<b>How to separate two Ts in a pod:</b><b> </b><b>Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning</b>

This repository contains the electronic supplementary files for the manuscript "<b>How to separate two Ts in a pod:</b><b> </b><b>Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning</b>".The files are:RP_JupyterNotebook-CopyDV.ipynb - Jupyter notebook containing the codes used for data preparation, machine learning, analysis, and screening of new candidates.COPPER_Candidate_space_reduced.csv - some of the candidates used for screening, along with features.COPPER_Candidates_aux.csv - candidates created to verify compounds found during synthesis attempts.RP_oxides_after_3_2021_10_23.csv - dataset containing compounds and their features used for training, validation, and testing.utilities.py - auxiliary functions used for data processing.element_properties_upd.csv - property values of elements used to construct the feature set.sisso_results.zip - compressed folder containing the inputs and outputs of SISSO calculation.<br>

Publication Details

Journal
Figshare
Published
2024-12-28
DOI
https://doi.org/10.6084/m9.figshare.28104659
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning

Figshare
Machine Learning in Materials Science
article

How to separate two Ts in a pod: Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning

article en

Abstract

This repository contains the electronic supplementary files for the manuscript "<b>How to separate two Ts in a pod:</b><b> </b><b>Classifying T- and T′-type Ruddlesden-Popper cuprates by machine learning</b>".The files are:RP_JupyterNotebook-CopyDV.ipynb - Jupyter notebook containing the codes used for data preparation, machine learning, analysis, and screening of new candidates.COPPER_Candidate_space_reduced.csv - some of the candidates used for screening, along with features.COPPER_Candidates_aux.csv - candidates created to verify compounds found during synthesis attempts.RP_oxides_after_3_2021_10_23.csv - dataset containing compounds and their features used for training, validation, and testing.utilities.py - auxiliary functions used for data processing.element_properties_upd.csv - property values of elements used to construct the feature set.sisso_results.zip - compressed folder containing the inputs and outputs of SISSO calculation.<br>

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Openalex Percentile: Top 72%
Machine Learning in Materials Science
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