PyFitIt

Fitting X-ray absorption near edge structure


Python implementation of FitIt software to fit X-ray absorption near edge structure (XANES) and other spectra. The python version is extended with additional features: machine learning, automatic component analysis, direct geometry prediction and others.

Features

  • Uses ipywidgets to construct the portable GUI
  • Calculates XAS by FDMNES or FEFF or ADF or pyGDM
  • Interpolates spectra in order to speedup fitting. Support different types of interpolation point generation: grid, random, IHS, adaptive and various interpolation methods including machine learning algorithms
  • Using multidimensional interpolation approximation you can vary parameters by sliders and see immediately theoretical spectrum, which corresponds to this geometry. Fitting can be performed on the basis of visual comparison with experiment or using automatic procedure and quantitative criteria.
  • Supports direct prediction of geometry parameters by machine learning
  • Includes automatic and semi-automatic component analysis

Installation

  1. Run the following command in your python environment:

    pip install --upgrade pyfitit

    If you don't have python and pip, install it, for example using anaconda distribution.
  2. Download and unpack examples in PyFitIt github repository.
  3. Start jupyter notebook system and open some .ipynb-example in your browser.

If you like the software, acknowledge it using the references below

  • A. Martini, A. L. Bugaev, S. A. Guda, A. A. Guda, E. Priola, E. Borfecchia, S. Smolders, K. Janssens, D. De Vos, and A. V. Soldatov Revisiting the Extended X-ray Absorption Fine Structure Fitting Procedure through a Machine Learning-Based Approach // The Journal of Physical Chemistry A Article ASAP 2021, 125, 32, 7080–7091 DOI: 10.1021/acs.jpca.1c03746
  • Guda A.A., Guda S.A., Martini A., Kravtsova A.N., Algasov A., Bugaev A., Kubrin S.P., Guda L.V., Šot P., van Bokhoven J.A., Copéret C., Soldatov A.V. Understanding X-ray absorption spectra by means of descriptors and machine learning algorithms (2021) npj Computational Materials, 7 (1), art. no. 203 DOI: 10.1038/s41524-021-00664-9
  • Kozyr E.G., Bugaev A.L., Guda S.A., Guda A.A., Lomachenko K.A., Janssens K., Smolders S., De Vos D., Soldatov A.V. Speciation of Ru Molecular Complexes in a Homogeneous Catalytic System: Fingerprint XANES Analysis Guided by Machine Learning (2021) Journal of Physical Chemistry C, 125 (50), pp. 27844 - 27852 DOI: 10.1021/acs.jpcc.1c09082
  • A. Martini, A.A. Guda, S.A. Guda, A.L. Bugaev, O.V. Safonova, A.V. Soldatov "Machine Learning Powered by Principal Component Descriptors as the Key for Sorted Structural Fit of XANES" // Phys. Chem. Chem. Phys., 2021 DOI: 10.1039/D1CP01794B
  • A. Martini, A. A. Guda, S. A. Guda, A. Dulina, F. Tavani, P. D’Angelo, E. Borfecchia, and A. V. Soldatov. Estimating a Set of Pure XANES Spectra from Multicomponent Chemical Mixtures Using a Transformation Matrix-Based Approach // In: Di Cicco A., Giuli G., Trapananti A. (eds) Synchrotron Radiation Science and Applications. Springer Proceedings in Physics, vol 220. Springer, Cham. DOI: 10.1007/978-3-030-72005-6_6
  • A. Martini, S. A. Guda, A. A. Guda, G. Smolentsev, A. Algasov, O. Usoltsev, M. A. Soldatov, A. Bugaev, Yu. Rusalev, C. Lamberti, A. V. Soldatov "PyFitit: the software for quantitative analysis of XANES spectra using machine-learning algorithms" Computer Physics Communications. 2019. DOI: 10.1016/j.cpc.2019.107064

Links

Acknowledgements

Выполнено в рамках реализации проекта "Фронтирная лаборатория рентгеноспектральной нанометрологии" при поддержке Программы стратегического академического лидерства Южного федерального университета ("Приоритет 2030").