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Research data for "Device-scale atomistic modelling of phase-change memory materials"

<p>This is a dataset related to the publication &quot;Device-scale atomistic modelling of phase-change memory materials&quot;.</p> <p>Two folders have been provided for&nbsp;(1) the production data shown in this work, and (2) GAP models trained in this work:</p> <p>(1) The production data have been categorised according to the main text figures:</p> <ul> <li>&quot;reference_database&quot;: &nbsp;Reference databases (i.e.,&nbsp;training structures) of three GAP models discussed in this work. The structure data are provided in (extended) XYZ format, as labelled using either the PBEsol or the PBE functional. <ul> <li>&quot;main_GST-GAP-22_PBEsol&quot;: the main&nbsp;GST-GAP-22&nbsp;database, which was fitted using a two-step iterative training protocol. The resulting GAP model was used to obtain the results shown in the main text. The reference database is visualised in Fig. 1 of the main text.&nbsp;</li> <li>&quot;refitted_GST-GAP-22_PBE&quot;: this dataset contains the same structures as&nbsp;the original GST-GAP-22 training data, with all structures having been relabelled using the PBE functional.</li> <li>&quot;extended_GST-GAP-22_for_efield_PBEsol&quot;: an extension of the GST-GAP-22 database to a new, task-specific application, i.e.,&nbsp;electromigration under an external electric field (cf. Extended Data Fig. 4).</li> </ul> </li> </ul> <ul> <li>&quot;crystallization_simulations&quot;: three trajectories for the crystallization simulations shown in Fig. 2, of which all were obtained from GAP-MD. <ul> <li>&quot;fig2b_growth_GAP-MD&quot;: Growth of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>&quot;fig2c_cumulative_set_cycles_GAP-MD&quot;: Cumulative set process&nbsp;of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>&quot;fig2d_crystallization_12096at_GAP-MD&quot;: Crystallization of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (12096 atoms), in which three crystalline seeds were used.</li> </ul> </li> </ul> <ul> <li>&quot;RESET_mushroom_model&quot;: two non-isothermal simulations shown in Fig. 3, obtained from GAP-MD. <ul> <li>&quot;Fig3b_small_pulse&quot;: The 70 ps NVE equilibrium process after a small heating pulse was imposed in the focal area, giving an excess kinetic energy of 1,650 eV for the atoms in the focal area.</li> <li>&quot;Fig3d_large_pulse&quot;: The 70 ps NVE equilibrium process after a large heating pulse was imposed in the focal area, giving an excess kinetic energy of 3,900 eV for the atoms in the focal area.</li> </ul> </li> </ul> <ul> <li>&quot;RESET_device_scale_simulations&quot;: the GAP-MD&nbsp;simulations of the melting and heat dissipation process of a device-scale structural model shown in Fig. 4. <ul> <li>&quot;Fig4b_device_scale<strong>_</strong>heating_10ps&quot;: The melting process of the device-scale model over 10 ps.</li> <li>&quot;Fig4c_device_scale<strong>_</strong>cooling_40ps&quot;: The heat dissipation process of the device-scale model over another 40 ps.</li> </ul> </li> </ul> <p>(2) The GAP models trained in this work:</p> <ul> <li>&quot;main_GAP_potential&quot;: the main GAP model used for the production data of this work, which is fitted based on PBEsol data.&nbsp;The XML identifier of this GAP model is&nbsp;GAP_2022_4_7_480_18_6_12_970.</li> </ul> <ul> <li>&quot;other_GAP_potentials&quot;: two derivatives of the original GAP model. <ul> <li>&quot;refitted_GST-GAP-22_PBE&quot;: using the same reference structures as the original GAP model but&nbsp;re-labelled using the PBE functional.&nbsp;The XML identifier of this GAP model is&nbsp;GAP_2022_5_7_480_0_58_2_26.</li> <li>&quot;extended_GST-GAP-22_for_efield_PBEsol&quot;:&nbsp;An extension of the original GAP model to a new, task-specific application, i.e.,&nbsp;electromigration under an external electric field.&nbsp;The XML identifier of this GAP model is&nbsp;GAP_2023_3_19_480_18_14_10_174.</li> </ul> </li> </ul> <p>&nbsp;</p>

ShareScore

24/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
16
Reuse readiness
0
Engagement
0