This repo contains the data and algorithms for manuscript "High-throughput micro-scale bandgap mapping for perovskite-inspired materials with complex composition space".
All spatially-resolved hyperspectral imaging raw data and bandgap analysis results can be found at https://drive.google.com/drive/folders/1BNqnNCenH5jfw8lUP5zS3Z8ytohY-6gB?usp=sharing. Transient absorption data can be found in the zip file named TA raw data with label.
The bandgap extraction algorithm in this manuscript was adapted from Siemenn, A. E. et al. Using scalable computer vision to automate high-throughput semiconductor characterization. Nat. Commun. 15, 4654 (2024). The original code is available at https://github.com/PV-Lab/Autocharacterization-Bandgap. The main changes made for better fitting and generalizability are summirized in the table below:
| This work | Previous work | |
|---|---|---|
| Range | Full data range | Manual selection of target bandgap range |
| Resolution | Spatially-resolved bandgaps (N x N spectra) | Average bandgap of each droplet (1 spectrum) |
| Fitting | Linear regression on maximum difference | Linear regression on detected peaks |