Code for "Machine learning uncovers degradation pathways of perovskite light-emitting diode with multispectral imaging"

Paper link

https://www.nature.com/articles/s42256-023-00736-z

Releases

• Version 1.0 (For Python 3.6, Cuda 10.0, and TensorFlow 1.14.0) https://doi.org/10.5281/zenodo.8281088
• Version 2.0 (For Python 3.6, Cuda 11.x, and TensorFlow 2.6.2) https://doi.org/10.5281/zenodo.8417653

System requirements

Software

• python 3.6.8
• h5py 3.1.0
• scipy 1.5.4
• joblib 0.14.1
• matplotlib 3.1.2
• tensorflow_gpu 2.6.2 (support Cuda 11.2/11.4)

Hardware

• 32GB RAM PC Memory
• (Optional) 16GB GPU Memory

Tested enviornment

• Linux, CentOS 7
• Windows 11, 22H2

Installation guide

Install Anaconda (15-30 min)

https://docs.anaconda.com/anaconda/install/

Create and activate new enviornment in anaconda prompt (5-10 min)

conda create -n (anyname) python=3.6.8

conda activate (anyname)

Register new enviornment for Jupyter Notebook

conda install ipykernel

Install dependencies through pip

pip install h5py==3.1.0
pip install matplotlib==3.1.2
pip install scipy==1.5.4
pip install joblib==0.14.1

To install the CPU version of tensorflow:

pip install tensorflow==2.6.2

OR To install the GPU version (which require a working GPU with CUDA 11.2 or 11.4 with a corresponding cuDNN driver):

pip install tensorflow_gpu==2.6.2

Demo and instruction to use

Launch Jupyter Notebook and locate this repository. https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html

Run PANet_blind.ipynb, follow the instruction to reproduce the reported result. The attached Demo.pdf is a complete run of the demo with full logs.

License

[Creative Commons Attribution-NonCommercial (CC BY-NC 4.0)] https://creativecommons.org/licenses/by-nc/4.0/