✏️ change version to v0.0.2

.zenodo.json

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 {
     "title": "Energy-GNoME",
-    "version": "0.0.1",
+    "version": "0.0.2",
     "description": "<h1 align=\"center\">\nEnergy-GNoME\n</h1>\n<p align=\"center\">\n<a href=\"https://squidfunk.github.io/mkdocs-material/\"> <img src=\"https://raw.githubusercontent.com/paolodeangelis/Energy-GNoME/main/docs/assets/img/logo.png\" width=\"250\" alt=\"Material for MkDocs\"> </a>\n</p>\n<p align=\"center\">\n<strong> AI-Driven Screening and Prediction for Selected <a href=\"https://paolodeangelis.github.io/Energy-GNoME\">Advanced Energy Materials</a> </strong>\n</p>\n<p>This repository contains the database, documentation, Python library (coming soon), and notebooks used to build the Energy-GNoME database.</p>\n<p>The purpose of this repository is to enable reproducibility and, more importantly, to support the continuous integration of your data points for model training, as the database is designed as a <em>living</em> database.</p>\n<p>For further details, refer to the associated article:</p>\n<blockquote>\n<p>De Angelis P., Trezza G., Barletta G., Asinari P., Chiavazzo E. “Energy-GNoME: A Living Database of Selected Materials for Energy Applications”. arXiv, November 15, 2024. doi: <a href=\"https://doi.org/10.48550/arXiv.2411.10125\">10.48550/arXiv.2411.10125</a>.</p>\n</blockquote>\n<h2 id=\"how-to-cite\">How to cite</h2>\n<p>If you find this project valuable, please consider citing the following pre-print work:</p>\n<blockquote>\n<p>De Angelis P., Trezza G., Barletta G., Asinari P., Chiavazzo E. “Energy-GNoME: A Living Database of Selected Materials for Energy Applications”. <em>arXiv</em> November 15, <strong>2024</strong>. doi: <a href=\"https://doi.org/10.48550/arXiv.2411.10125\">10.48550/arXiv.2411.10125</a>.</p>\n</blockquote>\n<div class=\"sourceCode\" id=\"cb1\"><pre class=\"sourceCode bibtex\"><code class=\"sourceCode bibtex\"><span id=\"cb1-1\"><a href=\"#cb1-1\" aria-hidden=\"true\"></a><span class=\"va\">@misc</span>{<span class=\"ot\">deangelis_energy</span>-<span class=\"ot\">gnome:_2024</span>,</span>\n<span id=\"cb1-2\"><a href=\"#cb1-2\" aria-hidden=\"true\"></a>    <span class=\"dt\">title</span> = {Energy-{GNoME}: {A} {Living} {Database} of {Selected} {Materials} for {Energy} {Applications}},</span>\n<span id=\"cb1-3\"><a href=\"#cb1-3\" aria-hidden=\"true\"></a>    <span class=\"dt\">shorttitle</span> = {Energy-{GNoME}},</span>\n<span id=\"cb1-4\"><a href=\"#cb1-4\" aria-hidden=\"true\"></a>    <span class=\"dt\">url</span> = {http://arxiv.org/abs/2411.10125},</span>\n<span id=\"cb1-5\"><a href=\"#cb1-5\" aria-hidden=\"true\"></a>    <span class=\"dt\">doi</span> = {10.48550/arXiv.2411.10125},</span>\n<span id=\"cb1-6\"><a href=\"#cb1-6\" aria-hidden=\"true\"></a>    <span class=\"dt\">abstract</span> = {Artificial Intelligence (AI) in materials science is driving significant advancements in the discovery of advanced materials for energy applications. The recent GNoME protocol identifies over 380,000 novel stable crystals. From this, we identify over 33,000 materials with potential as energy materials forming the Energy-GNoME database. Leveraging Machine Learning (ML) and Deep Learning (DL) tools, our protocol mitigates cross-domain data bias using feature spaces to identify potential candidates for thermoelectric materials, novel battery cathodes, and novel perovskites. Classifiers with both structural and compositional features identify domains of applicability, where we expect enhanced accuracy of the regressors. Such regressors are trained to predict key materials properties like, thermoelectric figure of merit (zT), band gap (Eg), and cathode voltage (<span class=\"ch\">\\$</span>{<span class=\"ch\">\\textbackslash</span>}Delta V<span class=\"ch\">\\_</span>c<span class=\"ch\">\\$</span>). This method significantly narrows the pool of potential candidates, serving as an efficient guide for experimental and computational chemistry investigations and accelerating the discovery of materials suited for electricity generation, energy storage and conversion.},</span>\n<span id=\"cb1-7\"><a href=\"#cb1-7\" aria-hidden=\"true\"></a>    <span class=\"dt\">urldate</span> = {2024-12-03},</span>\n<span id=\"cb1-8\"><a href=\"#cb1-8\" aria-hidden=\"true\"></a>    <span class=\"dt\">publisher</span> = {arXiv},</span>\n<span id=\"cb1-9\"><a href=\"#cb1-9\" aria-hidden=\"true\"></a>    <span class=\"dt\">author</span> = {De Angelis, Paolo and Trezza, Giovanni and Barletta, Giulio and Asinari, Pietro and Chiavazzo, Eliodoro},</span>\n<span id=\"cb1-10\"><a href=\"#cb1-10\" aria-hidden=\"true\"></a>    <span class=\"dt\">month</span> = <span class=\"st\">nov</span>,</span>\n<span id=\"cb1-11\"><a href=\"#cb1-11\" aria-hidden=\"true\"></a>    <span class=\"dt\">year</span> = {2024},</span>\n<span id=\"cb1-12\"><a href=\"#cb1-12\" aria-hidden=\"true\"></a>    <span class=\"dt\">note</span> = {arXiv:2411.10125},</span>\n<span id=\"cb1-13\"><a href=\"#cb1-13\" aria-hidden=\"true\"></a>    <span class=\"dt\">keywords</span> = {Condensed Matter - Materials Science, Condensed Matter - Other Condensed Matter, Computer Science - Machine Learning},</span>\n<span id=\"cb1-14\"><a href=\"#cb1-14\" aria-hidden=\"true\"></a>}</span></code></pre></div>\n",
     "creators": [
         {