PvkSOM: Perovskite solar cells degradation data clustering using self-organizing map

This documentation is prepared as the workflow to accompany the following study:

"Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset"

Nat Commun 14, 4869 (2023) https://doi.org/10.1038/s41467-023-40585-3

Noor Titan Putri Hartono (1), Hans Köbler (1), Paolo Graniero (1,2), Mark Khenkin (1), Rutger Schlatmann (1), Carolin Ulbrich (1), Antonio Abate (1)

Affiliations:

1. Helmholtz-Zentrum-Berlin für Materialien und Energie, 14109 Berlin, Germany
2. Department of Business Informatics, Freie Universität Berlin, 14195 Berlin, Germany

Installation and Requirements

To install, just clone this repository:

$ git clone https://github.com/noortitan/PvkSOM.git

$ cd PvkSOM

To install the required packages, create a virtual environment using Anaconda/ Miniconda (https://docs.conda.io/en/latest/miniconda.html). The optional but recommended setup on Anaconda/ Miniconda:

$ conda env create -f environment.yml

$ conda activate PvkSOM

$ jupyter notebook

Workflow

The processed data in .pkl format is available on Zenodo (https://doi.org/10.5281/zenodo.8185882), along with the dataset in this repository (PCE_df_grouping.csv).

Use 20230816_degradation_analysis_revision_11_cleaned.ipynb Jupyter Notebook with this data. This notebook loads the .pkl and .csv files, further pre-processing me (including smoothing (using Savitzky-Golay filter), and normalizing the data). At the end of the pre-processing, 2,245 data points are left, and we can start to train the dataset using self-organizing map (SOM). SOM is a popular unsupervised machine learning method for clustering data by converting the nonlinear statistical relationships of high-dimensional data into geometric relationships in low-dimensional nodes while preserving the topological structure of data, i.e. the relations between one data point and others (Kohonen, T., 1990, https://doi.org/10.1109/5.58325). The k-means clustering algorithm is also performed as a comparison to the SOM clustering method.

The other Jupyter Notebooks (which can't be run), 20221122_degradation_preprocessing_2.ipynb and 20230227_degradation_analysis_revision_10_cleaned.ipynb, are meant to demonstrate how the initial raw data is generated from the High-Throughput Ageing Test setup is first processed. The raw data (that comes in .json format; where each file corresponds to one degradation batch) is first converted into dataframes and saved in a pickle (.pkl) file on 20221122_degradation_preprocessing_2.ipynb Jupyter Notebook. Then, the dataframe .pkl files are loaded to 20230227_degradation_analysis_revision_10_cleaned.ipynb Jupyter Notebook, where data pre-processing is performed, which involves excluding 'bad' data, resampling, cutting down the data to 150 hours, smoothing (using Savitzky-Golay filter), and normalizing the data. After that, the subsequent steps are similar to the 20230816_degradation_analysis_revision_11_cleaned.ipynb Jupyter Notebook.

Authors

Author(s)	Noor Titan Putri Hartono
Version	1.1/ August 2023
E-mail(s)	titan dot hartono at helmholtz-berlin dot de

Attribution

This work is under an BSD 2-Clause License License. Please, acknowledge use of this work with the appropriate citation to the repository and research article.

Citation

@software{noortitan_2023_8181602,
  author       = {noortitan},
  title        = {noortitan/PvkSOM: First Release},
  month        = jul,
  year         = 2023,
  publisher    = {Zenodo},
  version      = {v1.0},
  doi          = {10.5281/zenodo.8181602},
  url          = {\url{https://doi.org/10.5281/zenodo.8181602}}
}