PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms

Martini, Andrea; Гуда, С. А.; Guda, Alexander A.; Smolentsev, Grigory; Algasov, Alexander; Usoltsev, Oleg A.; Soldatov, Mikhail A.; Bugaev, Aram L.; Rusalev, Yu. V.; Lamberti, Carlo; Солдатов, А. В.

doi:10.1016/j.cpc.2019.107064

Cited by 93 publications

(125 citation statements)

References 69 publications

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“…This set of spectra was decomposed by means of principal component analysis as implemented in the PyFitIt code. 40 The two principal components are shown in Fig. 6b in comparison to Fe K-edge LiFePO 4 , and FePO 4 spectra shied to the energies of Co K-edge.…”

Section: Resultsmentioning

confidence: 99%

One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework

et al. 2020

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Section: Resultsmentioning

confidence: 99%

One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework

et al. 2020

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“…While coordination, Bader charge, and nearest-neighbor distances do not always fully characterize the environment around the absorbing atom, they provide useful insights into structure-property relationships [11,29,69], and reduce the number of candidates necessary for complete local structure determination. At their current accuracies and efficiencies, models developed within this work could be used as a pre-processing step into these more rigorous workflows -those involving FEFF, MXAN, or Pyfitit -to narrow down initial guesses for a chemical structure when no a priori structure knowledge is available [41][42][43][44][45]. Materials Project (MP) [7], with geometries optimized via density functional theory (DFT).…”

Section: Discussionmentioning

confidence: 99%

“…In cases where heuristics fail, researchers typically must rely on their existing knowledge of specific materials' spectra, and also use software such as FEFF [40] to predict theoretical spectra from input crystal structures. These known or computational spectra can be used either through direct comparison or through specialized algorithms, such as those in MXAN [41][42][43] and Pyfitit [44,45]. This theoretical mapping of crystal structure to spectra can provide a thorough understanding of the materials under investigation, as long as the material's structure can be identified.…”

Section: Introductionmentioning

confidence: 99%

Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships

Torrisi

Carbone²,

Rohr³

et al. 2020

Preprint

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X-ray absorption spectroscopy (XAS) produces a wealth of information about the local structure of materials, but interpretation of spectra often relies on easily accessible trends and prior assumptions about the structure. Recently, researchers have demonstrated that machine learning models can automate this process to predict the coordinating environments of absorbing atoms from their XAS spectra. However, machine learning models are often difficult to interpret, making it challenging to determine when they are valid and whether they are consistent with physical theories. In this work, we present three main advances to the data-driven analysis of XAS spectra: we demonstrate the efficacy of random forests in solving two new property determination tasks (predicting Bader charge and mean nearest neighbor distance), we show that multiscale featurization can elucidate the regions and trends in spectra that encode various local properties, and we address the effect of normalization on model interpretability. The multiscale featurization transforms the spectrum into a vector of polynomial-fit features, and is contrasted with the commonly-used "pointwise" featurization that directly uses the entire spectrum as input. We find that across thousands of transition metal oxide spectra, the relative importance of features describing the curvature of the spectrum can be localized to individual energy ranges, and we can separate the importance of constant, linear, quadratic, and cubic trends, as well as the white line energy. This work has the potential to assist rigorous theoretical interpretations, expedite experimental data collection, and automate analysis of XAS spectra, thus accelerating discovery of new functional materials.

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“…Equipped with an intuitive graphical user interface (GUI), this program, developed in Python, allows fast data treatment and the visualization of several spectral profiles collected under different working conditions, ranging from UHV to ambientpressure atmosphere. Similarly to PyFitIt (Martini et al, 2020), one of its strengths is the possibility to quickly perform conventional XAS data-handling procedures, such as spectral background subtraction and normalization, using an approach based on sliders and cursors. A peak-fitting toolbox characterized by a high variety of peak functions and ionization step potentials is also included for in-depth studies.…”

Section: Introductionmentioning

confidence: 99%

THORONDOR: a software for fast treatment and analysis of low-energy XAS data

Simonne

Martini

Signorile

et al. 2020

J Synchrotron Radiat

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THORONDOR is a data treatment software with a graphical user interface (GUI) accessible via the browser-based Jupyter notebook framework. It aims to provide an interactive and user-friendly tool for the analysis of NEXAFS spectra collected during in situ experiments. The program allows on-the-fly representation and quick correction of large datasets from single or multiple experiments. In particular, it provides the possibility to align in energy several spectral profiles on the basis of user-defined references. Various techniques to calculate background subtraction and signal normalization have been made available. In this context, an innovation of this GUI involves the usage of a slider-based approach that provides the ability to instantly manipulate and visualize processed data for the user. Finally, the program is characterized by an advanced fitting toolbox based on the lmfit package. It offers a large selection of fitting routines as well as different peak distributions and empirical ionization potential step edges, which can be used for the fit of the NEXAFS rising-edge peaks. Statistical parameters describing the goodness of a fit such as χ2 or the R-factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.

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PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms

Cited by 93 publications

References 69 publications

One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework

One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework

Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships

THORONDOR: a software for fast treatment and analysis of low-energy XAS data

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PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms

Cited by 93 publications

References 69 publications

One-pot coating of LiCoPO4/C by a UiO-66 metal–organic framework

One-pot coating of LiCoPO4/C by a UiO-66 metal–organic framework

Random Forest Machine Learning Models for Interpretable X-Ray Absorption Near-Edge Structure Spectrum-Property Relationships

THORONDOR: a software for fast treatment and analysis of low-energy XAS data

Contact Info

Product

Resources

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One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework

One-pot coating of LiCoPO₄/C by a UiO-66 metal–organic framework