Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."

Schwalbe Koda, Daniel

doi:10.11578/2372801

Title: Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."

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Abstract

Repository containing all the data necessary to reproduce the paper "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". This content includes: raw data for all results in the article; figures appearing in the main paper; and Jupyter notebooks and scripts containing the code for analyzing/reproducing the results.

Authors:

Schwalbe Koda, Daniel

Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); PLS/MSD

Publication Date:: Thu Aug 10 04:00:00 UTC 2023

Other Number(s):: LLNL-MI-854709
IM # 1083508

DOE Contract Number:: AC52-07NA27344

Research Org.:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 2372801

DOI:: https://doi.org/10.11578/2372801

Citation Formats


                    Schwalbe Koda, Daniel. Data for: "Inorganic synthesis-structure maps in zeolites with machine learning...".  United States: N. p., 2023. 
        Web.  doi:10.11578/2372801.

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                    Schwalbe Koda, Daniel. Data for: "Inorganic synthesis-structure maps in zeolites with machine learning...".  United States.  doi:https://doi.org/10.11578/2372801

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                    Schwalbe Koda, Daniel. 2023.  
"Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."".  United States.  doi:https://doi.org/10.11578/2372801.  https://www.osti.gov/servlets/purl/2372801. Pub date:Thu Aug 10 04:00:00 UTC 2023

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@article{osti_2372801,

  title        = {Data for: "Inorganic synthesis-structure maps in zeolites with machine learning..."},

  author       = {Schwalbe Koda, Daniel},

  abstractNote = {Repository containing all the data necessary to reproduce the paper "Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances". This content includes: raw data for all results in the article; figures appearing in the main paper; and Jupyter notebooks and scripts containing the code for analyzing/reproducing the results.},

  doi          = {10.11578/2372801},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Thu Aug 10 04:00:00 UTC 2023},

  month        = {Thu Aug 10 04:00:00 UTC 2023}

}

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DOI: https://doi.org/10.11578/2372801

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Inorganic synthesis-structure maps in zeolites with machine learning and crystallographic distances

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Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on the design of organic structure-directing agents (OSDAs). In this work, we combine distances between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according tomore »« less
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