High-throughput X-ray Diffraction Research Articles

Remote and automated high-throughput powder diffraction measurements enabled by a robotic sample changer at SSRL beamline 2-1.

The general-purpose powder diffractometer beamline (BL2-1) at the Stanford Synchrotron Radiation Lightsource (SSRL) is described. The evolution of design and performance of BL2-1 are presented, in addition to current operating specifications, applications and measurement capabilities. Recent developments involve a robotic sample changer enabling high-throughput X-ray diffraction measurements, applicable to mail-in and remote operations. In situ and operando capabilities to measure samples with different form factors (e.g. capillary, flat plate or thin film, and transmission) and under variable experimental conditions are discussed. Several example datasets and accompanying Rietveld refinements are presented.

Extraction of component bases from mixed spectra using non-negative matrix factorization with dissimilarity regularization

We propose a method of automatic pattern decomposition for mixed spectra, based on non-negative matrix factorization. This method uses a regularization term that increases volume coordinated by the decomposed bases. This treatment enhances dissimilarities among the bases and is suitable for expressing the natural component bases of spectra, which generally differ from each other. This regularization term shows better accuracy in a test using virtual spectrum datasets compared to conventional regularizations. We also test the proposed method with a high-throughput X-ray diffraction dataset measured in a synchrotron radiation facility. Our method can decompose the dataset into component bases, which are correctly assigned to background and chemical compounds, while results obtained by other regularization terms cannot sufficiently separate background peaks from and meaningful peaks. We also find that a combination of dissimilarity regularization with smoothness regularization can detect small meaningful peaks. These results show the advantage of dissimilarity regularization for the purpose of automatic spectrum decompositions.

High-Throughput and Autonomous Grazing Incidence X-ray Diffraction Mapping of Organic Combinatorial Thin-Film Library Driven by Machine Learning.

High-throughput X-ray diffraction (XRD) is one of the most indispensable techniques to accelerate materials research. However, the conventional XRD analysis with a large beam spot size may not best appropriate in a case for characterizing organic materials thin film libraries, in which various films prepared under different process conditions are integrated on a single substrate. Here, we demonstrate that high-resolution grazing incident XRD mapping analysis is useful for this purpose: A 2-dimensional organic combinatorial thin film library with the composition and growth temperature varied along the two orthogonal axes was successfully analyzed by using synchrotron microbeam X-ray. Moreover, we show that the time-consuming mapping process is accelerated with the aid of a machine learning technique termed as Bayesian optimization based on Gaussian process regression.

Structural and Functional Properties of the Thin Film System Ti-Ni-Si.

The thin film system Ti-Ni-Si was investigated using methods of combinatorial materials science. A thin film composition spread library of the system was fabricated using combinatorial magnetron sputtering. The functional properties Seebeck coefficient, electrical resistivity, and luminance were determined using high-throughput characterization techniques. A thin-film phase diagram was established by the assessment of high-throughput X-ray diffraction results. Correlations between composition, phase constitution, and functional properties with focus on the binary composition space are discussed.

Combinatorial Study on Phase Formation and Oxidation in the Thin Film Superalloy Subsystems Co-Al-Cr and Co-Al-Cr-W.

Two Co-based superalloy subsystems, the ternary system Co-Al-Cr and the quasi-ternary system Co-Al-Cr-W with a constant amount of 10 at. % W, were deposited as thin-film materials libraries and analyzed in terms of phase formation and oxidation behavior at 500 °C in air. By combining energy-dispersive X-ray analysis and X-ray photoelectron spectroscopy high-throughput composition measurements, a detailed evaluation of the dependence between the initial multinary metal composition and the oxide scale composition which is forming upon oxidation on the surface of the thin film is established. Phase maps for both materials libraries are provided by high-throughput X-ray diffraction. In addition, the oxidation of a Co-Al-Cr-W bulk sample was analyzed and compared to a corresponding film in the library.

On-the-Fly Data Assessment for High-Throughput X-ray Diffraction Measurements.

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for the discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through the development of an approach that makes routine data assessment automatic and instantaneous. By extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large data sets, is highlighted. Deployment of such an approach not only improves the quality of data but also helps optimize the usage of expensive characterization resources by prioritizing measurements of the highest scientific impact. We anticipate our approach will become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.

