A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data

Baumes, Laurent A.; Moliner, Manuel; Nicoloyannis, Nicolas; Corma, Avelino

doi:10.1039/b812395k

Cited by 35 publications

(30 citation statements)

References 15 publications

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“…There have been a number of efforts directed at developing algorithms that allow rapid identification of key trends and features in diffraction data taken across multiphase CCS libraries, and subsequent attribution of such data to individual phases. [400][401][402][403][404][405][406][407] By performing cluster analysis, similar diffraction patterns, even among hundreds of spectra, can be rapidly identified. Similar XRD patterns usually indicate the presence of similar phases in a particular composition region.…”

Section: High-throughput Mapping Of Phase Diagramsmentioning

confidence: 99%

“…113 The CCS library chip was made by first depositing gradient thicknesses of the precursor layers La 2 O 3 , Mn 3 O 4 , and CaMnO 3 at RT on a (100) LaAlO 3 substrate, followed by annealing at 200 C for several days, 400 C for 30 h, and sintering at 1000 C for 2 h in flowing O 2 ; a predominantly epitaxial library film resulted. To map the magnetic phases, low temperature SSM was carried out at 3 K to7 K, and to track the qualitative change in electrical properties of this CCS film, SMM was performed at RT.…”

Section: B Magnetic Oxidesmentioning

confidence: 99%

See 1 more Smart Citation

Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

Green

Takeuchi

Hattrick‐Simpers

2013

Journal of Applied Physics

223

189

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High throughput (combinatorial) materials science methodology is a relatively new research paradigm that offers the promise of rapid and efficient materials screening, optimization, and discovery. The paradigm started in the pharmaceutical industry but was rapidly adopted to accelerate materials research in a wide variety of areas. High throughput experiments are characterized by synthesis of a "library" sample that contains the materials variation of interest (typically composition), and rapid and localized measurement schemes that result in massive data sets. Because the data are collected at the same time on the same "library" sample, they can be highly uniform with respect to fixed processing parameters. This article critically reviews the literature pertaining to applications of combinatorial materials science for electronic, magnetic, optical, and energy-related materials. It is expected that high throughput methodologies will facilitate commercialization of novel materials for these critically important applications. Despite the overwhelming evidence presented in this paper that high throughput studies can effectively inform commercial practice, in our perception, it remains an underutilized research and development tool. Part of this perception may be due to the inaccessibility of proprietary industrial research and development practices, but clearly the initial cost and availability of high throughput laboratory equipment plays a role. Combinatorial materials science has traditionally been focused on materials discovery, screening, and optimization to combat the extremely high cost and long development times for new materials and their introduction into commerce. Going forward, combinatorial materials science will also be driven by other needs such as materials substitution and experimental verification of materials properties predicted by modeling and simulation, which have recently received much attention with the advent of the Materials Genome Initiative. Thus, the challenge for combinatorial methodology will be the effective coupling of synthesis, characterization and theory, and the ability to rapidly manage large amounts of data in a variety of formats. V C 2013 AIP Publishing LLC. [http://dx

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Section: High-throughput Mapping Of Phase Diagramsmentioning

confidence: 99%

Section: B Magnetic Oxidesmentioning

confidence: 99%

Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

Green

Takeuchi

Hattrick‐Simpers

2013

Journal of Applied Physics

223

189

View full text Add to dashboard Cite

show abstract

“…23 Kusne, et al 24 demonstrated the speed and accuracy of using the L1 norm with a mean shift theory clustering method to provide analysis of XRD data as the data was being collected. Baumes, et al 25 investigated the use of the dynamic time warping (DTW) measure to assist in XRD analysis and found it to be resilient to peak shifting when the range of peak shifting was known. LeBras, et al 26 and Ermon, et al 27 later applied DTW as part of larger constraint programming-based algorithms for concurrent composition-phase map determination and constituent phase identification from XRD data.…”

Section: Introductionmentioning

confidence: 99%

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

2017

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Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation, fabrication, and characterization. In particular, machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps from structural data characterization of composition spread libraries, enabling rapid materials fabrication-structure-property analysis and functional materials discovery. A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure, or kernel function. The desired measure reduces the impact of confounding structural data issues on analysis performance. The issues include peak height changes and peak shifting due to lattice constant change as a function of composition. In this work, we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure's performance impact on automatic composition-phase map determination. Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition spread. The cosine, Pearson correlation coefficient, and Jensen-Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting (due to lattice constant change) when the magnitude of peak shifting is unknown. With prior knowledge of the maximum peak shifting, dynamic time warping in a normalized constrained mode provides the best performance. This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries.

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“…A careful inspection of their robustness and the corresponding highlight of specific failures led us to create a new technique: adaptable time warping (ATW). [7] Herein, the design process of the recently proposed ATW strategy is thoroughly detailed in a step-by-step manner. This allows a deep understanding of the motivations, improvements, and the resulting remarkable properties of the methodology.…”

Section: Introductionmentioning

confidence: 99%

Design of a Full‐Profile‐Matching Solution for High‐Throughput Analysis of Multiphase Samples Through Powder X‐ray Diffraction

Baumes

Moliner

Corma

2009

Chemistry A European J

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Few solutions that aim to identify crystalline materials from the analysis of powder X-ray diffraction (XRD) data have been reported to date. A careful inspection of the powder XRD data, and the corresponding highlight of specific failures when it has been used for the determination of the crystallographic phases of zeolites among mixtures, has allowed the creation of the recently proposed strategy: adaptable time warping (ATW). Herein, the design process is thoroughly detailed in a step-by-step manner, which allows a deep understanding of the motivations, improvements, and the resulting remarkable properties of our methodology. Because the use of high-throughput (HT) techniques for the discovery or for increasing the breadth of the synthetic routes of new microporous crystalline structures makes the reliability of search-match methods a critical factor to be assessed, a meticulous evaluation of the reliability and the robustness is provided and supported by both empirical comparisons and mathematical proof. The results offered by our methodology, which clearly outperforms the well-established solutions, open the way towards total automation of such a routine procedure, eliminating laborious and time-consuming controls, preliminary treatments, and settings. Consequently, the proposed solution is of great interest and appears to be very promising, not only because of the numerous potential applications of XRD in materials science, but also the possible expansion of the solution to several other characterization techniques.

show abstract

A reliable methodology for high throughput identification of a mixture of crystallographic phases from powder X-ray diffraction data

Cited by 35 publications

References 15 publications

Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

Applications of high throughput (combinatorial) methodologies to electronic, magnetic, optical, and energy-related materials

Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

Design of a Full‐Profile‐Matching Solution for High‐Throughput Analysis of Multiphase Samples Through Powder X‐ray Diffraction

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