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<figure class="media"><div data-oembed-url="https://www.youtube.com/watch?v=h4Tj_aT6fEc"><div style="position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;"><iframe src="https://www.youtube.com/embed/h4Tj_aT6fEc" style="position: absolute; width: 100%; height: 100%; top: 0; left: 0;" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe></div></div></figure><h2>Data and Software used:</h2><ul><li>Raw data from <a href="https://www.ebi.ac.uk/empiar/EMPIAR-10950/">EMPIAR-10950</a> (<a href="https://doi.org/10.1038/s41598-023-29396-0">Fujita et al., 2023</a>)</li><li><a href="https://relion.readthedocs.io/en/release-5.0/">RELION-5</a> and ModelAngelo (<a href="https://doi.org/10.1038/s41586-024-07215-4">Jamali et al., 2024</a>)</li><li><a href="https://cryosparc.com/">CryoSPARC</a> (<a href="https://doi.org/10.1038/nmeth.4169">Punjani et al., 2017</a>)</li><li><a href="https://www2.mrc-lmb.cam.ac.uk/personal/pemsley/coot/">Coot</a> (<a href="http://journals.iucr.org/d/issues/2010/04/00/ba5144/ba5144.pdf">Emsley et al., 2010</a>)</li></ul><h3>DECTRIS CLOUD environments</h3><ul><li>Globus Data Transfer v0</li><li>CryoSPARC v1</li><li>RELION-5.0 v0</li><li>CCP4 &amp; XDS &amp; PyMOL GUI v1</li></ul><h2>Data upload:</h2><ul><li>Create a new project and start a session using the Globus environment</li><li>Follow the instructions of the <a href="https://help.dectris.cloud/en/article/upload-via-globus">help page</a> to set up and then click on <code>Browse Globus</code> button on the <a href="https://www.ebi.ac.uk/empiar/EMPIAR-10950/" target="_blank" rel="noopener noreferrer">EMPIAR-10950</a> dataset page within the Session.</li><li>Download the dataset to <code>/dectris_data/path_to_your_project/raw/</code>.</li><li>After the download is completed, check if the files are present via the DECTRIS CLOUD data browser.</li></ul><h2>Processing steps:</h2><ul><li>Generate the gain reference from the raw movies by running the&nbsp;<code> Calculate Gain Reference</code> public job template on the raw folder of your project. This job does not require a GPU.</li><li>Create a Session using the <code>CryoSPARC</code> Environment. Make sure the node has at least 1 GPU.</li><li>Follow the standard CryoSPARC workflow. You can find a general tutorial <a href="https://guide.cryosparc.com/processing-data/get-started-with-cryosparc-introductory-tutorial" target="_blank" rel="noopener noreferrer">here</a>.</li><li>Download the final map and copy it to a <code>/dectris_data/path_to_your_project/</code> subfolder.</li><li>To build the atomic model automatically, run the <code>ModelAngelo</code> public job template using the final map as input. This job requires a GPU.</li><li>Create a new Session using the <code>CCP4 &amp; XDS &amp; PyMOL GUI</code> Environment, open a terminal and run <code>coot</code>, load your map and model to inspect the result.</li></ul>&nbsp;