(Invited) A Comprehensive Data Ecosystem to Accelerate Materials Research

In Materials-by-Design or Materials Genome Initiative (MGI) approaches to materials development, research (and researcher) friendly data tools are central to addressing the overarching challenge of creating new material science understanding from the vast amounts of data generated by both theory and experiments. At NREL, we have developed a comprehensive data ecosystem that includes automated data (and metadata) harvesting, expression of the aggregated data in an accessible database and the development of custom analysis tools as well as the deployment of public facing materials information resources. Using research examples taken from the Center for Next Generation Materials by Design EFRC and NREL’s internal development project on high-throughput x-ray diffraction analysis, we will present an overview of the complete NREL data ecosystem as it currently stands as well as a vision of how it might be integrated into multi-institute consortia such as the recently created DOE Energy Materials Networks. The public facing NREL MatDB database (materials.nrel.gov) contains the results of 200,000+ density functional theory calculations covering 30,000+ unique materials. Our recently established internal High-Throughput Experimental Materials database (HTEM-DB) currently contains data from more than 3300 combinatorial libraries representing roughly 86,000 different composition-processing synthesis combinations. Our data analytics use of HTEM-DB will be demonstrated by application to the rapid analysis of large x-ray diffraction datasets. HTEM-DB can be accessed via an API, direct SQL queries, a web interface and custom graphing-program based user tools to facilitate use by researchers. The overall result is a scientist-friendly extensible analysis environment with project specific machine learning and visualizations which is now both being put into use within Center for Next Generation Materials by Design and serving as the NREL access point for the NIST-led High-Throughput Experimental Materials Science Virtual Laboratory. A public facing version of the NREL HTEM-DB will be launched in 2017.

Semi-Supervised Approach to Phase Identification from Combinatorial Sample Diffraction Patterns

Manual attribution of crystallographic phases from high-throughput x-ray diffraction studies is an arduous task, and represents a rate-limiting step in high-throughput exploration of new materials. Here, we demonstrate a semi-supervised machine learning technique, SS-AutoPhase, which uses a two-step approach to identify automatically phases from diffraction data. First, clustering analysis is used to select a representative subset of samples automatically for human analysis. Second, an AdaBoost classifier uses the labeled samples to identify the presence of the different phases in diffraction data. SS-AutoPhase was used to identify the metallographic phases in 278 diffraction patterns from a FeGaPd composition spread sample. The accuracy of SS-AutoPhase was >82.6% for all phases when 15% of the diffraction patterns were used for training. The SS-AutoPhase predicted phase diagram showed excellent agreement with human expert analysis. Furthermore it was able to determine and identify correctly a previously unreported phase.

New Au–Cu–Al thin film shape memory alloys with tunable functional properties and high thermal stability

An Au–Cu–Al thin film materials library prepared by combinatorial sputter-deposition was characterized by high-throughput experimentation in order to identify and assess new shape memory alloys (SMAs) in this alloy system. Automated resistance measurements during thermal cycling between −20 and 250°C revealed a wide composition range that undergoes reversible phase transformations with martensite transformation start temperatures, reverse transformation finish temperatures and transformation hysteresis ranging from −15 to 149°C, 5 to 185°C and 8 to 60K, respectively. High-throughput X-ray diffraction analysis of the materials library confirmed that the phase-transforming compositions can be attributed to the existence of the β-AuCuAl parent phase and its martensite product. The formation of large amount of phases based on face-centered cubic (Au–Cu), Al–Cu and Al–Au is responsible for limiting the range of phase-transforming compositions. Selected alloys in this system show excellent thermal cyclic stability of the phase transformation. The functional properties of these alloys, combined with the inherent properties of Au-based alloys, i.e. aesthetic value, oxidation and corrosion resistance, makes them attractive as smart materials for a wide range of applications, including applications as SMAs for elevated temperatures in harsh environment.

High-Throughput Compositional and Structural Evaluation of a Lia(NixMnyCoz)Or Thin Film Battery Materials Library

A Lia(NixMnyCoz)Or cathode materials library was fabricated by combinatorial magnetron sputtering. The compositional analysis of the library was performed by a new high-throughput approach for Li-content measurement in thin films, which combines automated energy-dispersive X-ray spectroscopy, Deuteron-induced gamma emission, and Rutherford backscattering measurements. Furthermore, combining this approach with thickness measurements allows the mapping of density values of samples from the materials library. By correlating the obtained compositional data with structural data from high-throughput X-ray diffraction measurements, those compositions which show a layered (R3̅m) structure and are therefore most interesting for Li-battery applications (for cathode (positive) electrodes) can be rapidly identified. This structure was identified as being most pronounced in the compositions Li0.6(Ni0.16Mn0.35Co0.48)O2, Li0.7(Ni0.10Mn0.37Co0.51)O2, Li0.6(Ni0.23Mn0.33Co0.43)O2, Li0.3(Ni0.65Mn0.08Co0.26)O2, Li0.3(Ni0.63Mn0.08Co0.29)O2, Li0.4(Ni0.56Mn0.09Co0.34)O2, Li0.5(Ni0.45Mn0.13Co0.42)O2, and Li0.6(Ni0.34Mn0.14Co0.52)O2.

Dynamic composition determination in heterogeneous ensembles using angular autocorrelation functions as signatures

Signatures of heterogeneous ensembles composed of multiple types of random oriented particles are represented by the angular autocorrelation functions of the systems. These signatures are retrieved by averaging oversampled angular correlation functions calculated from simulated high throughput X-ray diffraction patterns. Using a component signature matrix, the composition of the heterogeneous system will be directly calculated from the signature of the ensemble. Dynamic composition of a heterogeneous ensemble is determined by fluctuation X-ray scattering using the angular autocorrelation functions as signatures.

