 O 6.85658 *1 \n", " 4.933702 0.949464 4.665829 2 O 6.85659 *1 \n", " -6.578157 -1.898982 -0.640992 2 O 6.87671 *1 \n", " -1.644506 6.646356 -0.640992 2 O 6.87672 *1 \n", " -4.933605 -4.747428 -0.640992 2 O 6.87673 *1 \n", " 1.644597 6.646356 -0.640992 2 O 6.87675 *1 \n", " 4.933704 -4.747428 -0.640992 2 O 6.87680 *1 \n", " 6.578255 -1.898982 -0.640992 2 O 6.87680 *1 \n", " -6.578158 -0.000018 2.012418 2 O 6.87910 *1 \n", " -3.289057 5.696874 2.012418 2 O 6.87911 *1 \n", " -3.289053 -5.696910 2.012418 2 O 6.87913 *1 \n", " 3.289149 5.696874 2.012418 2 O 6.87915 *1 \n", " 3.289153 -5.696910 2.012418 2 O 6.87918 *1 \n", " 6.578254 -0.000018 2.012418 2 O 6.87919 *1 \n", "\n", "END\n" ] } ], "source": [ "! cat {mat_obj.outdir}/feff.inp" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "run FEFF" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : ZnO_mp-2133\n", " : 2023-09-30_1618\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.42792E+00 1.30600E+00 1.15000E+00\n", " : 1 1.41649E+00 1.30145E+00 1.15000E+00\n", " : 2 1.09462E+00 1.00452E+00 1.15000E+00\n", " : : mu_old= -0.830\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 7.00000E+00 0.00000E+00\n", " : Rmax 7.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 1396 1396 5 5.2933\n", " : 2000 1876 1932 5 5.6530\n", " : 3000 2428 2428 5 5.9694\n", " : 4000 2961 2961 5 6.2061\n", " : 5000 3675 3679 5 6.2170\n", " : 6000 4015 4018 5 6.3022\n", " : 7000 4284 4287 5 6.4972\n", " : 8000 4710 4817 5 6.5658\n", " : 9000 4679 4817 5 6.5739\n", " : 10000 4615 4817 5 6.5767\n", " : 11000 4673 4817 5 6.5829\n", " : 12000 4699 4817 5 6.6057\n", " : 13000 5150 5166 5 6.6595\n", " : 14000 5127 5187 5 6.7838\n", " : 15000 5097 5187 5 6.8237\n", " : 16000 4791 5187 5 6.8759\n", " : 17000 4503 5187 5 6.9027\n", " : 18000 4222 5187 5 6.9117\n", " : 19000 4085 5187 5 6.9117\n", " : 20000 3924 5187 5 6.9117\n", " : 21000 3576 5187 5 6.9199\n", " : 22000 3351 5187 5 6.9289\n", " : 23000 2981 5187 5 6.9342\n", " : 24000 2576 5187 5 6.9379\n", " : 25000 2130 5187 5 6.9426\n", " : 26000 1458 5187 5 6.9446\n", " : 27000 925 5187 5 6.9654\n", " : 28000 293 5187 5 6.9826\n", " : Paths found 28478 (maxheap, maxscatt 5187 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 114, total paths 1044\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 3.000 2 2.0042\n", " : 2 33.026 1.000 2 2.0124\n", " : 3 65.446 6.000 2 3.2629\n", " : 4 64.213 6.000 2 3.2891\n", " : 5 10.014 1.000 2 3.2944\n", " : 6 8.562 6.000 3 3.6398\n", " : 7 9.468 6.000 3 3.6398\n", " : 8 9.479 6.000 3 3.6398\n", " : 9 9.145 6.000 3 3.6488\n", " : 10 20.151 12.000 3 3.6488\n", " : 11 19.892 3.000 2 3.8516\n", " : 12 39.665 6.000 2 3.8559\n", " : 13 5.315 3.000 4 4.0085\n", " : 14 3.512 6.000 4 4.0085\n", " : 15 1.693 3.000 4 4.0166 neglected\n", " : 16 1.697 3.000 4 4.0166 neglected\n", " : 17 7.267 12.000 3 4.5615\n", " : 18 5.523 12.000 3 4.5615\n", " : 19 7.131 12.000 3 4.5725\n", " : 20 5.492 12.000 3 4.5725\n", " : 21 7.077 12.000 3 4.5787\n", " : 22 5.452 12.000 3 4.5787\n", " : 23 27.221 6.000 2 4.6330\n", " : 24 23.562 6.000 2 4.6552\n", " : 25 3.655 24.000 3 4.9075\n", " : 26 5.846 12.000 3 4.9612\n", " : 27 11.968 12.000 3 4.9612\n", " : 28 3.749 12.000 3 4.9612\n", " : 29 9.396 3.000 2 5.0375\n", " : 30 18.498 6.000 2 5.0649\n", " : 31 5.058 6.000 3 5.1564\n", " : 32 11.259 6.000 3 5.1564\n", " : 33 2.237 6.000 3 5.1564 neglected\n", " : 34 10.156 12.000 3 5.1791\n", " : 35 22.687 12.000 3 5.1791\n", " : 36 4.631 12.000 3 5.1791\n", " : 37 3.095 12.000 3 5.2466\n", " : 38 3.455 12.000 3 5.2466\n", " : 39 3.656 12.000 3 5.2466\n", " : 40 3.340 6.000 4 5.2672\n", " : 41 4.485 3.000 4 5.2753\n", " : 42 2.394 12.000 4 5.2933 neglected\n", " : 43 5.385 12.000 4 5.2933\n", " : 44 5.282 6.000 4 5.2933\n", " : 45 9.129 6.000 4 5.2933\n", " : 46 3.141 12.000 4 5.2933\n", " : 47 6.284 2.000 2 5.3068\n", " : 48 3.595 2.000 3 5.3068\n", " : 49 7.213 2.000 3 5.3068\n", " : 50 6.775 2.000 3 5.3068\n", " : 51 4.151 1.000 4 5.3068\n", " : 52 3.646 1.000 4 5.3068\n", " : 53 31.146 12.000 2 5.6818\n", " : 54 15.457 6.000 2 5.6969\n", " : 55 12.575 12.000 3 5.7710\n", " : 56 14.674 12.000 3 5.7710\n", " : 57 12.865 12.000 3 5.7710\n", " : 58 6.442 6.000 3 5.7764\n", " : 59 7.989 6.000 3 5.7764\n", " : 60 7.024 6.000 3 5.7764\n", " : 61 2.773 3.000 4 5.8559\n", " : 62 2.296 3.000 4 5.8559 neglected\n", " : 63 3.596 12.000 4 5.8601\n", " : 64 4.992 6.000 4 5.8601\n", " : 65 4.104 6.000 4 5.8601\n", " : 66 5.546 3.000 2 6.0162\n", " : 67 10.949 6.000 2 6.0419\n", " : 68 4.246 24.000 3 6.1169\n", " : 69 2.405 24.000 3 6.1169 neglected\n", " : 70 2.388 24.000 3 6.1169 neglected\n", " : 71 2.337 24.000 