<figure class="media"><div data-oembed-url="https://youtu.be/nEofKBfTk6Y"><div style="position: relative; padding-bottom: 100%; height: 0; padding-bottom: 56.2493%;"><iframe src="https://www.youtube.com/embed/nEofKBfTk6Y" style="position: absolute; width: 100%; height: 100%; top: 0; left: 0;" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen=""></iframe></div></div></figure><h2>Data and software used</h2><ul><li>Sinogram data from: Vamvakeros, A., Matras, D., Ashton, T. E., Dong, H., Bauer, D., Odarchenko, Y., Price, S. W. T., Butler, K. T., Gutowski, O., Dippel, A.-C., von Zimmermann, M., Darr, J., Jacques, S. D. M., &amp; Beale, A. M. (2021). Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries: Datasets [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.5172980">https://doi.org/10.5281/zenodo.5172980</a></li><li>Paper based on example data: A. Vamvakeros, D. Matras, T. E. Ashton, A. A. Coelho, H. Dong, D. Bauer, Y. Odarchenko, S. W. T. Price, K. T. Butler, O. Gutowski, A. C. Dippel, M. v. Zimmerman, J. A. Darr, S. D. M. Jacques, A. M. Beale, Cycling Rate-Induced Spatially-Resolved Heterogeneities in Commercial Cylindrical Li-Ion Batteries. Small Methods 2021, 5, 2100512. <a href="https://doi.org/10.1002/smtd.202100512">https://doi.org/10.1002/smtd.202100512</a></li><li><a href="https://imagej.net/software/fiji/" target="_blank" rel="noopener noreferrer">Fiji (ImageJ)</a>: Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., … Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. <a href="https://doi.org/10.1038/nmeth.2019" target="_blank" rel="noopener noreferrer">doi:10.1038/nmeth.2019</a></li><li><a href="https://ndtomo.readthedocs.io/en/master/" target="_blank" rel="noopener noreferrer">nDTomo</a>: Vamvakeros, A., Papoutsellis, E., Dong, H., Docherty, R., Beale, A.M., Cooper, S.J., Jacques, S.D.M.J., nDTomo: A Python-Based Software Suite for X-ray Chemical Imaging and Tomography, 2025, <a href="https://doi.org/10.26434/chemrxiv-2025-h5t2x">https://doi.org/10.26434/chemrxiv-2025-h5t2x</a></li><li><a href="https://advancedphotonsource.github.io/GSAS-II-tutorials/" target="_blank" rel="noopener noreferrer">GSAS-II</a> Toby, B. H., &amp; Von Dreele, R. B. (2013). “GSAS-II: the genesis of a modern open-source all purpose crystallography software package”. Journal of Applied Crystallography, 46(2), 544-549. <a href=" https://doi.org/10.1107/S0021889813003531" target="_blank" rel="noopener noreferrer">doi:10.1107/S0021889813003531</a></li></ul><h2>DECTRIS CLOUD environments</h2><ul><li>Fiji (ImageJ) v0</li><li><a href="https://help.dectris.cloud/en/article/ndtomo" target="_blank" rel="noopener noreferrer">nDTomo</a> v0</li><li>nDTomo Jupyter Notebook v0</li><li><a href="https://help.dectris.cloud/en/article/gsas-ii" target="_blank" rel="noopener noreferrer">GSAS-II</a> v1</li></ul><h2>Processing steps</h2><h3>1. Project creation and data upload</h3>Create a project by navigating to Data → My Projects → Add Project + and fill out the metadata:<figure class="image"><img src="https://cdn-assets.productfruits.com/cb8829b0-e149-498b-acc0-c62e9f3a0e5d"></figure>Navigate inside the project space and upload the following .h5 files from the <a href="https://zenodo.org/records/5172980" target="_blank" rel="noopener noreferrer">zenodo repository</a>:<figure class="image"><img src="https://cdn-assets.productfruits.com/b436b9ba-1eeb-43da-8118-7b6a31af0a5e"></figure><h3>2. Data visualization in ImageJ</h3>To look at the data with ImageJ, you can use the public Fiji (ImageJ) environment to start a session.To start a session, navigate to Analysis → Public Environments and search for Fiji. Click on the Fiji environment card and then START SESSION in the new panel that pops out from the side.&nbsp;On the following page, the Fiji environment will be preselected. Configure your session by choosing a name, local software disk (space needed for the installed software, not the data in the cloud, for Fiji 16 GB is enough), and the sponsor.&nbsp;Finally choose the performance in the right panel before clicking NEXT STEP. This brings you to a page where you can select your created project.<figure class="image"><img src="https://cdn-assets.productfruits.com/d9228c29-70bf-45dd-80da-6394e17037d6"></figure>Once inside the virtual machine, you can start ImageJ by going to the the Fiji.app folder in the home directory:<figure class="image"><img src="https://cdn-assets.productfruits.com/08f3c902-cce0-410a-8ace-cb1ab1c05c4c"></figure>When opening the files with the hdf5 reader plugin, you can find them by navigating to the Desktop and going along the <code>/dectris_data/</code> path.<h3>3. Preprocessing with nDTomo in a python environment</h3>To process the sinograms, you can start a session with the nDTomo Jupyter environment.<figure class="image"><img src="https://cdn-assets.productfruits.com/e54e4e44-8e04-4feb-8c45-a46241a33722"></figure>Within the session, you can start a new notebook and use the following code for processing the sinograms. Feel free to experiment with improving the preprocessing steps:<pre><code class="language-python">import argparse, pathlib, h5py, numpy as np, matplotlib
import matplotlib.pyplot as plt
from nDTomo.tomo.sinograms import (
 airrem, scalesinos, sinocomcor, sinocentering
)
from nDTomo.tomo.conv_tomo import filter_sinogram, backproject
from pathlib import Path</code></pre>Import your uploaded sinogram data from your project. Make sure to adjust the path to match the mounted file path of your project:<pre><code class="language-python">with h5py.File("/dectris_data/CAMLA/2025/82169/raw/Pristine.h5", "r") as f:
 raw = f["data"][:] 
 q = f["q"][:]
stack = np.moveaxis(raw, (0, 1, 2), (2, 1, 0)) # (x, θ, q)
theta = np.linspace(0, 180,400, endpoint=False)</code></pre>The <code>moveaxis</code> command is used to make sure that the data is arranged in the way that nDTomo expects.We can now perform the preprocessing steps:<pre><code class="language-python">q_idx = 500 # Choose an index along the stack to visualize when doing corrections
plt.imshow(stack[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'1. Raw Sinogram, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = airrem(stack, ofs=2, method="both", coli=0, colf=2)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'2. airrem, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = scalesinos(sn)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'3. scalesinos, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = sinocomcor(sn, interp=False)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'4. sinocomcor, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()

