Journal of Molecular Catalysis A: Chemical 228 (2005) 211–225 Application of computational methods to catalytic systems Fernando Ruette a,∗ , Morella S´ anchez b , Anibal Sierraalta a , Claudio Mendoza c , Rafael A ˜ nez a , Luis Rodr´ ıguez a , Orlando Lisboa a , Judith Daza a , Pastor Manrique a , Zhandra Perdomo a , Marcos Rosa-Brussin d a Laboratorio de Qu´ ımica Computacional, Centro de Qu´ ımica, Instituto Venezolano de Investigaciones Cient´ ıficas, Apartado 21827, Caracas 1020-A, Venezuela b Departamento de Qu´ ımica, Instituto Universitario de Tecnolog´ ıa Federico Rivero-Palacio, Apartado 40347, Caracas, Venezuela c Laboratorio de F´ ısica Computacional, Centro de F´ ısica, Instituto Venezolano de Investigaciones Cient´ ıficas (IVIC), Apartado 21827, Caracas, Venezuela d Centro de Cat´ alisis, Petr´ oleo y Petroqu´ ımica, Facultad de Ciencias, Universidad Central de Venezuela, Caracas, Venezuela Available online 18 November 2004 Abstract An analysis of the complexity involved in the computational modeling of catalytic reactions is presented, including a review of applications and limitations of ab initio methods in this context. The foundations of parametric methods based on simulation techniques and the use of elementary parametric energy functionals are briefly discussed. We describe the implementation and facilities of a quantum-chemical package, usually referred to as CATIVIC, especially tailored for catalytic processes. Finally, the application of this code to catalytic systems is illustrated with several examples. © 2004 Published by Elsevier B.V. Keywords: Quantum-chemistry; Catalytic modeling; Catalysis complexity; CATIVIC; Parametric method; Catalytic processes 1. Introduction The importance of catalysis may be exemplified by the following facts: (i) the synthesis of about 60–80% of indus- trial chemicals relies on catalysts [1,2]; (ii) the world market for catalytic products is about US$ 10 trillion [3]; and (iii) the total investment on royalties and licenses in the field is more than US$ 3 billions [4]. In addition, catalysts are being massively used in automobile emission control and, more re- cently, in very selective devices in indoor environments [5,6] for the elimination of tobacco smoke and nasty toilet odors and in garbage disposal. Also for volatile organic compounds (VOCs) in cooking, wood products, plastics, office equip- ment, glues, the treatment of microbial contamination from ventilation systems and domestic water pollutants. In general, the quality of live is to improve by using “smart catalytic” devices – catalysts that modify their activity depending on a ∗ Corresponding author. E-mail address: fruette@quimica.ivic.ve (F. Ruette). coupled sensor – or “intelligent materials”. Catalysts are also useful in the synthesis of chiral pharmaceutical compounds and in cleaning water, air and soil pollution. A new generation of highly selective catalysts will be obtained from the synthesis of nanoparticles supported on surfaces, encouraging most of the major chemical and petro- chemical corporations to invest significant resources on nanocluster research. Furthermore, applications of cluster chemistry are relevant in different technological disciplines: the recording industry, in metallurgic and high-electronic technology, miniature magnets, quantum-dot lasers, single- electron transistors, thin films, Josephson devices, semi- conductor memory, optical storage, colloids, coating, seals, encapsulants, sensors, electro-optical microelectronics, xe- rogragy, biomedical devices and batteries. The catalytic properties of clusters are somewhat differ- ent from those in extended solids [7–9]. Despite of the great developments of modern quantum-chemistry (QC), many ap- proximations have to be made in the modeling of industrial catalysts in order to obtain results in tractable computational 1381-1169/$ – see front matter © 2004 Published by Elsevier B.V. doi:10.1016/j.molcata.2004.09.062