3 6.1375 neglected\n", " : 72 2.863 12.000 3 6.1706\n", " : 73 2.745 12.000 4 6.2061\n", " : 74 2.711 12.000 4 6.2061\n", " : 75 2.754 12.000 4 6.2170\n", " : 76 23.757 12.000 2 6.2434\n", " : 77 4.951 3.000 2 6.2436\n", " : 78 2.817 6.000 3 6.3308\n", " : 79 2.960 6.000 3 6.3396\n", " : 80 4.713 12.000 3 6.3846\n", " : 81 4.006 24.000 3 6.3846\n", " : 82 4.500 6.000 3 6.4005\n", " : 83 5.969 12.000 3 6.4556\n", " : 84 4.944 12.000 3 6.4556\n", " : 85 4.738 12.000 3 6.4556\n", " : 86 3.128 12.000 3 6.5675\n", " : 87 10.187 6.000 2 6.5782\n", " : 88 3.314 6.000 3 6.5782\n", " : 89 20.480 12.000 3 6.5782\n", " : 90 3.569 6.000 4 6.5782\n", " : 91 14.953 6.000 4 6.5782\n", " : 92 8.413 6.000 2 6.5809\n", " : 93 2.164 24.000 3 6.5935 neglected\n", " : 94 6.192 24.000 3 6.5935\n", " : 95 3.177 12.000 4 6.5968\n", " : 96 3.035 6.000 3 6.6090\n", " : 97 7.240 6.000 3 6.6091\n", " : 98 3.270 3.000 4 6.6372\n", " : 99 2.835 12.000 3 6.6426\n", " : 100 2.787 12.000 3 6.6969\n", " : 101 2.530 12.000 3 6.6969 neglected\n", " : 102 2.520 12.000 3 6.6969 neglected\n", " : 103 4.458 12.000 3 6.8237\n", " : 104 3.764 12.000 3 6.8237\n", " : 105 3.859 12.000 3 6.8237\n", " : 106 3.671 12.000 4 6.8237\n", " : 107 1.572 12.000 4 6.8237 neglected\n", " : 108 2.622 6.000 3 6.8477 neglected\n", " : 109 7.397 6.000 2 6.8566\n", " : 110 7.328 6.000 2 6.8768\n", " : 111 7.320 6.000 2 6.8791\n", " : 112 2.561 12.000 4 6.9379 neglected\n", " : 113 3.101 12.000 3 6.9877\n", " : 114 7.065 12.000 3 6.9877\n", " : 100 paths kept, 114 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.950 Global sig2 0.00000\n", " : Done with module 6: DW + final sum over paths.\n" ] } ], "source": [ "#via larch\n", "from larch.xafs import FeffRunner\n", "feff_inp = f\"{mat_obj.outdir}/feff.inp\"\n", "sim = FeffRunner(feff_inp)\n", "sim.run()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Note: can also be run via direct call to `feff8l`\n", "\n", "```\n", "! cd {mat_obj.parent_path}; feff8l\n", "```" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "load the simulated EXAFS" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from glob import glob\n", "xmu = glob(f\"{mat_obj.outdir}/xmu.dat\")[0]\n", "from larch.io import read_ascii\n", "gsim = read_ascii(xmu, labels=[\"energy\", \"erel\", \"k\", \"mu\", \"mu0\", \"chi\"])" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Load experimental data and quickly extract the EXAFS signal" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "example_fn = os.path.join('..', \"fdmnes\", \"ZnO_SSHADE.data.txt\")\n", "gexp = read_ascii(example_fn, labels=[\"energy\", \"mu\"])\n", "from larch.xafs import pre_edge, autobk\n", "pre_edge(gexp, e0=9661)\n", "autobk(gexp)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "then plot" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#1f77b4", "width": 3 }, "name": "FEFF simulation", "type": "scatter", "uid": "0a403907-ee6e-4f34-888e-e3c244318268", "x": [ 0.35, 0.4, 0.44999999999999996, 0.5, 0.55, 0.6, 0.6499999999999999, 0.7, 0.75, 0.8, 0.8500000000000001, 0.8999999999999999, 0.95, 1, 1.05, 1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4000000000000001, 1.45, 1.5, 1.55, 1.6, 1.6500000000000001, 1.7, 1.75, 1.8, 1.85, 1.9000000000000001, 1.95, 2, 2.05, 2.1, 2.15, 2.1999999999999997, 2.25, 2.3, 2.3499999999999996, 2.4, 2.4499999999999997, 2.5, 2.55, 2.5999999999999996, 2.65, 2.6999999999999997, 2.75, 2.8, 2.8499999999999996, 2.9, 2.9499999999999997, 3, 3.05, 3.0999999999999996, 3.15, 3.1999999999999997, 3.25, 3.3, 3.3499999999999996, 3.4, 3.4499999999999997, 3.5, 3.55, 3.5999999999999996, 3.65, 3.6999999999999997, 3.75, 3.8, 3.8499999999999996, 3.9, 3.9499999999999997, 4, 4.05, 4.1, 4.15, 4.2, 4.25, 4.3, 4.35, 4.3999999999999995, 4.45, 4.5, 4.55, 4.6, 4.6499999999999995, 4.7, 4.75, 4.8, 4.85, 4.8999999999999995, 4.95, 5, 5.05, 5.1, 5.1499999999999995, 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IIIAAAggggAACThcg8OL0J4D2I4AAAggggAACCCCAAAIIIIBAwAQIvASMlhMjgAACCCCAAAIIIIAAAggggIDTBf4/wvVUZQa/huAAAAAASUVORK5CYII=", "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from larch.plot.plotly_xafsplots import PlotlyFigure\n", "fig = PlotlyFigure()\n", "fig.add_plot(gsim.k+0.3, gsim.chi*gsim.k**2, label=\"FEFF simulation\")\n", "fig.add_plot(gexp.k, gexp.chi*gexp.k**2, label=\"EXP data\")\n", "\n", "fig.set_style(title=\"Wurtzite ZnO EXAFS\", width=800, height=500, xaxis_title=\"k (Å^-1)\", yaxis_title=\"chi(k) * k^2\" )\n", "fig.set_xrange(0, 10)\n", "fig.show()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "*Note: the agreement is not very good because the experimental EXAFS data stops at 8 $\\AA^{-1}$ and the structure parameters are not optimized.*" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2: structures with multiple sites\n", "\n", "Here an example when the input structure has multiple sites for a given absorbing element. The number of non-equivalent sites is found via the [SpaceGroupAnalyzer](https://pymatgen.org/pymatgen.symmetry.analyzer.html#pymatgen.symmetry.analyzer.SpacegroupAnalyzer) in Pymatgen, which is based on [Spglib](https://spglib.readthedocs.io/en/latest/).\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : Frames: 1, Absorbing sites: 4. (Indexes for frames and abs_sites start at 0)\n" ] } ], "source": [ "from larch.xrd.struct2xas import Struct2XAS\n", "cif_input_fn = os.path.join('..', \"structuredata\", \"struct2xas\", \"ZnO_mp-997630.cif\")\n", "mat_obj = Struct2XAS(file=cif_input_fn, abs_atom=\"Zn\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
idx_absspeciefrac_coordswyckoff_sitecart_coordsoccupancyidx_in_struct
0Zn[0.6667, 0.3333, 0.1651]4f[-1.5277 -0.882 -5.0195]10
1Zn[0.6667, 0.3333, 0.5825]4f[ -1.5277 -0.882 -17.7146]14
2Zn[0.0, 0.0, 0.75]2b[ 0. 0. -22.8074]18
3Zn[0.0, 0.0, 0.0]2a[0. 0. 0.]110
\n" ], "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mat_obj.get_abs_sites_info()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "it is possible to select one site and get information on the local environment and visualize, as shown before" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coord. Env. from absorber atom: Zn at site 2\n", "['T:6']\n", " Element Distance\n", "0 (O) 2.21120\n", "1 (O) 2.21120\n", "2 (O) 2.21120\n", "3 (O) 2.21124\n", "4 (O) 2.21124\n", "5 (O) 2.21124\n" ] }, { "data": { "application/3dmoljs_load.v0": "
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You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

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You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : {'Zn': 'red', 'O': 'green'}\n" ] } ], "source": [ "# analysis for second site\n", "mat_obj.set_abs_site(2)\n", "print(mat_obj.get_coord_envs_info())\n", "mat_obj.visualize()" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "to create the FDMNES inputs simply range over the sites" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-997630/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site2/job_inp.txt\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site3/job_inp.txt\n" ] } ], "source": [ "mypath = None\n", "for site in range(mat_obj.nabs_sites): \n", " mat_obj.set_abs_site(site)\n", " mat_obj.make_input_fdmnes(radius=7, green=False, parent_path=mypath)\n", " mypath = mat_obj.parent_path" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the FEFF inputs too" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site2/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/ZnO_mp-997630/Zn/frame0/site3/feff.inp\n" ] } ], "source": [ "for site in range(mat_obj.nabs_sites): \n", " mat_obj.set_abs_site(site)\n", " mat_obj.make_input_feff(radius=7, parent_path=mypath)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Example 3: grab CIFs from Materials Project\n", "\n", "In this example some CIFs files are grabbed from the Materials Project database (MP) via a query and the inputs files are generated.\n", "\n", "A personal API key is required to query the database (-> [here](https://legacy.materialsproject.org/open). *NOTE* the legacy version is used in this example, that is, the \"old\" version of MP. Export `your_api_key` to the environmental variable `MP_API_KEY`.\n", "\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "import os\n", "from mp_api.client import MPRester\n", "from larch.xrd.struct2xas import Struct2XAS, save_mp_structure, structure_folders\n", "\n", "api_key = os.getenv(\"MP_API_KEY\")\n", "mpr = MPRester(api_key)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we search for materials containing 2 elements, Zn and O, plus further filter those IDs not present in the ICSD database" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "29535a8b91ff478db48f82432c65d8fb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Retrieving SummaryDoc documents: 0%| | 0/3643 [00:00 /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020610/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020610/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-1017539/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO_mp-1017539/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn(Bi3O5)4_mp-28402/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn(Bi3O5)4_mp-28402/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-13161/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO_mp-13161/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-641917/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-641917/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-641917/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-641917/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-2229/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO_mp-2229/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO5_mp-1105595/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO5_mp-1105595/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-1020623/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-1020623/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-1986/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO_mp-1986/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO2_mp-1102744/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO2_mp-1102744/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO_mp-2133/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO_mp-2133/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO5_mp-1189355/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO5_mp-1189355/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-3789/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-3789/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-3789/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-3789/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020721/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020721/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020721/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020721/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn3(PO6)2_mp-1179030/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn3(PO6)2_mp-1179030/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn3(PO6)2_mp-1179030/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn3(PO6)2_mp-1179030/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020609/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020609/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020609/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020609/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO2_mp-8484/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO2_mp-8484/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-619034/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-619034/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-619034/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-619034/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-558096/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-558096/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/Zn2SiO4_mp-1020594/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/Zn2SiO4_mp-1020594/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 2. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-562182/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-562182/Zn/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnSiO3_mp-562182/Zn/frame0/site1/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnSiO3_mp-562182/Zn/frame0/site1/feff.inp\n", "[struct2xas] INFO : Frames: 1, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n", "[struct2xas] INFO : written FDMNES input -> /Users/Newville/.larch/fdmnes/ZnO2_mp-1094003/Zn/frame0/site0/job_inp.txt\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/ZnO2_mp-1094003/Zn/frame0/site0/feff.inp\n" ] } ], "source": [ "from glob import glob\n", "from larch.xrd.struct2xas import structure_folders\n", "sfolder = structure_folders()['mp_structs']\n", "print(sfolder)\n", "for sdat in glob(f\"{sfolder}/*.mpjson\"):\n", " s2x = Struct2XAS(sdat, abs_atom=\"Zn\")\n", " for abs_site in s2x.get_abs_sites():\n", " s2x.set_abs_site(abs_site[0])\n", " s2x.make_input_fdmnes(radius=7)\n", " s2x.make_input_feff(radius=7)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the next steps consist in running the simulations and comparing with experimental data. This is beyond the scope of current example." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 4: from XYZ (single or multi-frame) to XAS\n", "\n", "This example shows the case of XYZ files as input. In particular, in the case the XYZ file contains multiple frames. This is a typical output format of molecular dynamics simulations.