Heterogeneous catalyst discovery using 21st century tools: a tutorial

Ras, Erik‐Jan; Rothenberg, Gadi

doi:10.1039/c3ra45852k

Cited by 64 publications

(59 citation statements)

References 48 publications

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“…At the same time, the synthesis and testing of more complex materials can be exceedingly time consuming, which limits the size of the search space. 1 The progress in electronic structure calculations over the last decades has allowed to circumvent these problems. The comparatively efficient treatment of entire classes of materials allows the determination of fundamental material parameters, so called descriptors, which can be correlated to the performance in a given application.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…[20][21][22][23][24] Considering that first-principles calculations are too time-consuming to explore the full spectrum of possibilities, and on the other hand, a great amount of data is being generated and accumulated in the field, ML methods can give a fast and high-precision alternative to the first-principles models. However, ML methods in catalysis [25][26][27][28][29][30][31][32][33][34] are still in their infancy.…”

Section: Introductionmentioning

confidence: 99%

Machine-learning prediction of the d-band center for metals and bimetals

et al. 2016

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The d-band center for metals has been widely used in order to understand activity trends in metal-surface-catalyzed reactions in terms of the linear Brønsted-Evans-Polanyi relation and Hammer-Nørskov d-band model. In this paper, the d-band centers for eleven metals (Fe, Co, Ni, Cu, Ru, Rh, Pd, Ag, Ir, Pt, Au) and their pairwise bimetals for two different structures (1% metal doped-or overlayer-covered metal surfaces) are statistically predicted using machine learning methods from readily available values as descriptors for the target metals (such as the density and the enthalpy of fusion of each metal). The predictive accuracy of four regression methods with different numbers of descriptors and different test-set/training-set ratios are quantitatively evaluated using statistical cross validations. It is shown that the d-band centers are reasonably well predicted by the gradient boosting regression (GBR) method with only six descriptors, even when we predict 75% of the data from only 25% given for training (average 2 root mean square error (RMSE) < 0.5 eV). This demonstrates a potential use of machine learning methods for predicting the activity trends of metal surfaces with a negligible CPU time compared to first-principles methods.

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“…This being said, as catalyst development converges towards a final set of candidates, kinetic models will of course ultimately be developed to aid design and further scale up of the process. More detailed discussions on various modeling methods that can be applied have been published elsewhere [13,14].…”

Section: Modeling Methods and Resultsmentioning

confidence: 99%

Oxidative Coupling of Methane in Small Scale Parallel Reactors

Ras

Gómez‐Quero

2014

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In this work the testing of high temperature reactions in small scale parallel reactors is explored using the oxidative coupling of methane as an example. The advantages of small scale reactors for this very exothermic and complex reaction are discussed. The data generated in this study is explored making use of response surface models derived from statistically designed experiments. Methane conversion has been explored over a Mn-promoted Na 2 WO 4 /SiO 2 catalyst over the temperature range 755-875°C, pressure range 0.2-8.4 barg and GHSV range 4,000-36,000 h -1 using air as oxidant. Methane to oxygen stoichiometries of 4-8 have been explored. The highest C2 yield (16 %) is obtained at at low pressure.

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Heterogeneous catalyst discovery using 21st century tools: a tutorial

Cited by 64 publications

References 48 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

Machine-learning prediction of the d-band center for metals and bimetals

Oxidative Coupling of Methane in Small Scale Parallel Reactors

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