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

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.