Published October 9, 2023 | Version 1.0
Dataset Open

Raw Data for: "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances"

  • 1. Lawrence Livermore National Laboratory
  • 1. University of Liverpool
  • 2. Lawrence Livermore National Laboratory

Description

This repository contains all the raw data to reproduce the manuscript:

D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023)

The raw data should be used in combination with the code hosted on GitHub: https://github.com/dskoda/Zeolites-AMD.

Description of the data

The data in this link contains all necessary information to reproduce the manuscript. In combination with the code hosted on GitHub, it can be visualized and analyzed accordingly. The full description on the columns and results is available on the GitHub code.
The data files in this repository are:

- `hparams_rnd_*.json`: results of the hyperparameter optimization of all classifiers studied in this work. The data was produced by randomly sampling the train-validation-test sets. In some cases, the data was normalized (`_norm_`), and the train set was kept `balanced` or `unbalanced`.
- `hyp_dm`: distance matrix of all hypothetical zeolites towards the known zeolites
- `hyp_predictions`: predictions of the synthesis conditions for all hypothetical zeolites
- `xgb_ensembles*`: pickle files containing the serialized ensemble models used in the evaluation of the data in this work. The models can be loaded with the `xgboost` Python package.

License

The data and all the content from this repository is distributed under the Creative Commons Attribution 4.0 (CC-BY 4.0)

This work was produced under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Dataset released as: LLNL-MI-854709.

Files

LICENSE.md

Files (867.5 MB)

Name Size Download all
md5:bfaaa7e2071779e98c88a4f51bb8a8fa
16.5 MB Download
md5:d90b722ad8020426e3d3ed1412d090d1
16.5 MB Download
md5:2fcf424b9d30387102a78ba305d00602
21.5 MB Download
md5:8da2d84a6f0a8f6d058035b83f94d3bc
16.8 MB Download
md5:3babcf823f0209fd9b7f8d0361dad406
21.5 MB Download
md5:7736debaaddbd08cfb7028b69df4c8fe
21.4 MB Download
md5:fac6d81a473e0164b9de6a98c73e32a5
673.9 MB Download
md5:34d2878684eec8743006d412f57bb6a9
8.0 MB Download
md5:2ef204466e2ac86c1d340b036a765d47
6.3 MB Download
md5:300d007e76439c90a077d840789979e7
13.1 kB Preview Download
md5:19dec5c594dc018d75ba0d0eb8a5cd74
1.4 kB Download
md5:c47f03523b3f8eb10548ac3aec97b494
1.6 kB Preview Download
md5:89abb8d332a7755fc3a980f4a3b1c2e9
33.4 MB Download
md5:ccac4ef0004e117564a0dd1ea5aefd40
31.7 MB Download

Additional details

Related works

Is part of
Preprint: 10.48550/arXiv.2307.10935 (DOI)

References

  • D. Schwalbe-Koda et al. "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". arXiv:2307.10935 (2023)