sn = sinocentering(sn, crsr=20, interp=True, scan=180, channels=None, pbar=False)
plt.imshow(sn[:, :, q_idx], aspect='auto', cmap='inferno')
plt.ylabel('translation step')
plt.xlabel('rotation step')
plt.title(f'5. sinocentering, q = {q[q_idx]:.3f} Å⁻¹')
plt.show()</code></pre>Perform reconstruction of just a single slice:<pre><code class="language-python">sino = sn[:, :, q_idx]
sino_f = filter_sinogram(sino, filter_type="Ramp")
test = backproject(sino_f, theta)
plt.imshow(test)</code></pre>Once satisfied with the above test, you can loop over all q-indices and make a reconstruction for each:<pre><code class="language-python">nx, ntheta, nq = sn.shape 
vol = np.empty((nx, nx, nq), dtype="float32")

for j in range(nq):
 sino = sn[:, :, j] 
 sino_f = filter_sinogram(sino, filter_type="Ramp")
 vol[:, :, j] = backproject(sino_f, theta)
 if j % 100 == 0:
 print(f"reconstructed {j}/{nq}")</code></pre>Make sure to save your reconstruction results in a .h5 file, if you want to continue the analysis within a different environment. In this case, the .h5 file is saved to the work directory of my project. Make sure to adjust the path to your own personal project folder:<pre><code class="language-python">with h5py.File('/dectris_data/CAMLA/2025/82169/work/simple_rc_battery.h5', 'w') as hf:
 hf.create_dataset('q', data=q)
 hf.create_dataset('data', data=vol)</code></pre><h3>4. Starting the nDTomo GUI</h3>If you want to proceed the analysis using the nDTomo GUI, you can start up the nDTomo environment. This environment differ from the previous one by being a system environment, meaning that you within the virtual machine will have access to an Ubuntu system, where the nDTomo components are installed.<figure class="image"><img src="https://cdn-assets.productfruits.com/7ee39acb-fdde-4961-b1b4-edffa54e8bbc"></figure><h3>5. Configuring a job to run batch refinements with GSAS-II</h3>As a final step, we will perform a Rietveld refinement of every spectrum in the .h5 file.&nbsp;This can be done by making use of the public <a href="https://help.dectris.cloud/en/article/gsas-ii-batch-refinement-battery" target="_blank" rel="noopener noreferrer">GSAS-II batch refinement battery</a> job template created for this use case.&nbsp;To run the job, we need to provide a path to the reconstructed .h5 file from the previous step, as well as a path to a folder containing the .cif files for the phases we are refining (Al, Cu, Fe).Before running the job, we therefore upload the files <code>al.cif</code>, <code>cu.cif</code> and <code>fe.cif</code> to the work folder in our project. The GSAS-II refinement job will look for these specific files when starting the refinement.<figure class="image"><img src="https://cdn-assets.productfruits.com/f180d542-4615-4d49-8bf6-8cfa88e4944e"></figure>Once this is done, you can access and run the job template by going to Analysis → Public Job Templates and choosing the <a href="https://help.dectris.cloud/en/article/gsas-ii-batch-refinement-battery" target="_blank" rel="noopener noreferrer">GSAS-II batch refinement battery</a> job template:<figure class="image"><img src="https://cdn-assets.productfruits.com/b4e4d4b8-c9b1-47c2-a722-d0cfa4273224"></figure>After running it, you should see the following output:<figure class="image"><img src="https://cdn-assets.productfruits.com/522bac1a-b079-4d35-ba6f-bb8e5521b4cd"></figure>The job template attempts to parallelize the refinements, meaning that the running time should decrease with more powerful machine types. The above results were achieved with a Hypersonic node (64 CPUs) and took 33 minutes, while a test run with the Turbo node (32 CPUs) took 51 minutes.<h3>6. Getting access to the demonstration project</h3>If you want to be added to the example project for the video, so you do not have to run the full reconstruction yourself, you can contact us at <a href="mailto:support@dectris.cloud">support@dectris.cloud</a>

