Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards <i>in silico</i> Catalyst Optimisation

Hageman, Jos A.; Westerhuis, Johan A.; Frühauf, Hans‐Werner; Rothenberg, Gadi

doi:10.1002/adsc.200505299

Cited by 57 publications

(59 citation statements)

References 42 publications

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“…In the literature, descriptors based on structural parameters, the stability of the active site, electronic structure related parameters, the volume and the charge of the active site have been suggested. 2,3,25,35,36,[38][39][40] It is a priory not clear which descriptors are necessary to accurately describe the molecular adsorption of CO and NO to transition metal exchanged zeolites. We therefore focus on a series of descriptors.…”

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Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

et al. 2017

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The discovery of new materials tailored for a given application typically requires the screening of a large number of compounds and this process can be significantly accelerated by computational analysis. In such an approach the performance of a compound is correlated to a materials property, a so called descriptor. Here we develop a descriptor-based approach for the adsorption of CO and NO to Cu, Ni, Co and Fe sites in zeolites. We start out by discussing a possible design strategy for zeolite catalysts, define the studied test set of sites in the zeolites SSZ-13 and Mordenite, and define a 1 set of appropriate descriptors. In a subsequent step we use these descriptors in single-, two-and multi-parameter regression analysis and finally use a machine-learning genetic algorithm to reduce the number of variables. We find that one or two descriptors are not sufficient to accurately capture the interactions between molecules and metal centers in zeolites and indeed a multi-parameter approach is necessary. Even though many of the descriptors are directly correlated, we identify the position of the s-orbital and the number of valence electrons of the active site as well as the HOMO-LUMO gap of the adsorbate as most important descriptors. Furthermore the reconstruction of the active sites upon adsorption plays a crucial role and when it is explicitly included in the analysis, correlations improve significantly. In the future we expect that the fundamental methodology developed here will be adapted and transferred to selected problems in adsorption and catalysis and will assist the rational design of materials for the given application.

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Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

et al. 2017

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“…The idea behind building a predictive model is linking the ligand-complexes space (the imidazolinium precursors) with a figure of merit or measured response (e.g., the furfural hydrogenation yield), through an intermediate space of molecular descriptors. A descriptor is a number that encodes structural and/or chemical information 30,31. The ligand backbone, the cone angle and the reaction pocket volume are well known examples.…”

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confidence: 99%

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

et al. 2010

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We combine multicomponent reactions, catalytic performance studies and predictive modelling to find transfer hydrogenation catalysts. An initial set of 18 ruthenium-carbene complexes were synthesized and screened in the transfer hydrogenation of furfural to furfurol with isopropyl alcohol complexes gave varied yields, from 62% up to >99.9%, with no obvious structure/activity correlations. Control experiments proved that the carbene ligand remains coordinated to the ruthenium centre throughout the reaction. Deuterium-labelling studies showed a secondary isotope effect (kH:kD=1.5). Further mechanistic studies showed that this transfer hydrogenation follows the so-called monohydride pathway. Using these data, we built a predictive model for 13 of the catalysts, based on 2D and 3D molecular descriptors. We tested and validated the model using the remaining five catalysts (cross-validation, R2=0.913). Then, with this model, the conversion and selectivity were predicted for four completely new ruthenium-carbene complexes. These four catalysts were then synthesized and tested. The results were within 3% of the model’s predictions, demonstrating the validity and value of predictive modelling in catalyst optimization.

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“…It summarizes the steps analyzed in this review. The first step is the library generation which can be achieved by attaching automatically building blocks to a central scaffold (see the work of Hageman et al [91] for an example of automatic library generation). The library generation can lead to 1D, 2D and 3D structures of catalysts.…”

Section: General Methodology In Data Analysismentioning

confidence: 99%

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Burello

Rothenberg

2006

IJMS

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This review summarises the state-of-the-art methodologies used for designing homogeneous catalysts and optimising reaction conditions (e.g. choosing the right solvent). We focus on computational techniques that can complement the current advances in highthroughput experimentation, covering the literature in the period 1996-2006. The review assesses the use of molecular modelling tools, from descriptor models based on semiempirical and molecular mechanics calculations, to 2D topological descriptors and graph theory methods. Different techniques are compared based on their computational and time cost, output level, problem relevance and viability. We also review the application of various data mining tools, including artificial neural networks, linear regression, and classification trees. The future of homogeneous catalysis discovery and optimisation is discussed in the light of these developments.

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Design and Assembly of Virtual Homogeneous Catalyst Libraries –Towards in silico Catalyst Optimisation

Cited by 57 publications

References 42 publications

Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

Developing a Descriptor-Based Approach for CO and NO Adsorption Strength to Transition Metal Sites in Zeolites

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

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