UK Catalysis Hub


Balancing reductionist and systems approaches in computational catalysis: questions of accuracy and adequacy

Associate Prof Evgeny Pidko

Over the last decade, computational chemistry has become one of the key components of catalysis research and has deserved a place in the catalysis toolbox next to common laboratory techniques such as FTIR, NMR or XRD [1]. The progress in fundamental understanding of catalytic phenomena currently relies largely on quantum chemical computations. State-of-the-art quantum chemical methodologies and, particularly, the density functional theory (DFT) methods have matured to the level that they can be nowadays routinely used not only to rationalize, but also to direct experimental catalysis studies [2,3]. Accuracy is the corner stone of computational chemistry and it represents the key focus of this lecture. In this talk I will illustrate the problem of a balance between the model and method accuracy in computational studies on industrially-relevant catalytic systems [4]. The discussion on the issues related to modeling accuracy will be supported by representative examples from recent research of my group on catalysis by zeolites including selective catalytic oxidation of methane and production of biomass-derived aromatics [4,5]. Along the discussion, I will touch upon possible implications of the selective agreements between reductionism-dominated theories and highly complex catalytic experiments (Figure 1).  I will emphasize the necessity of establishing a balance between the reductionist and systems approaches to studying complex multicomponent reactive systems.

Figure 1. Illustrative examples of the excessively reductionist approaches common for the physical (model A) and chemical (model B) communities to modeling catalytic systems. Despite being partially representative to the actual system (spectral characteristics, general shape and size of the particles, etc), none of them is sufficient for an adequate description of the main functions of the experimental objects.


  1. Sperger, T.; Sanhueza, I. A.; Kalvet, I.; Schoenebeck, F. Chem. Rev. 2015, 115, 9532.
  2. Filonenko, G.A.; Hensen, E.J.M.; Pidko,  E.A. Catal. Sci. Technol. 2014, 4, 3474.
  3. Filonenko, G.A.; Smykowski, D.; Szyja,  B.M.; Hensen, E.J.M.; Pidko  E.A. ACS Catal. 2015, 5, 1145.
  4. Pidko, E.A. ACS Catal. 2017, 7, 4230.
  5. Li, G.; Vassilev, P.; Sanchez-Sanchez, M.; Lercher, J.; Hensen, E.J.M.; Pidko, E.A. J. Catal. 2016, 338, 305; Vogiatzis, K.D..; Li, G.; Hensen, E.J.M.; Gagliardi, L.; Pidko, E.A. J. Phys. Chem. C 2017, DOI: 10.1021/acs.jpcc.7b08714.