Phase Behavior of Pseudobinary Precious Metal−Carbide Systems

Transition metal carbides exhibit a variety of interesting material properties, including electrochemical stability. When combined with precious metals, Ta and W carbides have shown promise as fuel cell electrode materials; yet, the phase behavior of these precious metal−carbide systems is largely unexplored. We investigated P-M-C phase behavior with P = Pt, Pd, and Ru and M = Ta and W using composition spread thin films. We attained limited control of the deposited carbide phase through variation of the sputter atmosphere and demonstrated decreased corrosion of W−C materials with increasing C content. A high-throughput X-ray diffraction and X-ray fluorescence experiment was employed for thin film characterization, which revealed solubility of Pt, Pd, and Ru in cubic WC. Density functional calculations of the lattice parameter dependence on carbon concentration enabled the determination of carbon concentration from the X-ray data as a function of transition metal stoichiometry. Our measurement of variatio...

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High Throughput X-ray Diffraction Analysis of Combinatorial Polycrystalline Thin Film Libraries

With the increasing demand for new materials, analytical techniques which are able to rapidly characterize a large number of samples are becoming indispensable. Thin film technology has the potential to improve the amount of information contained on as-deposited samples by creating compositionally graded libraries. Conventionally, raster scan methods are used to interrogate such libraries but, in this paper, a different approach is presented to provide a method of high-throughput data collection and analysis using an X-ray diffraction (XRD) probe. An extended X-ray beam was used to illuminate the libraries, and a large area detector was used to collect the data. A new algorithm "Bandit" has been employed to analyze the collected data and extract the crystallographic information. The results of the technique have been compared with the raster scans showing that the algorithm provides reliable data at a significantly increased data acquisition speed.

Manipulation of the crystallinity boundary of pulsed laser deposited high-k HfO2–TiO2–Y2O3 combinatorial thin films

Combinatorial library films of HfO2–TiO2–Y2O3, a high-k dielectric system, grown by pulsed laser deposition, exhibit visible boundary lines separating amorphous and crystalline phases. By changing processing space parameters, specifically substrate temperature during deposition, as well as the composition of the library film, we are able to manipulate the boundary and hence, the microstructural properties of the film. High-throughput x-ray diffraction and spectroscopic reflectometry are effective tools for measuring the properties of the resulting library films altered via these changes in processing. Electrical measurements confirm that the dielectric constant of the library films is composition and microstructure dependent.

An approach to high-throughput X-ray diffraction analysis of combinatorial polycrystalline thin film libraries

With the demand for higher rates of discovery in the materials field, characterization techniques that are capable of rapidly and reliably surveying the characteristics of large numbers of samples are essential. A chemical combinatorial approach using thin films can provide detailed phase diagrams without the need to produce multiple, individual samples. This is achieved with compositional gradients forming high-density libraries. Conventional raster scanning of chemical or structural probes is subsequently used to interrogate the libraries. A new, alternative approach to raster scanning is introduced to provide a method of high-throughput data collection and analysis using an X-ray diffraction probe. Libraries are interrogated with an extended X-ray source and the scattering data collected using an area detector. A simple technique of `partitioning' this scattering distribution enables determination of information comparable to conventional raster scanned results but in a dramatically reduced collection time. The technique has been tested using synthetic X-ray scattering distributions and those obtained from contrived samples. In all cases, the partitioning algorithm is shown to be robust and to provide reliable data; discrimination along the library principal axis is shown to be ∼500 µm and the lattice parameter resolution to be ∼10−3 Å mm−1. The limitations of the technique are discussed and future potential applications described.

High Throughput Screening of the Effect of Carbon Coating in LiFePO4 Electrodes

Two high throughput methods are described for the synthesis of graded composition electrode arrays of carbon-coated by anaerobic pyrolysis of solution precursors at in the presence of sucrose as a carbon-coating additive. In the first method, direct in situ calcination, the array was prepared directly on the current collectors by a one-step heat-treatment of the solutions. In the second method, post-synthesis array transfer (PoSAT), the samples were first pyrolyzed in an array of individual microtubes, before mixing with solvent, binder solution, and carbon black then depositing onto the current collector array. High throughput X-ray diffraction clearly showed impurity phases for samples containing sucrose per mol iron(III) nitrate or less, but mainly olivine phase for sucrose contents above . Superior results from the PoSAT method showed up to 80% theoretical capacity appearing sharply at the point where the sucrose produced a carbon coating and the characteristic decay in capacity with excess carbon. The PoSAT method is not restricted to and is recommended as a useful method for high throughput screening of positive electrode materials.

High-throughput X-ray diffraction for polymorph screening

High-throughput X-ray diffraction for polymorph screening