This is the repository of code and data for paper "Machine learning-enabled chemical space exploration of all-inorganic perovskites for photovoltaics" (https://doi.org/10.21203/rs.3.rs-3315454/v1)
This codes are based on Python3. This codes require to install prerequistes of CGCNN. In addition, this codes require:
- Linux operating system
- Pandas with openpyxl
- Atomic Simulation Environment
- tqdm
- Matplotlib
- mpltern (for ternary plot)
If you are new to Python, the easiest way of installing the prerequisites is via conda. After installing conda, run the following command to create a new environment named cgcnn and install all prerequisites:
conda upgrade conda
conda create -n cgcnn python=3
conda activate cgcnn
pip install scikit-learn torch pymatgen pandas openpyxl ase tqdm matplotlib mpltern
- Computer cluster nodes with workload manager program like slurm or pbs.
- VASPKIT
"Exploration results obtained by CGCNN.xlsx" file contains screening results for 41,400 ABX3 perovskite compositions. "Training data for CGCNN.xlsx" file contains DFT calculation results for 3,159 ABX3 perovskite compositions.
This directory contains shell script for generating 3159 non-equivalent structures used for CGCNN training.
This directory contains codes and files necessary to train CGCNN models. See README file in the "CGCNN_training" directory for details.
This directory contains codes and necessary files for chemical exploration of ABX3 perovskite using the trained CGCNN models. "job_submit_example.sh" file shows example for using "random_searching_restart.py" code. The "random_searching_restart.py" code will create log and pickle files. The log file contains decomposition energy, the number of generated atomic configurations for each composition. The pickle file contains the lowest-energy atomic configuration for each composition, as a format of ASE (Atomic Simulation Environment) atoms. Using the generated pickle file, "bandgap_model_best.pth.tar", "bandtype_model_best.pth.tar", and "predict.py" files, you can predict bandgap and bandtype for the lowest-energy atomic configuration at each composition. See README file in the "CGCNN_searching" directory for details.
This directory contains codes and neccessary files for reproducing figures of the paper.
This codes were primarily written by Jin-Soo Kim and Juhwan Noh who were advised by Dr. Jino Im.