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| Format: | Recurso digital |
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Zenodo
2024
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| Online Access: | https://doi.org/10.5281/zenodo.15129133 |
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Table of Contents:
- <p>This dataset contains the raw data and plotting code used in the study titled: "<a href="https://doi.org/10.1039/d4fd00071d">Force and stress calculations with a neural-network wave function for solids</a>".</p> <p>The relevant code is available at <a href="https://github.com/bytedance/netobs">https://github.com/bytedance/netobs</a>.</p> <h2>Data Files</h2> <h3>Directory structure</h3> <ul> <li><code>deepsolid-force/</code>: Data files for calculating forces. <ul> <li><code>Graphene/{X}</code> contains training curves and forces by different estimators for 1x1x1 graphene cell, with one carbon atom stretched X angstroms.</li> <li><code>LiH_TRI/{X}</code> contains forces by different estimators for 1x1x1 LiH cell, with the hydrogen atom displaced along the diagonal line for X Bohr.</li> <li><code>LiH_NU/{X}</code> is similar to <code>LiH_TRI/{X}</code> but uses the NU feature instead of the TRI feature mentioned in the paper. <ul> <li><code>fs</code> means the function f is using the triangular version and <code>f0</code> means it's using the original version. Same for function g.</li> </ul> </li> </ul> </li> <li><code>deepsolid-stress/</code>: Data files for calculating stress <ul> <li><code>LiH/{X}</code> contains pressure and stress data for 1x1x1 LiH cell with lattice constant equals to X angstrom.</li> </ul> </li> </ul> <h3>File format</h3> <p>Raw force/stress data are stored in files named <code>netobs_ckpt_xxxxxx.npz</code>. Forces are stored in <code>digest/force</code> fields of the <code>npz</code> file and stresses are stored in <code>values/value</code> field.</p> <p>The training curves are stored in <code>train_stats.csv</code> files.</p> <h2>Code for Plotting Figures</h2> <ul> <li><code>prep_data.py</code>: Script to process data and create CSVs in <code>digest/</code>. <ul> <li>The easiest way to run it is using command <code>uv run prep_data.py</code></li> </ul> </li> <li><code>plot.ipynb</code>: Jupyter notebook for generating the plots. <ul> <li>The easiest way to run it is using command <code>uvx --with numpy,pandas,lmfit,matplotlib,git+https://github.com/AllanChain/acplot jupyter lab plot.ipynb</code><br><br></li> </ul> </li> </ul>