Supplementary data: Accurate large-scale simulations of siliceous zeolites by neural network potentials

<strong>Content</strong> <em>1. Zeolite databases</em> Deem database containing 331170 hypothetical zeolite frameworks [Deem09, Pophale11] geometrically optimized at the NNPscan level (note, the first row of the database is alpha-quartz): "DEEM_NNPscan.db" Database of 236 exiting zeolite frameworks of the International Zeolite Association (IZA) optimized at the NNPscan level: "IZA_NNPscan.db" Both databases are ASE SQLite database files of the Atomic Simulation Environment containing the ASE Atoms objects with energies and forces (NNPscan level); readable with ASE's I/O module Additionally, relevant quantities can be extracted with, e.g., the following queries (further information: ase db --help): <pre><code class="language-bash">ase db DEEM_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy # Output id|formula|natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy 1|O6Si3 | 9|111.161|180.249| 26.988| -31.796| 3| 0.000 2|O16Si8 | 24|433.858|480.664| 18.439| -31.638| 8| 15.265 3|O16Si8 | 24|421.114|480.664| 18.997| -31.596| 8| 19.359 4|O16Si8 | 24|426.557|480.664| 18.755| -31.614| 8| 17.613 5|O16Si8 | 24|412.410|480.664| 19.398| -31.613| 8| 17.677 6|O16Si8 | 24|393.544|480.664| 20.328| -31.594| 8| 19.546 7|O16Si8 | 24|422.400|480.664| 18.939| -31.657| 8| 13.476 8|O16Si8 | 24|394.405|480.664| 20.284| -31.581| 8| 20.797 9|O12Si6 | 18|265.201|360.498| 22.624| -31.611| 6| 17.868 10|O16Si8 | 24|357.047|480.664| 22.406| -31.581| 8| 20.785 11|O16Si8 | 24|434.894|480.664| 18.395| -31.621| 8| 16.911 12|O16Si8 | 24|384.158|480.664| 20.825| -31.657| 8| 13.448 13|O12Si6 | 18|258.977|360.498| 23.168| -31.679| 6| 11.278 14|O16Si8 | 24|466.429|480.664| 17.152| -31.593| 8| 19.588 15|O16Si8 | 24|423.469|480.664| 18.892| -31.639| 8| 15.179 16|O16Si8 | 24|450.716|480.664| 17.750| -31.628| 8| 16.219 17|O16Si8 | 24|331.528|480.664| 24.131| -31.642| 8| 14.857 18|O16Si8 | 24|458.573|480.664| 17.445| -31.635| 8| 15.572 19|O16Si8 | 24|359.298|480.664| 22.266| -31.655| 8| 13.636 20|O16Si8 | 24|464.264|480.664| 17.232| -31.612| 8| 17.750 Rows: 331171 (showing first 20) Keys: density, energy_per_tsite, n_tsites, relative_energy ase db IZA_NNPscan.db -c id,formula,natoms,volume,mass,density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output id|formula |natoms| volume| mass|density|energy_per_tsite|n_tsites|relative_energy|iza_code 1|O16Si8 | 24| 435.488| 480.664| 18.370| -31.676| 8| 11.594|ABW 2|O32Si16 | 48| 961.419| 961.328| 16.642| -31.645| 16| 14.612|ACO 3|O96Si48 | 144|3154.579|2883.984| 15.216| -31.664| 48| 12.810|AEI 4|O80Si40 | 120|2102.921|2403.320| 19.021| -31.703| 40| 9.021|AEL 5|O96Si48 | 144|2417.286|2883.984| 19.857| -31.666| 48| 12.586|AEN 6|O144Si72| 216|4075.300|4325.976| 17.667| -31.674| 72| 11.831|AET 7|O96Si48 | 144|2786.810|2883.984| 17.224| -31.675| 48| 11.716|AFG 8|O48Si24 | 72|1400.247|1441.992| 17.140| -31.690| 24| 10.268|AFI 9|O64Si32 | 96|1764.823|1922.656| 18.132| -31.653| 32| 13.809|AFN 10|O80Si40 | 120|2080.330|2403.320| 19.228| -31.707| 40| 8.632|AFO 11|O64Si32 | 96|2097.384|1922.656| 15.257| -31.655| 32| 13.622|AFR 12|O112Si56| 168|3820.116|3364.648| 14.659| -31.650| 56| 14.150|AFS 13|O144Si72| 216|4732.720|4325.976| 15.213| -31.664| 72| 12.793|AFT 14|O60Si30 | 90|1897.074|1802.490| 15.814| -31.659| 30| 13.268|AFV 15|O96Si48 | 144|3154.885|2883.984| 15.214| -31.664| 48| 12.776|AFX 16|O32Si16 | 48|1137.335| 961.328| 14.068| -31.591| 16| 19.790|AFY 17|O48Si24 | 72|1283.812|1441.992| 18.694| -31.620| 24| 17.034|AHT 18|O96Si48 | 144|2479.287|2883.984| 19.360| -31.681| 48| 11.155|ANA 19|O64Si32 | 96|1797.086|1922.656| 17.807| -31.662| 32| 12.924|APC 20|O64Si32 | 96|1751.393|1922.656| 18.271| -31.678| 32| 11.422|APD Rows: 236 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy # Filtering of the database, e.g., for structures with relative energies &lt; 10 kJ/(mol Si) ase db IZA_NNPscan.db relative_energy\&lt;10 -c density,energy_per_tsite,n_tsites,relative_energy,iza_code # Output density|energy_per_tsite|n_tsites|relative_energy|iza_code 19.021| -31.703| 40| 9.021|AEL 19.228| -31.707| 40| 8.632|AFO 19.385| -31.695| 24| 9.802|ATV 18.778| -31.702| 34| 9.061|DOH 19.570| -31.693| 24| 9.959|EWO 18.401| -31.698| 32| 9.451|GON 18.551| -31.695| 112| 9.807|IHW 17.778| -31.693| 288| 9.972|IMF 19.154| -31.695| 6| 9.762|JBW 18.187| -31.695| 96| 9.734|MFI 19.278| -31.709| 48| 8.443|MRE 18.035| -31.698| 90| 9.481|MSO 20.417| -31.724| 44| 7.003|MTF 19.227| -31.704| 136| 8.898|MTN 18.542| -31.693| 28| 9.966|MTW 19.137| -31.695| 60| 9.798|PCR 20.037| -31.709| 144| 8.464|PSI 18.843| -31.703| 64| 9.004|SAF 18.371| -31.703| 112| 8.975|STO 19.894| -31.706| 17| 8.671|VET Rows: 20 (showing first 20) Keys: density, energy_per_tsite, iza_code, n_tsites, relative_energy</code></pre> The quantities shown above are available with the keys (besides standard ASE database keys): Key Quantity Unit id Identifier formula Chemical formula of the unit cell natoms Number of atoms volume Unti cell volume ų mass Atomic mass of the unit cell amu density Framework density Si/nm³ energy_per_tsite NNPscan energy eV n_tsites Number of T-sites relative_energy Energy with respect to quartz kJ/(mol Si) iza_code only for 'IZA_NNPscan.db' Comma separated csv files for the quantities listed above: "DEEM_NNPscan.csv" and "IZA_NNPscan.csv" <em>2. Neural network potentials (NNP) for silica</em> SchNet [Schütt18,Schütt19] NNP files trained on DFT data at the PBE+D3 (NNPpbe) and SCAN+D3 level (NNPscan) Simulations can be performed using SchNetPack with its ASE calculator This example shows a simple single-point calculation <pre><code class="language-python">import ase.io import torch from schnetpack.interfaces import SpkCalculator from schnetpack.environment import AseEnvironmentProvider # check if GPU(s) are available if torch.cuda.is_available(): device = "cuda" else: device = "cpu" # load the NNP model model = torch.load('SiOscan1', map_location=device) # read some structure atoms = ase.io.read( ... ) # define SchNetPack calculator calc = SpkCalculator(model=model, device=device, energy='energy', forces='forces', environment_provider=AseEnvironmentProvider(6.) ) # attach calculator to atoms object atoms.set_calculator(calc) # perform simulations, e.g., single-point calculation energy = atoms.get_potential_energy() print(energy)</code></pre> <em>3. Test set used for accuracy evaluation (ASE database: test_set_NNPscan.db)</em>