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2 results for “NEGF”
Datasets for: Fullerene-based Single Molecule Diodes with Huge Rectification Ratios: A DFT-NEGF Study
<p>Input files and main output files for the study "Fullerene-based Single Molecule Diodes with Huge Rectification Ratios: A DFT-NEGF Study"</p>
Potential profile, design parameters and thermal properties of asymmetric double-barrier heterostructures based on AlGaAs simulated with NEGF+H
<p>This is a repository for assymmetric double barrier heterostructures that were simulated using the NEGF coupled with the heat equation described in <a href="https://doi.org/10.1103/PhysRevApplied.14.064022">BESCOND:Phys. Rev. Applied:2020</a>.</p> <ul> <li>Relation between the csv columns and variables:</li> </ul> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Variable</strong></td> <td><strong>Definition</strong></td> </tr> <tr> <td>1</td> <td>Lb1 [nm] </td> <td>First barrier length</td> </tr> <tr> <td>2</td> <td>LQW [nm] </td> <td>Quantum well length</td> </tr> <tr> <td>3</td> <td>Lb2 [nm] </td> <td>Second barrier length</td> </tr> <tr> <td>4</td> <td>γ</td> <td>Fraction on Al in AlGaAs alloy</td> </tr> <tr> <td>5</td> <td>V [V] </td> <td>Bias between emitter and collector</td> </tr> <tr> <td>6</td> <td>CP [W/m²] </td> <td>Cooling power</td> </tr> <tr> <td>7</td> <td>Te [K] </td> <td>Electron temperature in the Quantum well</td> </tr> <tr> <td>8</td> <td>W1 [eV] </td> <td>First activation energy</td> </tr> <tr> <td>9</td> <td>W2 [eV] </td> <td>Second activation energy</td> </tr> <tr> <td>10-1525</td> <td>PP [eV]</td> <td>Potential profile</td> </tr> </tbody> </table> <p>These data was used to feed the machine learning workflow shared in <a href="https://gitlab.citius.usc.es/modev/coolML">https://gitlab.citius.usc.es/modev/coolML</a>.</p> <p><br>This work was supported by the Spanish MICINN/AEI, Xunta de Galicia, and FEDER Funds under Grant RYC-2017-23312, Grant PID2019-104834GB-I00, Grant PID2022-141623NB-I00, Grant PID2022-142709OB-C21/PID2022-142709OA-C22, Grant ED431F 2020/008, Grant ED431C 2022/16 and GELATO ANR project (ANR-21-CE50-0017).</p>
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