Data flow modeling, data mining and QSAR in high-throughput discovery of functional nanomaterials

2011 ◽  
Vol 35 (4) ◽  
pp. 671-678 ◽  
Author(s):  
Yang Yang ◽  
Tian Lin ◽  
Xiao L. Weng ◽  
Jawwad A. Darr ◽  
Xue Z. Wang
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
Data Flow ◽  
Flow Modeling ◽  
Functional Nanomaterials ◽  
Modeling Data

Accelerated Discovery of High-Refractive-Index Polyimides via First-Principles Molecular Modeling, Virtual High-Throughput Screening, and Data Mining

2019 ◽  
Author(s):  
Mohammad Atif Faiz Afzal ◽  
Mojtaba Haghighatlari ◽  
Sai Prasad Ganesh ◽  
Chong Cheng ◽  
Johannes Hachmann
Keyword(s):  
Data Mining ◽  
Refractive Index ◽  
High Throughput ◽  
First Principles ◽  
High Throughput Screening ◽  
Large Scale ◽  
Computational Study ◽  
High Refractive Index ◽  
Structural Features ◽  
Learning Program

<div>We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. This rapid and efficient approach yields a number of highly promising leads compounds. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.</div>

Deriving Knowledge through Data Mining High-Throughput Screening Data

2004 ◽  
Vol 47 (25) ◽  
pp. 6373-6383 ◽  
Author(s):  
David J. Diller ◽  
Doug W. Hobbs
Keyword(s):  
Data Mining ◽  
High Throughput ◽  
High Throughput Screening

High-throughput and data mining with ab initio methods

2004 ◽  
Vol 16 (1) ◽  
pp. 296-301 ◽  
Author(s):  
Dane Morgan ◽  
Gerbrand Ceder ◽  
Stefano Curtarolo
Keyword(s):  
Data Mining ◽  
Ab Initio ◽  
High Throughput ◽  
Ab Initio Methods

Data Mining and Optimize TCM Prescription Compatibility Based on WDS-PLS

2014 ◽  
Vol 687-691 ◽  
pp. 1385-1388
Author(s):  
Zhuo Wang ◽  
Bin Nie ◽  
Ri Yue Yu
Keyword(s):  
Mathematical Modeling ◽  
Experimental Data ◽  
Data Mining ◽  
Regression Coefficient ◽  
Uniform Design ◽  
Herbal Medicines ◽  
Data Standardization ◽  
Modeling Data ◽  
Pls Method ◽  
Original Formula

Data mining and optimize traditional Chinese medicine (TCM) prescription compatibility based on wavelet denoise spectral and partial least squares (WDS-PLS). Method: First of all, experimental design: with reference to the original formula, the herbal medicines in a prescription designed nine formula based on mixing uniform design; Secondly, obtain experimental data and data standardization; Finally, mathematical modeling, data mining and optimize TCM prescription compatibility base on WDS-PLS.Results: gain the regression coefficient and equation, VIP sorting, loadings Bi plot, and seek out the optimized direction of the prescription. Conclusion: the method data mining and optimize the compatibility of the dachengqi decoction is feasible and effective.

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