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3 results for “CC BY-NC-SA”
Supplementary data (CC BY-NC-SA 4.0): A reactive neural network framework for water-loaded acidic zeolites
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p><p>This dataset provides supplementary data to "A reactive neural network framework for water-loaded acidic zeolites". It contains trained Neural Network Potentials (NNP and ΔNNP model), scripts, and all energy and force data used in this work at the (Δ)NNP, ReaxFF, and DFT (SCAN+D3(BJ) and ωB97X-D3(BJ)) level. Energy and forces are stored as ASE trajectory files (traj), readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE). In addition, this repository contains the generated training database with DFT (SCAN+D3(BJ)) energies and forces as SchNetPack1.0 database (SiAlOH.db) file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>.</p><ol><li>"aimd_simulations.zip" - VASP INCAR file, XDATCAR and traj file for 10 ps AIMD run (Supplementary Figure 6) and NNP level (re-)calculated energies/forces ("aimd_nnp_recalc.traj")</li><li>"biased_dynamics.zip" - VASP/Plumed input and output files for DFT (SCAN+D3(BJ)) and NNP level biased dynamics including traj files (Supplementary Figure 12)</li><li>"database_input.zip" - structure (cif) files of the initial structures used for database generation (Supplementary Table 1)</li><li>"delta_nnp.zip" - (pytorch) ΔNNP model (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>) together with example scripts </li><li>"error_stats.zip" - traj files of all generalization tests (Figure 1 and Supplementary Figure 4) storing energies/forces at the SCAN+D3(BJ), ReaxFF, and NNP level as well as traj files with ΔNNP and ωB97X-D3(BJ) energies/forces for a subset taken from biased dynamics runs (Supplementary Figure 11)</li><li>"md_simulations.zip" - NNP level MD trajectories of all generalization test (Figure 1 and Supplementary Figure 4) runs including an example script for an MD run</li><li>"neb_calculations.zip" - traj files and example scripts for NEB calculations at the (Δ)NNP along with the corresponding DFT energy/force data (SCAN+D3(BJ) and ωB97X-D3(BJ))</li><li>"nnps.zip" - (pytorch) NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)</li><li>"silica_database.zip" - output files of the single-point (SP) and optimization test runs (Supplementary Figure 1) of pure silica structures together with an example structure optimization script </li><li>"SiAlOH.db" - DFT (SCAN+D3(BJ)) training database as SchNetPack1.0 database file readable by ASE and <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li></ol>
Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <ul> <li>Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz files readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE)</li> <li>Animated gif files of Pt1 migration between double-six rings in CHA, Pt3 jump through an eight-ring in CHA, and insertion of Pt1 into a t-pen unit in MFI</li> <li>Neural Network Potential (NNP) files readable by <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> </ul>
Supplementary data (CC BY-NC-SA 4.0): Germanium Distributions in Zeolites Derived from Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <p>This dataset contains supplementary data related to the Germanosilicate Project titled <br>"Germanium Distributions in Zeolites Derived from Neural Network Potentials". <br>In various subfolders, it hosts database, simulations and post-processing calculations.<br><br>Below is a brief overview of each subfolder:</p> <ol> <li><em>Post_Processing_Calculation:</em> <br>- Contains scripts and data for post-processing calculations such as coordination numbers, <br>- Pair distribution function, and various germanium distribution metrics.<br><br></li> <li><em>NNP_Simulation_DATA:</em> <br>- Stores simulation data for different zeolite structures along with setup files for neural network potentials (NNP) simulations.<br><br></li> <li><em>NNP_files_database</em>: <br>- NNP_files: NNP model files (compatible with <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a>)<br>- GeSiO_training.db: DFT (PBE+D3(BJ)) training database as SchNetPack1.0 database file readable by <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment (ASE)</a> and SchNetPack version 1.0<br><br><em> </em></li> <li><em>DFT_vs_NNP_Data:</em> <br>- ASE traj files storing structures subsampled from MCBH runs along with energies/forces at the PBE+D3(BJ) ("*_dft.traj") and NNP level (*_nnp.traj)<br><br></li> <li><em>Zeolite_Structurers_ALL</em>: <br>- Holds data for various zeolite structures, including optimized structures for both single-cell and supercell configurations.<br><br></li> <li>GSOs_DATA:<br>- The unoptimised Global Structure Optimas (GSOs) are provided<br>- Optimised GSOs are stored inside folders for both various DFT and NNP methods</li> </ol> <p> <br>Please refer to individual readme files in each subfolder for more detailed information.</p>
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