\n", "\n", "Here the case of Ga K-edge of a GaBr solution is taken as example:\n", "- Cécile Da Silva, Olivier Proux, Jean-Louis Hazemann, Julianne James-Smith, Denis Testemale, Toshio Yamaguchi,\n", "X-ray absorption spectroscopy study of solvation and ion-pairing in aqueous gallium bromide solutions at supercritical conditions,\n", "Journal of Molecular Liquids, Volume 147, Issues 1–2, 2009, Pages 83-95, ISSN 0167-7322, [https://doi.org/10.1016/j.molliq.2008.06.022]\n", "- The experimental data are taken from the SSHADE database [DOI:10.26302/SSHADE/EXPERIMENT_ST_20180418_001](https://www.sshade.eu/data/EXPERIMENT_ST_20180418_001)\n", "- The input structure is manually generated by simply changing the Ga-O distance\n", "\n" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : Frames: 5, Absorbing sites: 1. (Indexes for frames and abs_sites start at 0)\n" ] } ], "source": [ "from larch.xrd.struct2xas import Struct2XAS\n", "gabr = Struct2XAS(file=\"../structuredata/struct2xas/GaBr_multi-frame.xyz\", abs_atom=\"Ga\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "it is possible to select a given frame, visualize and get the coordination environment" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ "
\n", "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : {'Ga': 'red', 'O': 'green', 'H': 'blue'}\n" ] } ], "source": [ "gabr.set_frame(2)\n", "gabr.visualize(5)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coord. Env. from absorber atom: Ga at site 0\n", "Elements Dict = {'H': 2}\n" ] }, { "data": { "text/html": [ "
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ElementDistance
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" ], "text/plain": [ " Element Distance\n", "0 (H) 2.64949\n", "1 (H) 2.64949" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gabr.get_coord_envs_info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "generate FEFF EXAFS inputs (for FDMNES is equivalent)" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame0/site0/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame1/site0/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame2/site0/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame3/site0/feff.inp\n", "[struct2xas] INFO : written FEFF input -> /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame4/site0/feff.inp\n", "/Users/Newville/.larch/feff/GaBr_multi-frame/Ga\n" ] } ], "source": [ "for frame in range(gabr.nframes):\n", " gabr.set_frame(frame)\n", " gabr.make_input_feff(radius=5, sig2=0.005)\n", "outdir = os.path.abspath(os.path.join(gabr.outdir, '..', '..'))\n", "print(outdir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "run the simulations" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['/Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame1/site0/feff.inp', '/Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame0/site0/feff.inp', '/Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame2/site0/feff.inp', '/Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame4/site0/feff.inp', '/Users/Newville/.larch/feff/GaBr_multi-frame/Ga/frame3/site0/feff.inp']\n", " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : GaBr_multi-frame\n", " : 2023-09-30_1621\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.34750E+00 1.17999E+00 1.15000E+00\n", " : 1 1.14900E+00 9.82254E-01 1.15000E+00\n", " : 2 1.04613E+00 8.81502E-01 1.15000E+00\n", " : : mu_old= 2.387\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 5.00000E+00 0.00000E+00\n", " : Rmax 5.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 837 868 5 4.3943\n", " : 2000 819 930 5 4.6309\n", " : 3000 666 930 5 4.8558\n", " : 4000 117 930 5 4.9986\n", " : Paths found 4224 (maxheap, maxscatt 930 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 40, total paths 936\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 6.000 2 1.9010\n", " : 2 17.363 12.000 2 2.5704\n", " : 3 35.732 24.000 3 2.7086\n", " : 4 35.828 12.000 4 2.8468\n", " : 5 19.179 24.000 3 3.2452\n", " : 6 2.683 12.000 3 3.3384\n", " : 7 4.589 24.000 4 3.4766\n", " : 8 4.578 12.000 4 3.5162\n", " : 9 1.441 12.000 4 3.5162 neglected\n", " : 10 2.506 12.000 5 3.6148 neglected\n", " : 11 2.517 24.000 5 3.6544 neglected\n", " : 12 9.831 24.000 5 3.6544\n", " : 13 11.954 12.000 6 3.7926\n", " : 14 32.116 12.000 6 3.7926\n", " : 15 16.274 6.000 3 3.8020\n", " : 16 30.993 6.000 4 3.8020\n", " : 17 7.995 6.000 4 3.8020\n", " : 18 9.012 24.000 4 3.8020\n", " : 19 4.124 48.000 3 3.8342\n", " : 20 5.460 48.000 4 3.9724\n", " : 21 3.785 24.000 4 4.0528\n", " : 22 2.849 48.000 4 4.0528\n", " : 23 7.384 24.000 5 4.1910\n", " : 24 3.449 48.000 5 4.1910\n", " : 25 2.602 48.000 5 4.3835 neglected\n", " : 26 6.869 24.000 3 4.4463\n", " : 27 11.192 24.000 4 4.4714\n", " : 28 3.790 24.000 4 4.4714\n", " : 29 8.597 24.000 4 4.5845\n", " : 30 6.579 24.000 4 4.5894\n", " : 31 13.592 24.000 4 4.5894\n", " : 32 7.045 24.000 4 4.6096\n", " : 33 13.463 24.000 5 4.6096\n", " : 34 14.179 24.000 5 4.6096\n", " : 35 4.941 24.000 5 4.6096\n", " : 36 4.513 24.000 5 4.6096\n", " : 37 16.496 24.000 6 4.7478\n", " : 38 7.880 24.000 5 4.7478\n", " : 39 6.229 24.000 6 4.7478\n", " : 40 2.627 48.000 6 4.9182 neglected\n", " : 35 paths kept, 40 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.951 Global sig2 0.00500\n", " : Done with module 6: DW + final sum over paths.