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

0334675 - ÚI 2010 RIV GB eng B - Monography
Baerns, M. - Holeňa, Martin
Combinatorial Development of Solid Catalytic Materials. Design of High Throughput Experiments, Data Analysis, Data Mining.
London: Imperial College Press, 2009. 178 s. Catalytic Science Series, 7. ISBN 978-1-84816-343-0
R&D Projects: GA ČR GA201/08/0802; GA ČR GA201/08/1744; GA ČR GEICC/08/E018
Institutional research plan: CEZ:AV0Z10300504
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
http://aleph.lib.cas.cz/F/?func=direct&doc_number=000727539&local_base=AV

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.
Permanent Link: http://hdl.handle.net/11104/0179349