Using Similarity Metrics to Quantify Differences in High-Throughput Data Sets: Application to X-ray Diffraction Patterns

Hernández–Rivera, Efraín; Coleman, Shawn P.; Tschopp, Mark A.

doi:10.1021/acscombsci.6b00142

Cited by 21 publications

(17 citation statements)

References 37 publications

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“…Only bin-to-bin similarity metrics are used in the ELSIE algorithm development as they are less computationally demanding for high-throughput datasets. 45 Four commonly used similarity metrics in the literatures are used in the ELSIE algorithm:…”

Section: Feffmentioning

confidence: 99%

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Automated generation and ensemble-learned matching of X-ray absorption spectra

et al. 2018

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X-ray absorption spectroscopy (XAS) is a widely used materials characterization technique to determine oxidation states, coordination environment, and other local atomic structure information. Analysis of XAS relies on comparison of measured spectra to reliable reference spectra. However, existing databases of XAS spectra are highly limited both in terms of the number of reference spectra available as well as the breadth of chemistry coverage. In this work, we report the development of XASdb, a large database of computed reference XAS, and an Ensemble-Learned Spectra IdEntification (ELSIE) algorithm for the matching of spectra. XASdb currently hosts more than 800,000 K-edge X-ray absorption near-edge spectra (XANES) for over 40,000 materials from the open-science Materials Project database. We discuss a high-throughput automation framework for FEFF calculations, built on robust, rigorously benchmarked parameters. FEFF is a computer program uses a real-space Green's function approach to calculate X-ray absorption spectra. We will demonstrate that the ELSIE algorithm, which combines 33 weak "learners" comprising a set of preprocessing steps and a similarity metric, can achieve up to 84.2% accuracy in identifying the correct oxidation state and coordination environment of a test set of 19 K-edge XANES spectra encompassing a diverse range of chemistries and crystal structures. The XASdb with the ELSIE algorithm has been integrated into a web application in the Materials Project, providing an important new public resource for the analysis of XAS to all materials researchers. Finally, the ELSIE algorithm itself has been made available as part of veidt, an open source machine-learning library for materials science.npj Computational Materials (2018) 4:12 ; doi:10.1038/s41524-018-0067-x INTRODUCTION X-ray absorption spectroscopy (XAS) is a widely used technique in the study of the properties, physical states, and local environments of materials.1-3 When incident X-ray photons with energy greater than the binding energy are absorbed by an atom, a corelevel electron is removed from its quantum level. In XAS, the absorption coefficient, μ(E) is measured as a function of X-ray energy E. Detailed descriptions of X-ray absorption theory and equation have been included in many excellent books and review papers.

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Section: Feffmentioning

confidence: 99%

“…45 3. Cosine similarity: The cosine similarity measure is the normalized inner product and measures the angle between two spectral vectors.…”

Section: Feffmentioning

confidence: 99%

Automated generation and ensemble-learned matching of X-ray absorption spectra

et al. 2018

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“…3 (the upper left inset contour plot) and grouped by various clusters (large contour plot). The clustering in the large contour plot was performed using Euclidean distances (L 2 norm) between rows/columns and then clustering based on maximum distances; there are a number of different distance (or similarity) functions that can be applied to cluster these vectors [59]. The diagonal for this plot is each response compared against itself (i.e., R ¼ 1).…”

Section: Discussionmentioning

confidence: 99%

Quantifying Parameter Sensitivity and Uncertainty for Interatomic Potential Design: Application to Saturated Hydrocarbons

Tschopp

Rinderspacher

Nouranian

et al. 2017

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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The research objective herein is to understand the relationships between the interatomic potential parameters and properties used in the training and validation of potentials, specifically using a recently developed modified embedded-atom method (MEAM) potential for saturated hydrocarbons (C–H system). This potential was parameterized to a training set that included bond distances, bond angles, and atomization energies at 0 K of a series of alkane structures from methane to n-octane. In this work, the parameters of the MEAM potential were explored through a fractional factorial design and a Latin hypercube design to better understand how individual MEAM parameters affected several properties of molecules (energy, bond distances, bond angles, and dihedral angles) and also to quantify the relationship/correlation between various molecules in terms of these properties. The generalized methodology presented shows quantitative approaches that can be used in selecting the appropriate parameters for the interatomic potential, selecting the bounds for these parameters (for constrained optimization), selecting the responses for the training set, selecting the weights for various responses in the objective function, and setting up the single/multi-objective optimization process itself. The significance of the approach applied in this study is not only the application to the C–H system but that the broader framework can also be easily applied to any number of systems to understand the significance of parameters, their relationships to properties, and the subsequent steps for designing interatomic potentials under uncertainty.

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“…26,27 The selection of appropriate measures is one of the most important steps when applying unsupervised learning methods to evaluate and analyse materials. [26][27][28][29] The estimation of materials parameters from experimental data via similarity measures has great potential for automated data analysis in materials research. The most important aspect of automated materials parameter estimation is the choice of a similarity measure (kernel functions, 30 ) which is not trivial and varies with the experimental method.…”

Section: Introductionmentioning

confidence: 99%

“…In this respect, the Euclidean distance (ED) (L2 norm) and Manhattan distance (L1 norm) are widely used in many fields as similarity/distance metrics; however, these metrics may perform poorly as similarity measures between measured data, 26,27,29 and the appropriate measure of similarity is not trivial. 26,29,31 We investigated measures that are robust to noise and peak broadening and are sensitive to changes in the material parameters. We demonstrate that an important material property, such as the crystal field parameter 10Dq, can be estimated automatically and promptly by the constructed regression model based on the similarity measure.…”

Section: Introductionmentioning

confidence: 99%

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

et al. 2019

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Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade. One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments, which has given rise to the need for accelerated and accurate automated estimation of the properties of materials. In this regard, spectroscopic data are widely used for materials discovery because these data include essential information about materials. An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure, or kernel function. The required measure should be robust in terms of peak shifting, peak broadening, and noise. However, the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved. We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra. The similarity measures show good correspondence with the materials parameter, that is, the crystal-field parameter, in all measures. The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening. We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra. The regression model enabled the materials parameter, that is, 10 Dq, to be automatically estimated from the spectra. With regard to research progress in similarity measures, this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.

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Using Similarity Metrics to Quantify Differences in High-Throughput Data Sets: Application to X-ray Diffraction Patterns

Cited by 21 publications

References 37 publications

Automated generation and ensemble-learned matching of X-ray absorption spectra

Automated generation and ensemble-learned matching of X-ray absorption spectra

Quantifying Parameter Sensitivity and Uncertainty for Interatomic Potential Design: Application to Saturated Hydrocarbons

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

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