In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Burello, Enrico; Rothenberg, Gadi

doi:10.3390/i7090375

Cited by 72 publications

(55 citation statements)

References 88 publications

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“…92 Due to the role that molecular structure plays in facilitating a chemical reaction, structure-activity relationships are prevalent in small molecule catalysis and motivate integration of HTE methods with descriptor-based catalyst design from statistical or theoretical computation. 93 Indeed, a hallmark achievement in the catalysis field resulted from a Dow Chemical Corporation computation-guided HTE program that yielded the now industrialized process for synthesizing InFuse TM olefin block copolymers (OBCS). This two-year project produced new theoretically predicted materials (at times more than 1600 individual polymerization reactions were evaluated during a three-week period), optimized production, and rapidly commercialized a new class of polymers now made at scales exceeding 10 8 pounds per year.…”

Section: Catalystsmentioning

confidence: 99%

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Green

Choi

Hattrick‐Simpers

et al. 2017

Applied Physics Reviews

257

173

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The Materials Genome Initiative, a national effort to introduce new materials into the market faster and at lower cost, has made significant progress in computational simulation and modeling of materials. To build on this progress, a large amount of experimental data for validating these models, and informing more sophisticated ones, will be required. High-throughput experimentation generates large volumes of experimental data using combinatorial materials synthesis and rapid measurement techniques, making it an ideal experimental complement to bring the Materials Genome Initiative vision to fruition. This paper reviews the state-of-the-art results, opportunities, and challenges in high-throughput experimentation for materials design. A major conclusion is that an effort to deploy a federated network of high-throughput experimental (synthesis and characterization) tools, which are integrated with a modern materials data infrastructure, is needed.

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Section: Catalystsmentioning

confidence: 99%

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Green

Choi

Hattrick‐Simpers

et al. 2017

Applied Physics Reviews

257

173

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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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confidence: 99%

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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“…However, choosing the right descriptors for modelling a given catalytic system is both crucial and difficult. Previously, we showed that one can select good descriptors by simply calculating a large set of them and then sieving out the less important ones by using, for example, variable importance (VIP) analysis 32,33. Here, we combined two statistical methods, principal component analysis (PCA) and partial least squares (PLS) regression, to build and validate our descriptor model (see Experimental Section for details).…”

Section: Resultsmentioning

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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In Silico Design in Homogeneous Catalysis Using Descriptor Modelling

Cited by 72 publications

References 88 publications

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

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

Finding Furfural Hydrogenation Catalysts via Predictive Modelling

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