“Secondary” descriptor development for zeolite framework design: an informatics approach

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

doi:10.1016/s0926-860x(03)00284-9

Cited by 31 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Partial Least Squares (PLS) regression method is particularly appropriate for QSAR formulations as it is used to predict properties of properties based on variables (even some which may have only indirect impact) which collectively relate to these properties. In addition, model parameters in PLS can be more accurately calculated with increasing number of relevant variables and observations [3]. PLS also has the advantage over multiple linear regressions for handling of collinearity and missing data [3].…”

Section: Quantitative Methodologymentioning

confidence: 99%

“…In addition, model parameters in PLS can be more accurately calculated with increasing number of relevant variables and observations [3]. PLS also has the advantage over multiple linear regressions for handling of collinearity and missing data [3]. Since we needed to cope with collinearity (for example, Q* oct and Q* N ) in variables with limited samples in this paper, we chose PLS regression as a modeling approach for developing a QSAR-type formulation.…”

Section: Quantitative Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Virtual Screening and QSAR Formulations for Crystal Chemistry

Suh

Rajan

2005

QSAR Comb. Sci.

Self Cite

View full text Add to dashboard Cite

In this paper, we demonstrate how partial least squares multivariate analysis can be used to develop a Quantitative Structure -Activity Relationship (QSAR). The analysis is applied to the prediction of bulk modulus based on a combinatorial analysis of crystalchemistry descriptors used in ab-initio calculations. The value of developing these QSAR formulations based on theoretical calculations is discussed in the context of its value as a screening tool for "virtual" compounds and chemistry.

show abstract

Section: Quantitative Methodologymentioning

confidence: 99%

Section: Quantitative Methodologymentioning

confidence: 99%

Virtual Screening and QSAR Formulations for Crystal Chemistry

Suh

Rajan

2005

QSAR Comb. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Both are powerful tools for the analysis of materials and have been used to address materials-science issues for a variety of reasons and materials [8][9][10][11][12][13][14][15][16][17].…”

Section: Data-mining Techniquesmentioning

confidence: 99%

Mining the bulk positron lifetime

Aourag¹,

Guittom

2009

Physica Status Solidi (b)

View full text Add to dashboard Cite

We introduce a new approach to investigate the bulk positron lifetimes of new systems based on data‐mining techniques. Through data mining of bulk positron lifetimes, we demonstrate the ability to predict the positron lifetimes of new semiconductors on the basis of available semiconductor data already studied. Informatics techniques have been applied to bulk positron lifetimes for different tetrahedrally bounded semiconductors in order to discover computational design rules. (© 2009 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

show abstract

“…One publication describes the use of PCA as a multivariate projection technique to visualize significant parts of a large molten salts database [13]. 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%

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

Sieg¹,

Suh²,

Schmidt³

et al. 2007

QSAR Comb. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“Secondary” descriptor development for zeolite framework design: an informatics approach

Cited by 31 publications

References 10 publications

Virtual Screening and QSAR Formulations for Crystal Chemistry

Virtual Screening and QSAR Formulations for Crystal Chemistry

Mining the bulk positron lifetime

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

Contact Info

Product

Resources

About