A suite of tools that enables analysis, comparison, and machine learning for X-ray spectroscopy measurements. Currently available tools focus on X-ray absorption spectroscopy, particularly XANES spectra, and have been developed at the Toyota Research Institute.
Use pip install trixs to install.
If you want to develop TRIXS, clone the repo via git and use
python setup.py develop for an editable install, or use pip:
git clone git@github.com:TRI-AMDD/trixs.git
cd trixs
pip install -e .The packages required for this repo can be found in requirements.txt.
The data used to generate the figures found in [1] are publically available at TRI's https://data.matr.io/4/.
If you use any part of this code for your own purposes, please cite:
[1] S.B. Torrisi et al, Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships, NPJ Computational Materials, 2020
If you use the Spectrum core class, which is built off of the pymatgen software package, don't forget to cite:
[2] Shyue Ping Ong, et al. Python Materials Genomics (pymatgen) : A Robust,
Open-Source Python Library for Materials Analysis. Computational
Materials Science, 2013, 68, 314-319. doi:10.1016/j.commatsci .2012.10.028 <http://dx.doi.org/10.1016/j.commatsci.2012.10.028>_
If you use any of the scripts which involve or use Atomate, please cite:
[3] Mathew, K. et al. Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows. Comput. Mater. Sci. 139, 140-152 (2017).
This repository was created by Steven B. Torrisi at the Toyota Research Institute during Summer 2019. While their names may not show up in the contributors tab, the feedback of Matthew Carbone, Santosh Suram, and Joseph Montoya were useful in shaping the initial design of the code in this repository.