\n", " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : GaBr_multi-frame\n", " : 2023-09-30_1621\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.36815E+00 1.20382E+00 1.15000E+00\n", " : 1 1.15562E+00 9.86379E-01 1.15000E+00\n", " : 2 1.04336E+00 8.74827E-01 1.15000E+00\n", " : : mu_old= 2.273\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 5.00000E+00 0.00000E+00\n", " : Rmax 5.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 710 738 5 4.4607\n", " : 2000 627 738 5 4.7010\n", " : 3000 360 738 5 4.9296\n", " : Paths found 3480 (maxheap, maxscatt 738 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 40, total paths 936\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 6.000 2 1.9310\n", " : 2 17.473 12.000 2 2.6093\n", " : 3 35.402 24.000 3 2.7500\n", " : 4 31.353 12.000 4 2.8906\n", " : 5 18.570 24.000 3 3.2964\n", " : 6 2.672 12.000 3 3.3893\n", " : 7 4.539 24.000 4 3.5300\n", " : 8 4.524 12.000 4 3.5689\n", " : 9 1.399 12.000 4 3.5689 neglected\n", " : 10 2.428 12.000 5 3.6706 neglected\n", " : 11 2.347 24.000 5 3.7096 neglected\n", " : 12 9.260 24.000 5 3.7096\n", " : 13 8.720 12.000 6 3.8503\n", " : 14 20.837 12.000 6 3.8503\n", " : 15 15.869 6.000 3 3.8620\n", " : 16 30.047 6.000 4 3.8620\n", " : 17 7.833 6.000 4 3.8620\n", " : 18 8.431 24.000 4 3.8620\n", " : 19 4.034 48.000 3 3.8932\n", " : 20 5.246 48.000 4 4.0339\n", " : 21 3.646 24.000 4 4.1154\n", " : 22 2.763 48.000 4 4.1154\n", " : 23 6.758 24.000 5 4.2560\n", " : 24 3.214 48.000 5 4.2560\n", " : 25 2.468 48.000 5 4.4505 neglected\n", " : 26 6.767 24.000 3 4.5148\n", " : 27 10.932 24.000 4 4.5403\n", " : 28 3.763 24.000 4 4.5403\n", " : 29 8.349 24.000 4 4.6555\n", " : 30 6.336 24.000 4 4.6618\n", " : 31 13.047 24.000 4 4.6618\n", " : 32 6.841 24.000 4 4.6810\n", " : 33 12.999 24.000 5 4.6810\n", " : 34 13.625 24.000 5 4.6810\n", " : 35 4.842 24.000 5 4.6810\n", " : 36 4.435 24.000 5 4.6810\n", " : 37 15.116 24.000 6 4.8216\n", " : 38 7.310 24.000 5 4.8216\n", " : 39 5.769 24.000 6 4.8216\n", " : 40 2.415 48.000 6 4.9935 neglected\n", " : 35 paths kept, 40 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.951 Global sig2 0.00500\n", " : Done with module 6: DW + final sum over paths.\n", " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : GaBr_multi-frame\n", " : 2023-09-30_1621\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.38811E+00 1.22707E+00 1.15000E+00\n", " : 1 1.16169E+00 9.90134E-01 1.15000E+00\n", " : 2 1.04083E+00 8.68755E-01 1.15000E+00\n", " : : mu_old= 2.186\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 5.00000E+00 0.00000E+00\n", " : Rmax 5.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 588 619 5 4.5295\n", " : 2000 471 619 5 4.7734\n", " : Paths found 2940 (maxheap, maxscatt 619 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 39, total paths 888\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 6.000 2 1.9600\n", " : 2 17.598 12.000 2 2.6495\n", " : 3 34.996 24.000 3 2.7921\n", " : 4 23.701 12.000 4 2.9347\n", " : 5 18.000 24.000 3 3.3459\n", " : 6 2.655 12.000 3 3.4413 neglected\n", " : 7 4.519 24.000 4 3.5839\n", " : 8 4.453 12.000 4 3.6242\n", " : 9 1.350 12.000 4 3.6242 neglected\n", " : 10 2.368 12.000 5 3.7266 neglected\n", " : 11 2.032 24.000 5 3.7668 neglected\n", " : 12 8.707 24.000 5 3.7668\n", " : 13 4.061 12.000 6 3.9095\n", " : 14 9.184 12.000 6 3.9095\n", " : 15 15.492 6.000 3 3.9200\n", " : 16 29.154 6.000 4 3.9200\n", " : 17 7.672 6.000 4 3.9200\n", " : 18 7.906 24.000 4 3.9200\n", " : 19 3.949 48.000 3 3.9526\n", " : 20 5.034 48.000 4 4.0952\n", " : 21 3.520 24.000 4 4.1780\n", " : 22 2.670 48.000 4 4.1780\n", " : 23 6.208 24.000 5 4.3207\n", " : 24 2.963 48.000 5 4.3207\n", " : 25 2.329 48.000 5 4.5187 neglected\n", " : 26 6.677 24.000 3 4.5836\n", " : 27 10.684 24.000 4 4.6095\n", " : 28 3.723 24.000 4 4.6095\n", " : 29 8.109 24.000 4 4.7262\n", " : 30 6.103 24.000 4 4.7319\n", " : 31 12.539 24.000 4 4.7319\n", " : 32 6.643 24.000 4 4.7521\n", " : 33 12.538 24.000 5 4.7521\n", " : 34 13.082 24.000 5 4.7521\n", " : 35 4.729 24.000 5 4.7521\n", " : 36 4.343 24.000 5 4.7521\n", " : 37 13.682 24.000 6 4.8947\n", " : 38 6.723 24.000 5 4.8947\n", " : 39 5.291 24.000 6 4.8947\n", " : 34 paths kept, 39 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.951 Global sig2 0.00500\n", " : Done with module 6: DW + final sum over paths.