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Combinatorial Development of Solid Catalytic Materials. Design of High Throughput Experiments, Data Analysis, Data Mining

SYSNO ASEP	0334675
Document Type	B - Monograph
R&D Document Type	Monograph
Title	Combinatorial Development of Solid Catalytic Materials. Design of High Throughput Experiments, Data Analysis, Data Mining
Author(s)	Baerns, M. (DE) Holeňa, Martin (UIVT-O)_{SAI, RID}
Issue data	London: Imperial College Press, 2009
ISBN	978-1-84816-343-0
Series	Catalytic Science Series
Series number	7
Number of pages	178 s.
Number of copy	1400
Language	eng - English
Country	GB - United Kingdom
Keywords	combinatorial catalyst design ; high-throughput experimentation ; computer-aided materials search ; catalyst design ; combinatorial computational chemistry ; data mining ; data analysis ; genetic algorithms ; artificial neural networks
Subject RIV	IN - Informatics, Computer Science
R&D Projects	GA201/08/0802 GA ČR - Czech Science Foundation (CSF)
	GA201/08/1744 GA ČR - Czech Science Foundation (CSF)
	GEICC/08/E018 GA ČR - Czech Science Foundation (CSF)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
DOI	10.1142/9781848163447_fmatter
Annotation	The book provides a comprehensive treatment of combinatorial development of heterogeneous catalysts. In particular, two computer-aided approaches that have played a key role in combinatorial catalysis and high-throughput experimentation during the last decade - evolutionary optimization and artificial neural networks - are described. The book describes evolutionary optimization in the context of methods of searching for optimal catalytic materials, including statistical design of experiments, and neural networks in the context of data analysis. It is the first book that demystifies the attractiveness of artificial neural networks, explaining its rational fundamental - their universal approximation capability. At the same time, it shows the limitations of that capability and describes two methods for how it can be improved. The book is also the first that presents automatic generating of problem-tailored genetic algorithms, and tuning evolutionary algorithms with neural networks.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2010

Number of the records: 1