Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models
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Data associated with the manuscript "Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models" by Daniel Wines, Tian Xie and Kamal Choudhary published in Journal of Physical Chemistry Letters (2023), <em>https://doi.org/10.1021/acs.jpclett.3c01260</em> <br> Files include: <br> jarvis_epc_data_figshare_1058.json.zip (JARVIS superconductor training data from https://www.nature.com/articles/s41524-022-00933-1) <br> all_pred_data.json.zip (CDVAE generated structures) <br> cdvae_plotting.ipynb (plotting scripts for data) <br> CDVAE_relax_DFT.zip (All relaxed DFT structure files) <br> jarvis_epc_data_cdvae.json (DFT electron-phonon coupling results for top candidate superconductors) <br> <br> <br> <br> <br>
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figshare创建时间：
2023-07-14
