Computer‐Aided Design of Solid Catalysts

The sections in this article are Introduction Theoretical Methods Rational Methods for the Design of Catalytic Experiments The Statistical Design of Experiments Optimization Methods for Empirical Objective Functions Evolutionary Methods Feature 1 Feature 2 Feature 3 Feature 4 Other Stochastic Methods Deterministic Methods Group 1 Methods Group 2 Methods Group 3 Methods Concluding Remarks Data Analysis and Data Mining Statistical Methods: An Overview Artificial Neural Networks Concluding Remarks Conclusions Keywords: computer-aided design; combinatorial catalyst development; statistical methods; factorial design; data analysis; data mining; artificial neural networks; genetic algorithms; solid catalysts; density functional theory; micro-kinetic analysis

[1]  David C. Miller,et al.  Computer-aided molecular design using Tabu search , 2005, Comput. Chem. Eng..

[2]  Claude Mirodatos,et al.  Combinatorial Approaches to Heterogeneous Catalysis: Strategies and Perspectives for Academic Research , 2001 .

[3]  Artificial neural network-aided development of supported Co catalyst for preferential oxidation of CO in excess hydrogen , 2005 .

[4]  Xiaoqun Wu,et al.  Artificial neural network aided design of catalyst for propane ammoxidation , 1997 .

[5]  B. Ogunnaike,et al.  Development and optimization of NOx storage and reduction catalysts using statistically guided high-throughput experimentation , 2004 .

[6]  J. M. Serra,et al.  Application of artificial neural networks to combinatorial catalysis: modeling and predicting ODHE catalysts. , 2002, Chemphyschem : a European journal of chemical physics and physical chemistry.

[7]  Akira Endou,et al.  Combinatorial computational chemistry approach to the design of metal catalysts for deNOx , 2004 .

[8]  Manfred Baerns,et al.  Fundamental and combinatorial approaches in the search for and optimisation of catalytic materials for the oxidative dehydrogenation of propane to propene , 2001 .

[9]  José M. Serra,et al.  Development of a low temperature light paraffin isomerization catalysts with improved resistance to water and sulphur by combinatorial methods , 2003 .

[10]  F. Bickelhaupt,et al.  Activation of C–H, C–C and C–I bonds by Pd and cis-Pd(CO)2I2. Catalyst–substrate adaptation , 2005 .

[11]  Estefania Argente,et al.  Optimisation of olefin epoxidation catalysts with the application of high-throughput and genetic algorithms assisted by artificial neural networks (softcomputing techniques) , 2005 .

[12]  Jaume Giralt,et al.  Kinetic modelling of catalytic wet air oxidation of phenol by simulated annealing , 2001 .

[13]  Venkat Venkatasubramanian,et al.  Catalyst design: knowledge extraction from high-throughput experimentation , 2003 .

[14]  Huang Kai,et al.  Artificial neural network-aided design of a multi-component catalyst for methane oxidative coupling , 2001 .

[15]  Martin Holeňa,et al.  Feedforward neural networks in catalysis: A tool for the approximation of the dependency of yield on catalyst composition, and for knowledge extraction , 2003 .

[16]  A. Endou,et al.  Design of new catalysts for ecological high-quality transportation fuels by combinatorial computational chemistry and tight-binding quantum chemical molecular dynamics approaches , 2004 .

[17]  J. Pinto,et al.  Preparation of high loading silica supported nickel catalyst: simultaneous analysis of the precipitation and aging steps , 1999 .

[18]  J. Richardson,et al.  Properties of ceramic foam catalyst supports: mass and heat transfer , 2003 .

[19]  Manfred Baerns,et al.  Fundamental insights into the oxidative dehydrogenation of ethane to ethylene over catalytic materials discovered by an evolutionary approach , 2003 .

[20]  András Tompos,et al.  Development of catalyst libraries for total oxidation of methane: A case study for combined application of “holographic research strategy and artificial neural networks” in catalyst library design , 2005 .

[21]  M. Tagliabue,et al.  Multivariate approach to zeolite synthesis , 2003 .

[22]  Peter Claus,et al.  High-throughput synthesis and screening of catalytic materials: Case study on the search for a low-temperature catalyst for the oxidation of low-concentration propane , 2001 .

[23]  Kohji Omata,et al.  Optimization of Cu oxide catalysts for methanol synthesis by combinatorial tools using 96 well microplates, artificial neural network and genetic algorithm , 2004 .

[24]  Huang Kai,et al.  Catalyst design for methane oxidative coupling by using artificial neural network and hybrid genetic algorithm , 2003 .

[25]  Motoi Sasaki,et al.  Application of a neural network to the analysis of catalytic reactions Analysis of NO decomposition over Cu/ZSM-5 zeolite , 1995 .

[26]  Selim Senkan,et al.  Combinatorial Heterogeneous Catalysis-A New Path in an Old Field. , 2001, Angewandte Chemie.

[27]  Michael L. Turner,et al.  Mid-IR spectroscopy for rapid on-line analysis in heterogeneous catalyst testing , 2003 .

[28]  A. M. Amat,et al.  Oxidative degradation of 2,4-xylidine by photosensitization with 2,4,6-triphenylpyrylium: homogeneous and heterogeneous catalysis. , 2004, Chemosphere.

[29]  T. Hattori,et al.  Expert systems approach to computer-aided design of catalysts , 1989 .

