Quantitative Structure-Activity Relationships (QSARs) for Materials Science

Rajan, Krishna; Suh, Changwon; Rajagopalan, Arun; Li, Xiang

doi:10.1557/proc-700-s7.5

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…Rajan and co-workers [111][112][113][114] applied data-mining methods, such as PCA, and predictive methods, such as partial least squares (PLS) to certain fields of materials science (zeolites, semiconductors, etc.). They connected conventional materials databases with experimental data sets in searches for correlations and patterns.…”

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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Section: Reviewsmentioning

confidence: 99%

Combinatorial and High‐Throughput Materials Science

2007

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“…Furthermore, the group used PCA to analyze crystallographic structure data for all known zeolite structure types to help designing new zeolites [14]. Another example for the application of PCA for QSARs in materials science is given by Rajan et al [1].…”

Section: Pcamentioning

confidence: 99%

“…Although combinatorial experiments provide a means of generating large amounts of experimental data, they do not necessarily lead to high-throughput interpretation of that data. To solve this problem, it is indispensable to integrate the experimental aspects of combinatorial synthesis with the computational aspects of information-based design of materials [1].…”

Section: Introductionmentioning

confidence: 99%

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

Sieg¹,

Suh²,

Schmidt³

et al. 2007

QSAR Comb. Sci.

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The objective of this paper is to identify which combinations of constituents of heterogeneous catalysts are related to which final products from a large combinatorially generated dataset. To this end, data on the composition of catalysts are combined with a set of four catalytic activities, and analyzed using a Principle Component Analysis (PCA). From the first attempt of PCA, a dataset of pentanary-mixed metal oxides, Cr a Mo b Ni cCo d Mn e O x , is visualized on the planes of principal components (PCs) in a way of linking the composition spread with the activities toward each product. It has been possible to identify three key elements (Mo, Co, and Cr) out of five by PCA. Having these key elements identified from PCA, a ternary system of mixed metal oxide catalysts, Mo a Co b Cr c O x , was analyzed again in the PC space for further relationships between composition of catalysts and activities of final products. These two case studies demonstrate how large multidimensional datasets can be analyzed to identify relevant key parameters for catalyst discovery and optimization.

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“…[62][63][64][65][66][67][68][69][70][71][72][73][74] The goal is to explore the individual correlations of the specifi c variables (i.e., LVs) used in the ab-initio calculations, which take into account their relative impact on fi nal properties. The partial least squares (PLS) regression method is particularly appropriate for QSAR formulations as it is used to predict properties based on variables (even some which may have only indirect impact) which collectively relate to these properties.…”

Section: Soft Modeling: Application To Crystal Chemistry Designmentioning

confidence: 99%