\n", " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : GaBr_multi-frame\n", " : 2023-09-30_1621\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.42868E+00 1.27476E+00 1.15000E+00\n", " : 1 1.17353E+00 9.97844E-01 1.15000E+00\n", " : 2 1.03739E+00 8.59043E-01 1.15000E+00\n", " : : mu_old= 1.982\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 5.00000E+00 0.00000E+00\n", " : Rmax 5.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 458 507 5 4.6651\n", " : 2000 144 507 5 4.9165\n", " : Paths found 2220 (maxheap, maxscatt 507 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 36, total paths 816\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 6.000 2 2.0190\n", " : 2 17.834 12.000 2 2.7289\n", " : 3 34.577 24.000 3 2.8759\n", " : 4 19.979 12.000 4 3.0230\n", " : 5 16.971 24.000 3 3.4466\n", " : 6 2.646 12.000 3 3.5449 neglected\n", " : 7 4.487 24.000 4 3.6919\n", " : 8 4.362 12.000 4 3.7329\n", " : 9 1.301 12.000 4 3.7329 neglected\n", " : 10 2.282 12.000 5 3.8390 neglected\n", " : 11 1.977 24.000 5 3.8800 neglected\n", " : 12 8.187 24.000 5 3.8800\n", " : 13 2.467 12.000 6 4.0271 neglected\n", " : 14 5.764 12.000 6 4.0271\n", " : 15 14.787 6.000 3 4.0380\n", " : 16 28.801 6.000 4 4.0380\n", " : 17 6.695 6.000 4 4.0380\n", " : 18 7.299 24.000 4 4.0380\n", " : 19 3.801 48.000 3 4.0712\n", " : 20 4.692 48.000 4 4.2183\n", " : 21 3.278 24.000 4 4.3036\n", " : 22 2.528 48.000 4 4.3036 neglected\n", " : 23 5.476 24.000 5 4.4507\n", " : 24 2.662 48.000 5 4.4507 neglected\n", " : 25 2.113 48.000 5 4.6543 neglected\n", " : 26 6.501 24.000 3 4.7212\n", " : 27 10.800 24.000 4 4.7479\n", " : 28 3.402 24.000 4 4.7479\n", " : 29 7.704 24.000 4 4.8682\n", " : 30 5.694 24.000 4 4.8743\n", " : 31 11.615 24.000 4 4.8743\n", " : 32 6.312 24.000 4 4.8949\n", " : 33 12.398 24.000 5 4.8949\n", " : 34 12.895 24.000 5 4.8949\n", " : 35 4.231 24.000 5 4.8949\n", " : 36 3.820 24.000 5 4.8949\n", " : 28 paths kept, 36 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.951 Global sig2 0.00500\n", " : Done with module 6: DW + final sum over paths.\n", " : ======== running Feff module feff8l_rdinp ========\n", " : Feff8L (EXAFS) release 0.1\n", " : GaBr_multi-frame\n", " : 2023-09-30_1621\n", " : site 0\n", " : ======== running Feff module feff8l_pot ========\n", " : Calculating potentials ...\n", " : free atom potential and density for atom type 0\n", " : free atom potential and density for atom type 1\n", " : free atom potential and density for atom type 2\n", " : initial state energy\n", " : overlapped potential and density for unique potential 0\n", " : overlapped potential and density for unique potential 1\n", " : overlapped potential and density for unique potential 2\n", " : muffin tin radii and interstitial parameters\n", " : : ipot, Norman radius, Muffin tin radius, Overlap\n", " : 0 1.40806E+00 1.25046E+00 1.15000E+00\n", " : 1 1.16758E+00 9.93896E-01 1.15000E+00\n", " : 2 1.03884E+00 8.63537E-01 1.15000E+00\n", " : : mu_old= 2.082\n", " : Done with module 1: potentials.\n", " : ======== running Feff module feff8l_xsph ========\n", " : Calculating cross-section and phases...\n", " : absorption cross section\n", " : phase shifts for unique potential 0\n", " : phase shifts for unique potential 1\n", " : phase shifts for unique potential 2\n", " : Done with module 2: cross-section and phases...\n", " : ======== running Feff module feff8l_pathfinder ========\n", " : Preparing plane wave scattering amplitudes...\n", " : Searching for paths...\n", " : WARNING: rmax > distance to most distant atom.\n", " : Some paths may be missing.\n", " : rmax, ratx 5.00000E+00 0.00000E+00\n", " : Rmax 5.0000 keep and heap limits 0.0000000 0.0000000\n", " : Preparing neighbor table\n", " : nfound heapsize maxheap maxscatt reff\n", " : 1000 469 518 5 4.5970\n", " : 2000 315 518 5 4.8447\n", " : Paths found 2532 (maxheap, maxscatt 518 5)\n", " : Eliminating path degeneracies...\n", " : Plane wave chi amplitude filter 2.50%\n", " : Unique paths 39, total paths 888\n", " : Done with module 4: pathfinder.\n", " : ======== running Feff module feff8l_genfmt ========\n", " : Calculating EXAFS parameters...