[30]  M. Yamada,et al.  Optimization of Cu oxide catalyst for methanol synthesis under high CO2 partial pressure using combinatorial tools , 2004 .

[31]  L. Gladden,et al.  Development of a Genetic Algorithm for Molecular Scale Catalyst Design , 1997 .

[32]  J. M. Serra,et al.  Discovery of new paraffin isomerization catalysts based on SO42−/ZrO2 and WOx/ZrO2 applying combinatorial techniques , 2003 .

[33]  M. Baerns,et al.  Application of a genetic algorithm and a neural network for the discovery and optimization of new solid catalytic materials , 2004 .

[34]  Claude Mirodatos,et al.  How to Design Diverse Libraries of Solid Catalysts , 2003 .

[35]  Claude Mirodatos,et al.  The development of descriptors for solids: teaching "catalytic intuition" to a computer. , 2004, Angewandte Chemie.

[36]  Martin Holena,et al.  Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set , 2004, J. Chem. Inf. Model..

[37]  Takao Motoki,et al.  New reaction simulator “LUMMOX” and its application for prediction of catalytic activities , 2004, J. Comput. Chem..

[38]  Combinatorial computational chemistry approach to the design of methanol synthesis catalyst , 2002 .

[39]  M. Menéndez,et al.  Oxidative dehydrogenation of propane in an inert membrane reactor , 2000 .

[40]  Stoltze,et al.  Bridging the "pressure gap" between ultrahigh-vacuum surface physics and high-pressure catalysis. , 1985, Physical review letters.

[41]  H. Nakanishi,et al.  Cyclohexane dehydrogenation catalyst design based on spin polarization effects , 2004 .

[42]  J. M. Serra,et al.  Heterogeneous combinatorial catalysis applied to oil refining, petrochemistry and fine chemistry , 2005 .

[43]  J. Nørskov,et al.  An interpretation of the high-pressure kinetics of ammonia synthesis based on a microscopic model , 1988 .

[44]  Thomas R. Cundari,et al.  Design of a Propane Ammoxidation Catalyst Using Artificial Neural Networks and Genetic Algorithms , 2001 .

[45]  R. Harding,et al.  Evaluation of sparse data sets obtained from microactivity testing of FCC catalysts , 1999 .

[46]  Estefania Argente,et al.  Neural networks for modelling of kinetic reaction data applicable to catalyst scale up and process control and optimisation in the frame of combinatorial catalysis , 2003 .

[47]  M. Koyama,et al.  Tight-binding quantum chemical molecular dynamics method: a novel approach to the understanding and design of new materials and catalysts , 2005 .

[48]  Manfred Baerns,et al.  Ethylene and propene by oxidative dehydrogenation of ethane and propane: ‘Performance of rare-earth oxide-based catalysts and development of redox-type catalytic materials by combinatorial methods’ , 2000 .

[49]  Suljo Linic,et al.  Selectivity driven design of bimetallic ethylene epoxidation catalysts from first principles , 2004 .

[50]  Frédéric Clerc,et al.  Effect of the Genetic Algorithm Parameters on the Optimisation of Heterogeneous Catalysts , 2005 .

[51]  José M. Serra,et al.  Integrating high-throughput characterization into combinatorial heterogeneous catalysis: unsupervised construction of quantitative structure/property relationship models , 2005 .

[52]  Manfred Baerns,et al.  An evolutionary approach in the combinatorial selection and optimization of catalytic materials , 2000 .

[53]  Momoji Kubo,et al.  Combinatorial computational chemistry approach to the design of deNOx catalysts , 2000 .

[54]  Estefania Argente,et al.  Application of artificial neural networks to high-throughput synthesis of zeolites , 2005 .

[55]  András Tompos,et al.  Holographic research strategy for catalyst library design: Description of a new powerful optimisation method , 2003 .

[56]  Claude Mirodatos,et al.  Design of Discovery Libraries for Solids Based on QSAR Models , 2005 .

[57]  Tadashi Hattori,et al.  Neural network as a tool for catalyst development , 1995 .

[58]  C. Snively,et al.  Multivariate and univariate analysis of infrared imaging data for high-throughput studies of NH3 decomposition and NOx storage and reduction catalysts , 2004 .

[59]  Estefania Argente,et al.  Can artificial neural networks help the experimentation in catalysis , 2003 .

[60]  Tadashi Hattori,et al.  Application of neural network to estimation of catalyst deactivation in methanol conversion , 2004 .

[61]  Gholamreza Zahedi,et al.  A Neural Network Approach for Prediction of the CuO-ZnO-Al2O3 Catalyst Deactivation , 2005 .

[62]  Baohui Li,et al.  A simulated annealing study of Si,Al distribution in the omega framework 1 Supported by the National , 1999 .

[63]  Sen Han,et al.  Design of CO2 hydrogenation catalyst by an artificial neural network , 2001 .

[64]  L. F. Gladden,et al.  Heterogeneous Catalyst Design Using Stochastic Optimization Algorithms , 2000, J. Chem. Inf. Comput. Sci..

[65]  A. Miyamoto,et al.  Integrated computational chemistry system for catalysts design , 1999 .

[66]  Bernard F. Buxton,et al.  Support Vector Machines in Combinatorial Chemistry , 2001 .

[67]  Karen Wilson,et al.  Structure-reactivity correlations in MgAl hydrotalcite catalysts for biodiesel synthesis , 2005 .