Beginning Relational Data Modeling

2005 ◽  
Keyword(s):  
Data Modeling ◽  
Relational Data

Fuzzy XML data modeling with the UML and relational data models

2007 ◽  
Vol 63 (3) ◽  
pp. 972-996 ◽  
Author(s):  
Z.M. Ma ◽  
Li Yan
Keyword(s):  
Data Modeling ◽  
Data Models ◽  
Relational Data ◽  
Xml Data ◽  
Fuzzy Xml

Relational Data Modeling for Geographic Information Systems

1999 ◽  
Vol 10 (2) ◽  
pp. 27-34 ◽  
Author(s):  
Lawrence A. West Jr. ◽  
Brian E. Mennecke
Keyword(s):  
Information Systems ◽  
Geographic Information Systems ◽  
Data Modeling ◽  
Geographic Information ◽  
Relational Data

Agile Development with 3D Data Modeling

2015 ◽  
Vol 10 (6) ◽  
pp. 558 ◽  
Author(s):  
Kristian Sestak ◽  
Zdenek Havlice
Keyword(s):  
Data Modeling ◽  
Agile Development ◽  
3D Data

Combining First-Principles and Data Modeling for the Accurate Prediction of the Refractive Index of Organic Polymers

2017 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
First Principles ◽  
Data Modeling ◽  
Accurate Prediction ◽  
Index Of Refraction ◽  
Structure Theory ◽  
Organic Polymers ◽  
Number Density ◽  
Lorenz Equation ◽  
In Silico Studies ◽  
Wide Range

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3–1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that an guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and highly economical path to determining the RI values for a wide range of organic polymers.

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