<h2 class="LexTKB__h2" dir="ltr">Job Template &amp; Execution</h2>To run ORCA calculations at scale and automate DFT jobs in DECTRIS D.LAB, you need to define a Job Template. This template acts as a blueprint that combines your ORCA environment, execution logic, and adjustable parameters.You can use the provided examples to help you build your fully custom template. The Job Template here consists of a Bash execution script that handles the ORCA/OpenMPI environment and given input variables that can be configured manually in the UI, or with a json input.<h3 class="LexTKB__h3" dir="ltr">1. Initialize the Template</h3><ul class="LexTKB__ul"><li value="1" class="LexTKB__listItem">Name &amp; Description: Start by creating a new job template. Give it a scannable name (e.g., <code spellcheck="false" style="white-space: pre-wrap;">ORCA_DFT_Optimization</code>) and a description that notes the ORCA and OpenMPI versions used.</li><li value="2" class="LexTKB__listItem">Select Environment: Select the ORCA Docker environment as the base for this job. This ensures all math libraries and MPI runtimes are available. (If you do not have an ORCA environment, see instructions on how to build it <a href="https://help.dectris.cloud/en/article/setting-up-orca-for-dft-calculations" target="_blank" rel="noreferrer" class="LexTKB__link">HERE</a>)</li></ul><h3 class="LexTKB__h3" dir="ltr">2. Define Input Variables</h3>Set up variables to allow for easy iteration without modifying the underlying script. Example Variables Configuration (JSON):In order to configure variables, you can either declare them using the Job Template creation UI, or using json format. An example of variable declaration is shown below:<pre class="LexTKB__code" spellcheck="false"><pre class="LexTKB__code" spellcheck="false">{
 "input_variables": {
 "DC_ORCA_INPUT": {
 "default_value": "",
 "type": "path",
 "unit": ""
 },
 "DC_NUM_THREADS": {
 "default_value": -1,
 "type": "number",
 "unit": ""
 },
 "DC_NP": {
 "default_value": 0,
 "type": "number",
 "unit": ""
 },
 "DC_PARAMS": {
 "default_value": "--mca btl",
 "type": "string",
 "unit": ""
 }
 }
}</pre></pre>The table below shows examples of variables you may want to declare if you need to tweak them when you run the DFT calculation job. (The table below was built in accordance with the bash instruction provided in section 3. Your variables and synthax may differ depending on your instructions file.)<table class="LexTKB__table"><colgroup><col><col><col><col></colgroup><tbody><tr><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Variable</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Type</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Default</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Description</td></tr><tr><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">DC_ORCA_INPUT</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Path</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">(empty)</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">The path to your ORCA <code spellcheck="false" style="white-space: pre-wrap;">.inp</code> file.</td></tr><tr><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">DC_NUM_THREADS</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Number</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">-1</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">OpenMP threads (use <code spellcheck="false" style="white-space: pre-wrap;">-1</code> for all available cores).</td></tr><tr><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">DC_NP</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Number</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">0</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Number of MPI ranks (set to <code spellcheck="false" style="white-space: pre-wrap;">0</code> for serial runs).</td></tr><tr><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">DC_PARAMS</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">String</td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;"><code spellcheck="false" style="white-space: pre-wrap;">--mca btl</code></td><td class="LexTKB__tableCell" style="vertical-align: top; text-align: start;">Additional flags for the <code spellcheck="false" style="white-space: pre-wrap;">mpirun</code> command.</td></tr></tbody></table><h3 class="LexTKB__h3" dir="ltr">3. Build the Instructions File</h3>The Instructions File is the Bash script that orchestrates the calculation. You can configure your custom bash script as suits you best. Below you find and example, that you may copy and paste into the template's instruction block. This script is designed to handle ORCA path discovery and output collection automatically.<pre class="LexTKB__code" spellcheck="false"><pre class="LexTKB__code" spellcheck="false">#!/usr/bin/env bash
set -euo pipefail


