Principal Component Analysis of Catalytic Functions in the Composition Space of Heterogeneous Catalysts

Sieg, Simone; Suh, Changwon; Schmidt, Timm; Stukowski, M.; Rajan, Krishna; Maier, Wilhelm F.

doi:10.1002/qsar.200620074

Cited by 37 publications

(27 citation statements)

References 13 publications

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“…This could include groupings from dimensional analysis (Rajan et al, 2009), simple relational features (Kanter and Veeramachaneni, 2015), PCA (Sieg et al, 2006), or some other method. In all cases, care must be taken to avoid incompatible operations (e.g., do not add an atomic radius to an ionization potential).…”

Section: Feature Extractionmentioning

confidence: 99%

Theory-Guided Machine Learning in Materials Science

2016

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Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require careful choices of data processing and machine learning methods. Here, we outline potential pitfalls involved in using machine learning without robust protocols. We address some problems of overfitting to training data using decision trees as an example, rational descriptor selection in the field of perovskites, and preserving physical interpretability in the application of dimensionality reducing techniques such as principal component analysis. We show how proceeding without the guidance of domain knowledge can lead to both quantitatively and qualitatively incorrect predictive models.

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Section: Feature Extractionmentioning

confidence: 99%

Theory-Guided Machine Learning in Materials Science

2016

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“…[129] Sieg et al showed with quaternary composition spaces that the proper mathematical modeling of such relationships can be accomplished readily with the help of SVMs, multilevel B-splines, or Kriging ( Figure 11). [130] There have been significant developments in the use of IT tools for HTE.…”

Section: Reviewsmentioning

confidence: 99%

Combinatorial and High‐Throughput Materials Science

2007

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There is increasing acceptance of high-throughput technologies for the discovery, development, and optimization of materials and catalysts in industry. Over the years, the relative synchronous development of technologies for parallel synthesis and characterization has been accompanied by developments in associated software and information technologies. This Review aims to provide a comprehensive overview on the state of the art of the field by selected examples. Technologies developed to aid research on complex materials are covered as well as databases, design of experiment, data-mining technologies, modeling approaches, and evolutionary strategies for development. Different methods for parallel synthesis provide single sample libraries, gradient libraries for electronic or optical materials, similar to polymers and catalysts, and products produced through formulation strategies. Many examples illustrate the variety of isolated solutions and document the barely recognized variety of new methods for the synthesis and analysis of almost any material. The Review ends with a summary of success stories and statements on still-present problems and future tasks.

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“…For example, principle component analysis has recently been applied to the problem of heterogeneous catalysis using only composition variables as components. 6 The ability to include crystalline phase and fiber texture in this type of analysis would greatly facilitate the discovery of new materials not only for catalysis but also for the broad range of applications conducive to investigation with combinatorial methods. We present a simple configuration for simultaneous measurement of thin film composition, thickness, structure, and fiber texture using high-energy x-ray diffraction ͑XRD͒ and energy-dispersive x-ray fluorescence spectroscopy ͑XRF͒.…”

Section: Introductionmentioning

confidence: 99%

High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films

Gregoire

Dale

Kazimirov

et al. 2009

Review of Scientific Instruments

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High-throughput crystallography is an important tool in materials research, particularly for the rapid assessment of structure-property relationships. We present a technique for simultaneous acquisition of diffraction images and fluorescence spectra on a continuous composition spread thin film using a 60 keV x-ray source. Subsequent noninteractive data processing provides maps of the diffraction profiles, thin film fiber texture, and composition. Even for highly textured films, our diffraction technique provides detection of diffraction from each family of Bragg reflections, which affords direct comparison of the measured profiles with powder patterns of known phases. These techniques are important for high throughput combinatorial studies as they provide structure and composition maps which may be correlated with performance trends within an inorganic library.

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Principal Component Analysis of Catalytic Functions in the Composition Space of Heterogeneous Catalysts

Cited by 37 publications

References 13 publications

Theory-Guided Machine Learning in Materials Science

Theory-Guided Machine Learning in Materials Science

Combinatorial and High‐Throughput Materials Science

High energy x-ray diffraction/x-ray fluorescence spectroscopy for high-throughput analysis of composition spread thin films

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