High-throughput determination of structural phase diagram and constituent phases using GRENDEL

Kusne, A. Gilad; Keller, David A.; Anderson, Assaf Y.; Zaban, Arie; Takeuchi, Ichiro

doi:10.1088/0957-4484/26/44/444002

Cited by 76 publications

(64 citation statements)

References 25 publications

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“…7: (a) hypothesis-driven design and synthesis of a "library" sample with variations in the materials parameter(s) of interest [118][119][120][121] (typically composition); (b) rapid, local, and automated interrogation of the library for the properties of interest; 53,[122][123][124][125][126][127][128] and (c) analysis, mining, display, and curation of the resultant data. [129][130][131] Each step presents current challenges that must be overcome before HTE methodologies can be widely deployed.…”

Section: Challenges For Htementioning

confidence: 99%

“…The benefits of machine learning for accelerated materials data analysis have already been realized, with numerous studies showing the great potential for research and discovery. [199][200][201] These studies include a wide range of materials analysis challenges including crystal structure [202][203][204] and phase diagram 130,[205][206][207] determination, materials property predictions, 208,209 micrograph analysis, 210,211 development of interatomic potentials [212][213][214] and energy functionals 215 to improve materials simulations, and on-the-fly data analysis of high-throughput experiments. 216 …”

Section: Informaticsmentioning

confidence: 99%

“…The authors propose the following strategic action items to enable HTE, a key component of the MGI, to significantly 130 contribute to accelerated materials discovery and commercialization (these action items are the opinions of the authors, not necessarily the organization they represent):…”

Section: Path Forwardmentioning

confidence: 99%

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Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

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The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. A major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.

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Section: Challenges For Htementioning

confidence: 99%

Section: Informaticsmentioning

confidence: 99%

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Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

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show abstract

“…These end members directly represent the diffraction patterns of the structures present at a given multi-phase region. Due to the ease of interpreting its results, NMF has been used in several unsupervised and semi-supervised systems for decomposing XRD patterns, and has given very encouraging results 11,17,18 . However, some significant challenges to utilizing the full potential of this method remain.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

et al. 2018

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Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peakshifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition, and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique singlephase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset, and demonstrates robust accuracy and identification abilities.

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“…Compressed, low-dimensional representations are typically more desirable than sparse, highdimensional feature descriptors as low-dimensional representations are able to emphasize the most relevant dimensions (e.g., combinations of synthesis temperatures used) while also avoiding the so-called "curse of dimensionality." 37,38 Indeed, neural network-based dimensionality reduction has seen success in learning representations of meaningful word vectors, 39 hierarchical image filters, 40 representations of organic chemicals, 1,41 and quantum spin systems. 42 While neural networks show broad potential for learning compressed data representations, they often consume large amounts of training data to achieve high accuracies, 43,44 and standard training sets often include millions of data points.…”

Section: Introductionmentioning

confidence: 99%

Virtual screening of inorganic materials synthesis parameters with deep learning

Kim

Huang

Jegelka³

et al. 2017

npj Comput Mater

171

136

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Virtual materials screening approaches have proliferated in the past decade, driven by rapid advances in first-principles computational techniques, and machine-learning algorithms. By comparison, computationally driven materials synthesis screening is still in its infancy, and is mired by the challenges of data sparsity and data scarcity: Synthesis routes exist in a sparse, highdimensional parameter space that is difficult to optimize over directly, and, for some materials of interest, only scarce volumes of literature-reported syntheses are available. In this article, we present a framework for suggesting quantitative synthesis parameters and potential driving factors for synthesis outcomes. We use a variational autoencoder to compress sparse synthesis representations into a lower dimensional space, which is found to improve the performance of machine-learning tasks. To realize this screening framework even in cases where there are few literature data, we devise a novel data augmentation methodology that incorporates literature synthesis data from related materials systems. We apply this variational autoencoder framework to generate potential SrTiO 3 synthesis parameter sets, propose driving factors for brookite TiO 2 formation, and identify correlations between alkali-ion intercalation and MnO 2 polymorph selection.

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High-throughput determination of structural phase diagram and constituent phases using GRENDEL

Cited by 76 publications

References 25 publications

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

Virtual screening of inorganic materials synthesis parameters with deep learning

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