CAT-COSMO-CAMPD: Integrated in silico design of catalysts and processes based on quantum chemistry

Catalysts are of paramount importance as most chemical processes would be uneconomical without suitable catalysts. Consequently, the identification of appropriate catalysts is a key step in chemical process design. However, the number of potential catalysts is usually vast. To suggest promising candidates for experimental testing, in silico catalyst design methods are highly desirable. Still, such computational methods are in their infancy. Moreover, simple performance indicators are commonly employed as design objective instead of evaluating the actual process performance enabled by considered catalysts. Here, we present the CAT-COSMO-CAMPD method for integrated in silico design of homogeneous molecular catalysts and processes. CAT-COSMO-CAMPD integrates design of molecular catalysts with process optimization, enabling a process-based evaluation of every designed candidate catalyst. Reaction kinetics of catalytic reactions are predicted by advanced quantum chemical methods. We demonstrate for a catalytic carbamate-cleavage process that CAT-COSMO-CAMPD successfully identifies catalyst molecules maximizing the predicted process performance. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

Catalysts are crucial for chemical processes to be economically feasible, and identifying appropriate catalysts is key in process design. Computational design methods like CAT-COSMO-CAMPD integrate molecular catalyst design with process optimization, enabling evaluation of candidate catalysts based on process performance. Advanced quantum chemical methods are used to predict reaction kinetics, successfully identifying catalyst molecules that maximize predicted process performance.