# -------------------------------
# Required environment variables
# -------------------------------
: "${DC_ORCA_INPUT:?DC_ORCA_INPUT is not set}"
: "${JOB_OUTPUT_DIR:?JOB_OUTPUT_DIR is not set}"


# -------------------------------
# Optional environment variables
# -------------------------------
DC_NUM_THREADS="${DC_NUM_THREADS:--1}" # -1 =&gt; use all cores
DC_NP="${DC_NP:-0}" # 0 =&gt; no MPI
DC_PARAMS="${DC_PARAMS:-}" # extra mpirun params (string)
DC_ORCA_OUTPUT="${DC_ORCA_OUTPUT:-}" # optional override; if empty -&gt; derived from input


# -------------------------------
# Resolve input/output
# -------------------------------
INP="${DC_ORCA_INPUT}"
BASENAME="$(basename "${INP}")"
JOBNAME="${BASENAME%.inp}"


# Output log naming (optional override)
if [[ -n "${DC_ORCA_OUTPUT}" ]]; then
 OUT="${DC_ORCA_OUTPUT}"
else
 if [[ "${BASENAME}" == *.inp ]]; then
 OUT="${JOBNAME}.log"
 else
 OUT="${BASENAME}.log"
 JOBNAME="${BASENAME}" # so we match outputs like "foo.log", "foo.gbw", etc.
 fi
fi


# -------------------------------
# Resolve thread count
# -------------------------------
if [[ "${DC_NUM_THREADS}" == "-1" ]]; then
 NUM_THREADS="$(nproc)"
else
 NUM_THREADS="${DC_NUM_THREADS}"
fi
export OMP_NUM_THREADS="${NUM_THREADS}"


# -------------------------------
# ORCA + MPI locations (from container)
# -------------------------------
ORCA_HOME="${ORCA_HOME:-/opt/orca}"
OMPI_HOME="${OMPI_HOME:-/opt/openmpi}"


# Find ORCA executable robustly (handles /opt/orca/orca_6_1_0/orca, etc.)
EXEC="${ORCA_HOME}/orca"
if [[ ! -x "${EXEC}" ]]; then
 # Common layout in your image: /opt/orca/orca_6_1_0/orca
 if [[ -x "${ORCA_HOME}/orca_6_1_0/orca" ]]; then
 EXEC="${ORCA_HOME}/orca_6_1_0/orca"
 else
 # Generic fallback: search a few levels deep
 EXEC="$(find "${ORCA_HOME}" -maxdepth 4 -type f -name orca -perm -111 2&gt;/dev/null | head -n 1 || true)"
 fi
fi


# -------------------------------
# Validation (ORCA + input)
# -------------------------------
if [[ -z "${EXEC}" || ! -x "${EXEC}" ]]; then
 echo "ERROR: ORCA not found or not executable under ORCA_HOME=${ORCA_HOME}" &gt;&amp;2
 ls -lah "${ORCA_HOME}" &gt;&amp;2 || true
 exit 1
fi


if [[ ! -f "${INP}" ]]; then
 echo "ERROR: Input file not found: ${INP}" &gt;&amp;2
 exit 1
fi


mkdir -p "${JOB_OUTPUT_DIR}"


# Set PATH/LD_LIBRARY_PATH based on where ORCA was found
ORCA_BIN_DIR="$(dirname "${EXEC}")"
ORCA_LIB_DIR="${ORCA_BIN_DIR}/lib"


export PATH="${ORCA_BIN_DIR}:${OMPI_HOME}/bin:${PATH}"
export LD_LIBRARY_PATH="${ORCA_LIB_DIR}:${OMPI_HOME}/lib:${LD_LIBRARY_PATH:-}"


