PyTASER

PyTASER is a Python (3.9+) library and set of command-line tools for classifying spectral features in bulk materials, post-DFT. The goal of this library is to provide qualitative comparisons for experimental TAS spectra - a complex and tedious process, especially for pristine materials. The main features include:

• An interactive TAS spectrum for a pristine semiconducting material
• Isolating spectra for individual band transitions from the overall TAS spectrum for the material.
• Spectra in different conditions: temperature, carrier concentrations (analogous to pump-probe time delay)
• Identifying partial occupancies of valence and conduction bands, using the Fermi-Dirac distribution for different Quasi-Fermi levels.
• Considers both non-magnetic and magnetic materials.
• Taking DFT-calculated bandstructure and dos inputs, with primary support for the Materials Project.

Installation

The recommended way to install PyTASER is in a conda environment.

Installation method to be updated here

PyTASER is currently compatible with Python 3.9+ and relies on a number of open-source python packages, specifically:

• pymatgen
• numpy, scipy for data structures and unit conversion
• matplotlib, plotly for plotting the spectra
• AbiPy for the gaussian function implemented in JDOS_simple

Visualisation

Once the library is installed, please setup a file as done in the examples provided. Then just run it as a python file:

python3 filename.py

Contributing

The library is currently undergoing some final changes before it is finalised. However, once it is completed, we would greatly appreciate any contributions in the form of a pull request. Additionally, any test cases/example spectra performed with PyTASER would be welcomed.

Future topics we'd like to build on:

• Converting between carrier concentrations and pump-probe time delay (for a more quantitative analysis)
• Incorporating spin-change processes (e.g. moving from Spin.up to Spin.down and vice-versa) for spin-polarised systems
• Incorporating finite-temperature effects (particularly with indirect bandgaps and phonons, and defects)
• Incorporating more complex optical processes (e.g. Stimulated Emissions)
• Cleaning the regions further away from the bandgap
• Implementing the optical transition probabilities alongside the JDOS
• Creating a kinetics plot for TAS analysis.
• Relating spectral features with associated optical processes

Acknowledgements

Developed by Savyasanchi Aggarwal, Alex Ganose and Liam Harnett-Caulfield. Aron Walsh designed and led the project.

Thanks to the WMD group @ Imperial/Yonsei for all the interesting discussions and improvements!