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

Bunn, Jonathan Kenneth; Hu, Jianjun; Hattrick‐Simpers, Jason

doi:10.1007/s11837-016-2033-8

Cited by 35 publications

(30 citation statements)

References 25 publications

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“…There have been a number of reports dealing with machine learning (ML) attempts in XRD-based materials research 20–34 and ML is known to be very powerful when associated with high-throughput experiments 30–34 . In particular, these include a prestigious ML algorithm for XRD-based phase matching based on a convolutive non-negative matrix factorization, the so-called AgileFD 35,36 .…”

Section: Introductionmentioning

confidence: 99%

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

et al. 2020

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Here we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray powder diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

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Section: Introductionmentioning

confidence: 99%

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

et al. 2020

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“…Unsupervised learning using fine-level fingerprints (and clustering based on these fingerprints) has led to the classification of materials based on their phases or structural characteristics [11,12]. Using the XRD spectrum itself as the fingerprint, high-throughput XRD measurements for various compositional spreads [11,12,[102][103][104][105] have been used to automate the creation of phase diagrams. Essentially, features of the XRD spectra are used to distinguish between phases of a material as a function of composition.…”

Section: Examples Of Learning Based On Sub-angstrom-level Descriptorsmentioning

confidence: 99%

Machine learning in materials informatics: recent applications and prospects

et al. 2017

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Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods-due to the cost, time or effort involved-but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative-extending into new materials spaces-provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven "materials informatics" strategies undertaken in the last decade, and identifies some challenges the community is facing and those that should be overcome in the near future.

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“…X-ray magnetic circular dichroism has been used to investigate the electronic structure of quantum spin liquid α-RuCl 3 116 These advanced techniques require unique synchrotron or neutron facilities, and their implementation in the screening of combinatorial libraries has been limited to date 117,118 The high-throughput approach generates large highdimensional datasets, requiring analysis techniques that can rapidly turn raw data into knowledge with limited or no human supervision 119 . This community adopted dimensionality reduction and data mining techniques early on [120][121][122][123] , gradually creating a diverse ML toolbox for the rapid digestion of combinatorial data [124][125][126][127][128][129][130] . One very common task is to quickly group (cluster) measurements from different points of a combinatorial library, and ML algorithms have been routinely applied to a large number of X-ray diffraction patterns to delineate structural phases and rapidly construct a composition-structure relationship 120,124,129,130 .…”

Section: Autonomous Materials Laboratoriesmentioning

confidence: 99%

Artificial intelligence for search and discovery of quantum materials

et al. 2021

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Artificial intelligence and machine learning are becoming indispensable tools in many areas of physics, including astrophysics, particle physics, and climate science. In the arena of quantum materials, the rise of new experimental and computational techniques has increased the volume and the speed with which data are collected, and artificial intelligence is poised to impact the exploration of new materials such as superconductors, spin liquids, and topological insulators. This review outlines how the use of data-driven approaches is changing the landscape of quantum materials research. From rapid construction and analysis of computational and experimental databases to implementing physical models as pathfinding guidelines for autonomous experiments, we show that artificial intelligence is already well on its way to becoming the lynchpin in the search and discovery of quantum materials.

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

Cited by 35 publications

References 25 publications

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

Machine learning in materials informatics: recent applications and prospects

Artificial intelligence for search and discovery of quantum materials

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