# -------------------------------
# PARAMS handling (string -&gt; array)
# -------------------------------
NP="${DC_NP}"
PARAMS="${DC_PARAMS}"
PARAM_ARR=()
if [[ -n "${PARAMS}" ]]; then
 read -r -a PARAM_ARR &lt;&lt;&lt; "${PARAMS}"
fi


# -------------------------------
# Execution
# -------------------------------
echo "======================================"
echo "Running ORCA"
echo " ORCA executable : ${EXEC}"
echo " Input : ${INP}"
echo " Output (stdout) : ${OUT}"
echo " Job name prefix : ${JOBNAME}"
echo " Threads : ${OMP_NUM_THREADS}"
echo " MPI ranks (NP) : ${NP}"
echo " MPI params : ${PARAMS}"
echo " Job output dir : ${JOB_OUTPUT_DIR}"
echo "======================================"


if [[ "${NP}" -gt 0 ]]; then
 mpirun -np "${NP}" "${PARAM_ARR[@]}" "${EXEC}" "${INP}" &gt; "${OUT}"
else
 "${EXEC}" "${INP}" &gt; "${OUT}"
fi


echo "ORCA finished. Collecting outputs..."


# -------------------------------
# Collect outputs: copy all files that start with JOBNAME*
# (covers .gbw, .engrad, .xyz, .hess, etc.)
# -------------------------------
shopt -s nullglob
moved_any=0
for f in "${JOBNAME}"*; do
 if [[ -e "${f}" ]]; then
 cp -a "${f}" "${JOB_OUTPUT_DIR}/"
 moved_any=1
 fi
done
shopt -u nullglob


# Always ensure the main log is present in the output dir
cp -a "${OUT}" "${JOB_OUTPUT_DIR}/" || true


if [[ "${moved_any}" -eq 0 ]]; then
 echo "WARNING: No files matched '${JOBNAME}*' in the working directory." &gt;&amp;2
 echo "Contents of current directory:" &gt;&amp;2
 ls -lah &gt;&amp;2 || true
fi


echo "Done."
echo "Outputs copied to: ${JOB_OUTPUT_DIR}"
echo " - ${JOB_OUTPUT_DIR}/$(basename "${OUT}")" </pre></pre><h3 class="LexTKB__h3" dir="ltr">4. Upload Supporting Files</h3>If your calculation requires external files, such as specific Basis Set files, XYZ coordinates not included in the <code spellcheck="false" style="white-space: pre-wrap;">.inp</code>, or Hessian files for restarts, upload them to your Project/Experiment directory or as the job's supporting files. Ensure the paths specified in your <code spellcheck="false" style="white-space: pre-wrap;">.inp</code> file match the location of these uploaded files.<h3 class="LexTKB__h3" dir="ltr">Important Notes</h3><ul class="LexTKB__ul"><li value="1" class="LexTKB__listItem">Parallelization Matching: Ensure the <code spellcheck="false" style="white-space: pre-wrap;">%pal nprocs X end</code> block in your ORCA input file matches the <code spellcheck="false" style="white-space: pre-wrap;">DC_NP</code> variable in your job template.</li><li value="2" class="LexTKB__listItem">Memory Management: ORCA memory (<code spellcheck="false" style="white-space: pre-wrap;">%maxcore</code>) is requested per MPI rank. Ensure your total memory usage (MaxCore × NP) does not exceed the hardware limits of the allocated node.</li><li value="3" class="LexTKB__listItem">Log Files: The main log file is automatically saved as <code spellcheck="false" style="white-space: pre-wrap;">{your_input_name}.log</code> in the output directory for easy review.</li></ul> <h4 class="LexTKB__h4" dir="ltr">ORCA REFERENCE Neese, F., Wennmohs, F., Becker, U., &amp; Riplinger, C.&nbsp;(2020). The ORCA quantum chemistry program package.&nbsp;The Journal of Chemical Physics, 152(22), 224108. <a href="https://doi.org/10.1063/5.0004608" class="LexTKB__link">https://doi.org/10.1063/5.0004608</a></h4> Important: The ORCA installer is not provided by DECTRIS due to licensing restrictions. ORCA is distributed under a specific agreement that you must comply, and its redistribution is not permitted in DECTRIS CLOUD.<h3 class="LexTKB__h3" dir="ltr"> </h3>

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