\n", " : Curved wave chi amplitude ratio 4.00%\n", " : Discard feff.dat for paths with cw ratio < 2.67%\n", " : path cw ratio deg nleg reff\n", " : 1 100.000 6.000 2 1.9890\n", " : 2 17.731 12.000 2 2.6890\n", " : 3 34.824 24.000 3 2.8338\n", " : 4 21.611 12.000 4 2.9786\n", " : 5 17.499 24.000 3 3.3954\n", " : 6 2.654 12.000 3 3.4930 neglected\n", " : 7 4.514 24.000 4 3.6378\n", " : 8 4.413 12.000 4 3.6786\n", " : 9 1.325 12.000 4 3.6786 neglected\n", " : 10 2.331 12.000 5 3.7826 neglected\n", " : 11 1.996 24.000 5 3.8234 neglected\n", " : 12 8.441 24.000 5 3.8234\n", " : 13 3.090 12.000 6 3.9682\n", " : 14 7.049 12.000 6 3.9682\n", " : 15 15.149 6.000 3 3.9780\n", " : 16 29.702 6.000 4 3.9780\n", " : 17 6.794 6.000 4 3.9780\n", " : 18 7.787 24.000 4 3.9780\n", " : 19 3.883 48.000 3 4.0113\n", " : 20 4.869 48.000 4 4.1561\n", " : 21 3.400 24.000 4 4.2402\n", " : 22 2.604 48.000 4 4.2402 neglected\n", " : 23 5.833 24.000 5 4.3851\n", " : 24 2.810 48.000 5 4.3851\n", " : 25 2.222 48.000 5 4.5861 neglected\n", " : 26 6.599 24.000 3 4.6517\n", " : 27 11.065 24.000 4 4.6780\n", " : 28 3.419 24.000 4 4.6780\n", " : 29 7.918 24.000 4 4.7965\n", " : 30 5.911 24.000 4 4.8019\n", " : 31 12.089 24.000 4 4.8019\n", " : 32 6.487 24.000 4 4.8228\n", " : 33 12.832 24.000 5 4.8228\n", " : 34 13.401 24.000 5 4.8228\n", " : 35 4.305 24.000 5 4.8228\n", " : 36 3.867 24.000 5 4.8228\n", " : 37 13.591 24.000 6 4.9676\n", " : 38 6.402 24.000 5 4.9676\n", " : 39 4.635 24.000 6 4.9676\n", " : 33 paths kept, 39 examined.\n", " : Done with module 5: F_eff.\n", " : Note: The following floating-point exceptions are signalling: IEEE_UNDERFLOW_FLAG IEEE_DENORMAL\n", " : ======== running Feff module feff8l_ff2x ========\n", " : Calculating chi...\n", " : Use all paths with cw amplitude ratio 4.00%\n", " : S02 0.951 Global sig2 0.00500\n", " : Done with module 6: DW + final sum over paths.\n" ] } ], "source": [ "from glob import glob\n", "from larch.xafs import FeffRunner\n", "\n", "feffinps = glob(f\"{outdir}/**/feff.inp\", recursive=True)\n", "print(feffinps)\n", "for feffinp in feffinps:\n", " sim = FeffRunner(feffinp)\n", " sim.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "sometimes is useful to export all the `xmu.dat` files in a single HDF5 file container" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "save to group /GaBr_multi-frame/Ga/frame1/site0\n", "save to group /GaBr_multi-frame/Ga/frame0/site0\n", "save to group /GaBr_multi-frame/Ga/frame2/site0\n", "save to group /GaBr_multi-frame/Ga/frame4/site0\n", "save to group /GaBr_multi-frame/Ga/frame3/site0\n", "xmu data exported to HDF5: /Users/Newville/.larch/feff/GaBr_multi-frame/Ga/hdf5/gabr_exafs.h5\n" ] } ], "source": [ "import os\n", "import h5py\n", "from larch.io import read_ascii\n", "\n", "h5dir = os.path.join(outdir, \"hdf5\")\n", "try:\n", " os.makedirs(h5dir)\n", "except FileExistsError:\n", " pass\n", "h5out = os.path.join(h5dir, \"gabr_exafs.h5\")\n", "\n", "xmus = glob(f\"{outdir}/**/xmu.dat\", recursive=True)\n", "labels = [\"energy\", \"erel\", \"k\", \"mu\", \"mu0\", \"chi\"]\n", "\n", "with h5py.File(h5out, \"w\") as f:\n", " for xmu in xmus:\n", " gxmu = read_ascii(xmu, labels=labels)\n", " sample, abs_at, framestr, sitestr = xmu.split(os.sep)[-5:-1] \n", " grouppath = f\"/{sample}/{abs_at}/{framestr}/{sitestr}\"\n", " print(f\"save to group {grouppath}\")\n", " try:\n", " f.create_group(grouppath)\n", " except Exception:\n", " pass\n", " for lab in labels:\n", " f.create_dataset(f\"{grouppath}/{lab}\", data=getattr(gxmu, lab))\n", "print(f\"xmu data exported to HDF5: {h5out}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "line": { "color": "#1f77b4", "width": 3 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from larch.xafs import pre_edge, autobk\n", "from larch.plot.plotly_xafsplots import PlotlyFigure, plotlabels\n", "\n", "fig = PlotlyFigure()\n", "\n", "xmus = glob(f\"{outdir}/**/xmu.dat\", recursive=True)\n", "for xmu in xmus:\n", " sample, abs_at, framestr, sitestr = xmu.split(os.sep)[-5:-1] \n", " gxmu = read_ascii(xmu, labels=[\"energy\", \"erel\", \"k\", \"mu\", \"mu0\", \"chi\"])\n", " fig.add_plot(x=gxmu.k, y=gxmu.chi*gxmu.k**2, label=f\"{sample}/{framestr}\")\n", "\n", "#EXP DATA\n", "fn_in = os.path.join('..', \"structuredata\", \"struct2xas\", \"GaBr_GaK_SSHADE.data.txt\")\n", "gexp = read_ascii(fn_in, labels=[\"energy\", \"mu\"])\n", "pre_edge(gexp)\n", "autobk(gexp)\n", "fig.add_plot(x=gexp.k, y=gexp.chi*gexp.k**2, color=\"black\", label=\"exp data\")\n", "\n", "\n", "\n", "fig.set_style(title=\"GaBr (Ga K-edge): FEFF sim. vs exp.\", width=900, height=500, xaxis_title=plotlabels.k, yaxis_title=plotlabels.chi2)\n", "fig.set_xrange(2, 14)\n", "fig.set_yrange(-3, 3)\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['GaBr_multi-frame']\n" ] } ], "source": [ "with h5py.File(h5out, \"r\") as f:\n", " print(list(f.keys()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n" ] } ], "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": "3.11.0" }, "pycharm": { "stem_cell": { "cell_type": "raw", "metadata": { "collapsed": false }, "source": [] } }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }