Published January 7, 2022 | Version v1
Dataset Open

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

Authors/Creators

  • 1. Charles University, Prague

Description

Content

1. Zeolite databases

  • 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):
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 < 10 kJ/(mol Si)
ase db IZA_NNPscan.db relative_energy\<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
  • 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 Å3
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"

2. Neural network potentials (NNP) for silica

  • 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
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)

3. Test set used for accuracy evaluation (ASE database: test_set_NNPscan.db)

Files

DEEM_NNPscan.csv

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Additional details

Related works

Is supplement to
Preprint: arXiv:2102.12404 (arXiv)
Journal article: 10.1038/s41524-022-00865-w (DOI)

References

  • Deem, M. W., Pophale, R., Cheeseman, P. A. & Earl, D. J. Computational Discovery of New Zeolite-Like Materials. J. Phys. Chem. C 113, 21353–21360 (2009).
  • Pophale, R., Cheeseman, P. A. & Deem, M. W. A database of new zeolite-like materials. Phys. Chem. Chem. Phys. 13, 12407 (2011).
  • Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R. SchNet – A deep learning architecture for molecules and materials. J. Chem. Phys. 148, 241722 (2018).
  • Schütt, K. T. et al. SchNetPack: A Deep Learning Toolbox For Atomistic Systems. J. Chem. Theory Comput. 15, 448–455 (2019).